WorldWideScience

Sample records for supervised learning unsupervised

  1. Integrating the Supervised Information into Unsupervised Learning

    Directory of Open Access Journals (Sweden)

    Ping Ling

    2013-01-01

    Full Text Available This paper presents an assembling unsupervised learning framework that adopts the information coming from the supervised learning process and gives the corresponding implementation algorithm. The algorithm consists of two phases: extracting and clustering data representatives (DRs firstly to obtain labeled training data and then classifying non-DRs based on labeled DRs. The implementation algorithm is called SDSN since it employs the tuning-scaled Support vector domain description to collect DRs, uses spectrum-based method to cluster DRs, and adopts the nearest neighbor classifier to label non-DRs. The validation of the clustering procedure of the first-phase is analyzed theoretically. A new metric is defined data dependently in the second phase to allow the nearest neighbor classifier to work with the informed information. A fast training approach for DRs’ extraction is provided to bring more efficiency. Experimental results on synthetic and real datasets verify that the proposed idea is of correctness and performance and SDSN exhibits higher popularity in practice over the traditional pure clustering procedure.

  2. Comparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification

    Directory of Open Access Journals (Sweden)

    R. Sathya

    2013-02-01

    Full Text Available This paper presents a comparative account of unsupervised and supervised learning models and their pattern classification evaluations as applied to the higher education scenario. Classification plays a vital role in machine based learning algorithms and in the present study, we found that, though the error back-propagation learning algorithm as provided by supervised learning model is very efficient for a number of non-linear real-time problems, KSOM of unsupervised learning model, offers efficient solution and classification in the present study.

  3. Combining Unsupervised and Supervised Learning for Discovering Disease Subclasses

    OpenAIRE

    Tucker, A; Bosoni, P; Bellazzi, R.; Nihtyanova, S; Denton, C.

    2016-01-01

    Diseases are often umbrella terms for many subcategories of disease. The identification of these subcategories is vital if we are to develop personalised treatments that are better focussed on individual patients. In this short paper, we explore the use of a combination of unsupervised learning to identify potential subclasses, and supervised learning to build models for better predicting a number of different health outcomes for patients that suffer from systemic sclerosis, a rare chronic co...

  4. Function approximation using combined unsupervised and supervised learning.

    Science.gov (United States)

    Andras, Peter

    2014-03-01

    Function approximation is one of the core tasks that are solved using neural networks in the context of many engineering problems. However, good approximation results need good sampling of the data space, which usually requires exponentially increasing volume of data as the dimensionality of the data increases. At the same time, often the high-dimensional data is arranged around a much lower dimensional manifold. Here we propose the breaking of the function approximation task for high-dimensional data into two steps: (1) the mapping of the high-dimensional data onto a lower dimensional space corresponding to the manifold on which the data resides and (2) the approximation of the function using the mapped lower dimensional data. We use over-complete self-organizing maps (SOMs) for the mapping through unsupervised learning, and single hidden layer neural networks for the function approximation through supervised learning. We also extend the two-step procedure by considering support vector machines and Bayesian SOMs for the determination of the best parameters for the nonlinear neurons in the hidden layer of the neural networks used for the function approximation. We compare the approximation performance of the proposed neural networks using a set of functions and show that indeed the neural networks using combined unsupervised and supervised learning outperform in most cases the neural networks that learn the function approximation using the original high-dimensional data.

  5. Unsupervised/supervised learning concept for 24-hour load forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Djukanovic, M. (Electrical Engineering Inst. ' Nikola Tesla' , Belgrade (Yugoslavia)); Babic, B. (Electrical Power Industry of Serbia, Belgrade (Yugoslavia)); Sobajic, D.J.; Pao, Y.-H. (Case Western Reserve Univ., Cleveland, OH (United States). Dept. of Electrical Engineering and Computer Science)

    1993-07-01

    An application of artificial neural networks in short-term load forecasting is described. An algorithm using an unsupervised/supervised learning concept and historical relationship between the load and temperature for a given season, day type and hour of the day to forecast hourly electric load with a lead time of 24 hours is proposed. An additional approach using functional link net, temperature variables, average load and last one-hour load of previous day is introduced and compared with the ANN model with one hidden layer load forecast. In spite of limited available weather variables (maximum, minimum and average temperature for the day) quite acceptable results have been achieved. The 24-hour-ahead forecast errors (absolute average) ranged from 2.78% for Saturdays and 3.12% for working days to 3.54% for Sundays. (Author)

  6. A comparative evaluation of supervised and unsupervised representation learning approaches for anaplastic medulloblastoma differentiation

    Science.gov (United States)

    Cruz-Roa, Angel; Arevalo, John; Basavanhally, Ajay; Madabhushi, Anant; González, Fabio

    2015-01-01

    Learning data representations directly from the data itself is an approach that has shown great success in different pattern recognition problems, outperforming state-of-the-art feature extraction schemes for different tasks in computer vision, speech recognition and natural language processing. Representation learning applies unsupervised and supervised machine learning methods to large amounts of data to find building-blocks that better represent the information in it. Digitized histopathology images represents a very good testbed for representation learning since it involves large amounts of high complex, visual data. This paper presents a comparative evaluation of different supervised and unsupervised representation learning architectures to specifically address open questions on what type of learning architectures (deep or shallow), type of learning (unsupervised or supervised) is optimal. In this paper we limit ourselves to addressing these questions in the context of distinguishing between anaplastic and non-anaplastic medulloblastomas from routine haematoxylin and eosin stained images. The unsupervised approaches evaluated were sparse autoencoders and topographic reconstruct independent component analysis, and the supervised approach was convolutional neural networks. Experimental results show that shallow architectures with more neurons are better than deeper architectures without taking into account local space invariances and that topographic constraints provide useful invariant features in scale and rotations for efficient tumor differentiation.

  7. Contributions to unsupervised and supervised learning with applications in digital image processing

    OpenAIRE

    2012-01-01

    311 p. : il. [EN]This Thesis covers a broad period of research activities with a commonthread: learning processes and its application to image processing. The twomain categories of learning algorithms, supervised and unsupervised, have beentouched across these years. The main body of initial works was devoted tounsupervised learning neural architectures, specially the Self Organizing Map.Our aim was to study its convergence properties from empirical and analyticalviewpoints.From the digita...

  8. Contributions to unsupervised and supervised learning with applications in digital image processing

    OpenAIRE

    González Acuña, Ana Isabel

    2014-01-01

    311 p. : il. [EN]This Thesis covers a broad period of research activities with a commonthread: learning processes and its application to image processing. The twomain categories of learning algorithms, supervised and unsupervised, have beentouched across these years. The main body of initial works was devoted tounsupervised learning neural architectures, specially the Self Organizing Map.Our aim was to study its convergence properties from empirical and analyticalviewpoints.From the digita...

  9. Unsupervised Learning and Generalization

    DEFF Research Database (Denmark)

    Hansen, Lars Kai; Larsen, Jan

    1996-01-01

    The concept of generalization is defined for a general class of unsupervised learning machines. The generalization error is a straightforward extension of the corresponding concept for supervised learning, and may be estimated empirically using a test set or by statistical means-in close analogy ...... with supervised learning. The empirical and analytical estimates are compared for principal component analysis and for K-means clustering based density estimation...

  10. Unsupervised Labeling Of Data For Supervised Learning And Its Application To Medical Claims Prediction

    Directory of Open Access Journals (Sweden)

    Che Ngufor

    2013-01-01

    Full Text Available The task identifying changes and irregularities in medical insurance claim pay-ments is a difficult process of which the traditional practice involves queryinghistorical claims databases and flagging potential claims as normal or abnor-mal. Because what is considered as normal payment is usually unknown andmay change over time, abnormal payments often pass undetected; only to bediscovered when the payment period has passed.This paper presents the problem of on-line unsupervised learning from datastreams when the distribution that generates the data changes or drifts overtime. Automated algorithms for detecting drifting concepts in a probabilitydistribution of the data are presented. The idea behind the presented driftdetection methods is to transform the distribution of the data within a slidingwindow into a more convenient distribution. Then, a test statistics p-value ata given significance level can be used to infer the drift rate, adjust the windowsize and decide on the status of the drift. The detected concepts drifts areused to label the data, for subsequent learning of classification models by asupervised learner. The algorithms were tested on several synthetic and realmedical claims data sets.

  11. Unsupervised learning algorithms

    CERN Document Server

    Aydin, Kemal

    2016-01-01

    This book summarizes the state-of-the-art in unsupervised learning. The contributors discuss how with the proliferation of massive amounts of unlabeled data, unsupervised learning algorithms, which can automatically discover interesting and useful patterns in such data, have gained popularity among researchers and practitioners. The authors outline how these algorithms have found numerous applications including pattern recognition, market basket analysis, web mining, social network analysis, information retrieval, recommender systems, market research, intrusion detection, and fraud detection. They present how the difficulty of developing theoretically sound approaches that are amenable to objective evaluation have resulted in the proposal of numerous unsupervised learning algorithms over the past half-century. The intended audience includes researchers and practitioners who are increasingly using unsupervised learning algorithms to analyze their data. Topics of interest include anomaly detection, clustering,...

  12. Supervised and Unsupervised Speaker Adaptation Using Confidence Measure

    Institute of Scientific and Technical Information of China (English)

    LIUJia; LIHusheng; LIUJing; LIURunsheng

    2003-01-01

    The speaker adaptation is an effective means of improving the performance of a speech recognition system,and it can be divided into supervised or unsupervised speaker adaptation.In this paper a kind of confidence measure based on the word lattice structure is proposed and is used for the supervised and unsupervised speaker adaptation.The reliability of the recognition results can be evaluated by the confidence measure,and the uncertain parts in the recognition results can be removed or be given smaller weights in the speaker adaptation process.The experiments show that the confidence can effectively eliminate the suspicious speech and improve the performance of the supervised and unsupervised adaptation considerably.The performance difference between the supervised and unsupervised adaptation is reduced by using the confidence measure based on the word lattice structure.

  13. Concept formation knowledge and experience in unsupervised learning

    CERN Document Server

    Fisher, Douglas H; Langley, Pat

    1991-01-01

    Concept Formation: Knowledge and Experience in Unsupervised Learning presents the interdisciplinary interaction between machine learning and cognitive psychology on unsupervised incremental methods. This book focuses on measures of similarity, strategies for robust incremental learning, and the psychological consistency of various approaches.Organized into three parts encompassing 15 chapters, this book begins with an overview of inductive concept learning in machine learning and psychology, with emphasis on issues that distinguish concept formation from more prevalent supervised methods and f

  14. Clustervision: Visual Supervision of Unsupervised Clustering.

    Science.gov (United States)

    Kwon, Bum Chul; Eysenbach, Ben; Verma, Janu; Ng, Kenney; deFilippi, Christopher; Stewart, Walter F; Perer, Adam

    2017-08-29

    Clustering, the process of grouping together similar items into distinct partitions, is a common type of unsupervised machine learning that can be useful for summarizing and aggregating complex multi-dimensional data. However, data can be clustered in many ways, and there exist a large body of algorithms designed to reveal different patterns. While having access to a wide variety of algorithms is helpful, in practice, it is quite difficult for data scientists to choose and parameterize algorithms to get the clustering results relevant for their dataset and analytical tasks. To alleviate this problem, we built Clustervision, a visual analytics tool that helps ensure data scientists find the right clustering among the large amount of techniques and parameters available. Our system clusters data using a variety of clustering techniques and parameters and then ranks clustering results utilizing five quality metrics. In addition, users can guide the system to produce more relevant results by providing task-relevant constraints on the data. Our visual user interface allows users to find high quality clustering results, explore the clusters using several coordinated visualization techniques, and select the cluster result that best suits their task. We demonstrate this novel approach using a case study with a team of researchers in the medical domain and showcase that our system empowers users to choose an effective representation of their complex data.

  15. Supervised and unsupervised condition monitoring of non-stationary acoustic emission signals

    DEFF Research Database (Denmark)

    Sigurdsson, Sigurdur; Pontoppidan, Niels Henrik; Larsen, Jan

    2005-01-01

    We are pursuing a system that monitors the engine condition under multiple load settings, i.e. under non-stationary operating conditions. The running speed when data acquired under simulated marine conditions (different load settings on the propeller curve) was in the range from approximately 70...... approaches perform well, which indicates that unsupervised models, modelled without faulty data, may be used for accurate condition monitoring....... condition changes across load changes. In this paper we approach this load interpolation problem with supervised and unsupervised learning, i.e. model with normal and fault examples and normal examples only, respectively. We apply non-linear methods for the learning of engine condition changes. Both...

  16. Specialization processes in on-line unsupervised learning

    NARCIS (Netherlands)

    Biehl, M.; Freking, A.; Reents, G.; Schlösser, E.

    1998-01-01

    From the recent analysis of supervised learning by on-line gradient descent in multilayered neural networks it is known that the necessary process of student specialization can be delayed significantly. We demonstrate that this phenomenon also occurs in various models of unsupervised learning. A sol

  17. Penalized unsupervised learning with outliers.

    Science.gov (United States)

    Witten, Daniela M

    2013-01-01

    We consider the problem of performing unsupervised learning in the presence of outliers - that is, observations that do not come from the same distribution as the rest of the data. It is known that in this setting, standard approaches for unsupervised learning can yield unsatisfactory results. For instance, in the presence of severe outliers, K-means clustering will often assign each outlier to its own cluster, or alternatively may yield distorted clusters in order to accommodate the outliers. In this paper, we take a new approach to extending existing unsupervised learning techniques to accommodate outliers. Our approach is an extension of a recent proposal for outlier detection in the regression setting. We allow each observation to take on an "error" term, and we penalize the errors using a group lasso penalty in order to encourage most of the observations' errors to exactly equal zero. We show that this approach can be used in order to develop extensions of K-means clustering and principal components analysis that result in accurate outlier detection, as well as improved performance in the presence of outliers. These methods are illustrated in a simulation study and on two gene expression data sets, and connections with M-estimation are explored.

  18. Decomposition methods for unsupervised learning

    DEFF Research Database (Denmark)

    Mørup, Morten

    2008-01-01

    This thesis presents the application and development of decomposition methods for Unsupervised Learning. It covers topics from classical factor analysis based decomposition and its variants such as Independent Component Analysis, Non-negative Matrix Factorization and Sparse Coding to their genera......This thesis presents the application and development of decomposition methods for Unsupervised Learning. It covers topics from classical factor analysis based decomposition and its variants such as Independent Component Analysis, Non-negative Matrix Factorization and Sparse Coding...... methods and clustering problems is derived both in terms of classical point clustering but also in terms of community detection in complex networks. A guiding principle throughout this thesis is the principle of parsimony. Hence, the goal of Unsupervised Learning is here posed as striving for simplicity...... in the decompositions. Thus, it is demonstrated how a wide range of decomposition methods explicitly or implicitly strive to attain this goal. Applications of the derived decompositions are given ranging from multi-media analysis of image and sound data, analysis of biomedical data such as electroencephalography...

  19. Supervised and unsupervised classification - The case of IRAS point sources

    Science.gov (United States)

    Adorf, Hans-Martin; Meurs, E. J. A.

    Progress is reported on a project which aims at mapping the extragalactic sky in order to derive the large scale distribution of luminous matter. The approach consists in selecting from the IRAS Point Source Catalog a set of galaxies which is as clean and as complete as possible. The decision and discrimination problems involved lend themselves to a treatment using methods from multivariate statistics, in particular statistical pattern recognition. Two different approaches, one based on supervised Bayesian classification, the other on unsupervised data-driven classification, are presented and some preliminary results are reported.

  20. Unsupervised Learning of Action Primitives

    DEFF Research Database (Denmark)

    Baby, Sanmohan; Krüger, Volker; Kragic, Danica

    2010-01-01

    and scale, the use of the object can provide a strong invariant for the detection of motion primitives. In this paper we propose an unsupervised learning approach for action primitives that makes use of the human movements as well as the object state changes. We group actions according to the changes......Action representation is a key issue in imitation learning for humanoids. With the recent finding of mirror neurons there has been a growing interest in expressing actions as a combination meaningful subparts called primitives. Primitives could be thought of as an alphabet for the human actions....... In this paper we observe that human actions and objects can be seen as being intertwined: we can interpret actions from the way the body parts are moving, but as well from how their effect on the involved object. While human movements can look vastly different even under minor changes in location, orientation...

  1. Unsupervised learning of binary vectors

    Science.gov (United States)

    Copelli Lopes da Silva, Mauro

    In this thesis, unsupervised learning of binary vectors from data is studied using methods from Statistical Mechanics of disordered systems. In the model, data vectors are distributed according to a single symmetry-breaking direction. The aim of unsupervised learning is to provide a good approximation to this direction. The difference with respect to previous studies is the knowledge that this preferential direction has binary components. It is shown that sampling from the posterior distribution (Gibbs learning) leads, for general smooth distributions, to an exponentially fast approach to perfect learning in the asymptotic limit of large number of examples. If the distribution is non-smooth, then first order phase transitions to perfect learning are expected. In the limit of poor performance, a second order phase transition ("retarded learning") is predicted to occur if the data distribution is not biased. Using concepts from Bayesian inference, the center of mass of the Gibbs ensemble is shown to have maximal average (Bayes-optimal) performance. This upper bound for continuous vectors is extended to a discrete space, resulting in the clipped center of mass of the Gibbs ensemble having maximal average performance among the binary vectors. To calculate the performance of this best binary vector, the geometric properties of the center of mass of binary vectors are studied. The surprising result is found that the center of mass of infinite binary vectors which obey some simple constraints, is again a binary vector. When disorder is taken into account in the calculation, however, a vector with continuous components is obtained. The performance of the best binary vector is calculated and shown to always lie above that of Gibbs learning and below the Bayes-optimal performance. Making use of a variational approach under the replica symmetric ansatz, an optimal potential is constructed in the limits of zero temperature and mutual overlap 1. Minimization of this potential

  2. Spoken Document Retrieval Leveraging Unsupervised and Supervised Topic Modeling Techniques

    Science.gov (United States)

    Chen, Kuan-Yu; Wang, Hsin-Min; Chen, Berlin

    This paper describes the application of two attractive categories of topic modeling techniques to the problem of spoken document retrieval (SDR), viz. document topic model (DTM) and word topic model (WTM). Apart from using the conventional unsupervised training strategy, we explore a supervised training strategy for estimating these topic models, imagining a scenario that user query logs along with click-through information of relevant documents can be utilized to build an SDR system. This attempt has the potential to associate relevant documents with queries even if they do not share any of the query words, thereby improving on retrieval quality over the baseline system. Likewise, we also study a novel use of pseudo-supervised training to associate relevant documents with queries through a pseudo-feedback procedure. Moreover, in order to lessen SDR performance degradation caused by imperfect speech recognition, we investigate leveraging different levels of index features for topic modeling, including words, syllable-level units, and their combination. We provide a series of experiments conducted on the TDT (TDT-2 and TDT-3) Chinese SDR collections. The empirical results show that the methods deduced from our proposed modeling framework are very effective when compared with a few existing retrieval approaches.

  3. Supervised and Unsupervised Classification for Pattern Recognition Purposes

    Directory of Open Access Journals (Sweden)

    Catalina COCIANU

    2006-01-01

    Full Text Available A cluster analysis task has to identify the grouping trends of data, to decide on the sound clusters as well as to validate somehow the resulted structure. The identification of the grouping tendency existing in a data collection assumes the selection of a framework stated in terms of a mathematical model allowing to express the similarity degree between couples of particular objects, quasi-metrics expressing the similarity between an object an a cluster and between clusters, respectively. In supervised classification, we are provided with a collection of preclassified patterns, and the problem is to label a newly encountered pattern. Typically, the given training patterns are used to learn the descriptions of classes which in turn are used to label a new pattern. The final section of the paper presents a new methodology for supervised learning based on PCA. The classes are represented in the measurement/feature space by a continuous repartitions

  4. Neural-network front ends in unsupervised learning.

    Science.gov (United States)

    Pedrycz, W; Waletzky, J

    1997-01-01

    Proposed is an idea of partial supervision realized in the form of a neural-network front end to the schemes of unsupervised learning (clustering). This neural network leads to an anisotropic nature of the induced feature space. The anisotropic property of the space provides us with some of its local deformation necessary to properly represent labeled data and enhance efficiency of the mechanisms of clustering to be exploited afterwards. The training of the network is completed based upon available labeled patterns-a referential form of the labeling gives rise to reinforcement learning. It is shown that the discussed approach is universal and can be utilized in conjunction with any clustering method. Experimental studies are concentrated on three main categories of unsupervised learning including FUZZY ISODATA, Kohonen self-organizing maps, and hierarchical clustering.

  5. Unsupervised Learning (Clustering) of Odontocete Echolocation Clicks

    Science.gov (United States)

    2015-09-30

    1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Unsupervised Learning ( Clustering ) of Odontocete...develop methods for clustering of marine mammal echolocation clicks to learn about species assemblages where little or no prior knowledge exists about...California Bight where many of the species can be acoustically identified, enabling the development of clustering algorithms whose performance can be

  6. ANOMALY INTRUSION DETECTION DESIGN USING HYBRID OF UNSUPERVISED AND SUPERVISED NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    M. Bahrololum

    2009-07-01

    Full Text Available This paper proposed a new approach to design the system using a hybrid of misuse and anomalydetection for training of normal and attack packets respectively. The utilized method for attack training isthe combination of unsupervised and supervised Neural Network (NN for Intrusion Detection System. Bythe unsupervised NN based on Self Organizing Map (SOM, attacks will be classified into smallercategories considering their similar features, and then unsupervised NN based on Backpropagation willbe used for clustering. By misuse approach known packets would be identified fast and unknown attackswill be able to detect by this method.

  7. Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks.

    Science.gov (United States)

    Dosovitskiy, Alexey; Fischer, Philipp; Springenberg, Jost Tobias; Riedmiller, Martin; Brox, Thomas

    2016-09-01

    Deep convolutional networks have proven to be very successful in learning task specific features that allow for unprecedented performance on various computer vision tasks. Training of such networks follows mostly the supervised learning paradigm, where sufficiently many input-output pairs are required for training. Acquisition of large training sets is one of the key challenges, when approaching a new task. In this paper, we aim for generic feature learning and present an approach for training a convolutional network using only unlabeled data. To this end, we train the network to discriminate between a set of surrogate classes. Each surrogate class is formed by applying a variety of transformations to a randomly sampled 'seed' image patch. In contrast to supervised network training, the resulting feature representation is not class specific. It rather provides robustness to the transformations that have been applied during training. This generic feature representation allows for classification results that outperform the state of the art for unsupervised learning on several popular datasets (STL-10, CIFAR-10, Caltech-101, Caltech-256). While features learned with our approach cannot compete with class specific features from supervised training on a classification task, we show that they are advantageous on geometric matching problems, where they also outperform the SIFT descriptor.

  8. Space coding for sensorimotor transformations can emerge through unsupervised learning.

    Science.gov (United States)

    De Filippo De Grazia, Michele; Cutini, Simone; Lisi, Matteo; Zorzi, Marco

    2012-08-01

    The posterior parietal cortex (PPC) is fundamental for sensorimotor transformations because it combines multiple sensory inputs and posture signals into different spatial reference frames that drive motor programming. Here, we present a computational model mimicking the sensorimotor transformations occurring in the PPC. A recurrent neural network with one layer of hidden neurons (restricted Boltzmann machine) learned a stochastic generative model of the sensory data without supervision. After the unsupervised learning phase, the activity of the hidden neurons was used to compute a motor program (a population code on a bidimensional map) through a simple linear projection and delta rule learning. The average motor error, calculated as the difference between the expected and the computed output, was less than 3°. Importantly, analyses of the hidden neurons revealed gain-modulated visual receptive fields, thereby showing that space coding for sensorimotor transformations similar to that observed in the PPC can emerge through unsupervised learning. These results suggest that gain modulation is an efficient coding strategy to integrate visual and postural information toward the generation of motor commands.

  9. Unsupervised Learning of Action Primitives

    DEFF Research Database (Denmark)

    Baby, Sanmohan; Krüger, Volker; Kragic, Danica

    2010-01-01

    Action representation is a key issue in imitation learning for humanoids. With the recent finding of mirror neurons there has been a growing interest in expressing actions as a combination meaningful subparts called primitives. Primitives could be thought of as an alphabet for the human actions. ...

  10. "Memory foam" approach to unsupervised learning

    CERN Document Server

    Janson, Natalia B

    2011-01-01

    We propose an alternative approach to construct an artificial learning system, which naturally learns in an unsupervised manner. Its mathematical prototype is a dynamical system, which automatically shapes its vector field in response to the input signal. The vector field converges to a gradient of a multi-dimensional probability density distribution of the input process, taken with negative sign. The most probable patterns are represented by the stable fixed points, whose basins of attraction are formed automatically. The performance of this system is illustrated with musical signals.

  11. The Supervised Learning Gaussian Mixture Model

    Institute of Scientific and Technical Information of China (English)

    马继涌; 高文

    1998-01-01

    The traditional Gaussian Mixture Model(GMM)for pattern recognition is an unsupervised learning method.The parameters in the model are derived only by the training samples in one class without taking into account the effect of sample distributions of other classes,hence,its recognition accuracy is not ideal sometimes.This paper introduces an approach for estimating the parameters in GMM in a supervising way.The Supervised Learning Gaussian Mixture Model(SLGMM)improves the recognition accuracy of the GMM.An experimental example has shown its effectiveness.The experimental results have shown that the recognition accuracy derived by the approach is higher than those obtained by the Vector Quantization(VQ)approach,the Radial Basis Function (RBF) network model,the Learning Vector Quantization (LVQ) approach and the GMM.In addition,the training time of the approach is less than that of Multilayer Perceptrom(MLP).

  12. CHISSL: A Human-Machine Collaboration Space for Unsupervised Learning

    Energy Technology Data Exchange (ETDEWEB)

    Arendt, Dustin L.; Komurlu, Caner; Blaha, Leslie M.

    2017-07-14

    We developed CHISSL, a human-machine interface that utilizes supervised machine learning in an unsupervised context to help the user group unlabeled instances by her own mental model. The user primarily interacts via correction (moving a misplaced instance into its correct group) or confirmation (accepting that an instance is placed in its correct group). Concurrent with the user's interactions, CHISSL trains a classification model guided by the user's grouping of the data. It then predicts the group of unlabeled instances and arranges some of these alongside the instances manually organized by the user. We hypothesize that this mode of human and machine collaboration is more effective than Active Learning, wherein the machine decides for itself which instances should be labeled by the user. We found supporting evidence for this hypothesis in a pilot study where we applied CHISSL to organize a collection of handwritten digits.

  13. Inductive Supervised Quantum Learning

    Science.gov (United States)

    Monràs, Alex; Sentís, Gael; Wittek, Peter

    2017-05-01

    In supervised learning, an inductive learning algorithm extracts general rules from observed training instances, then the rules are applied to test instances. We show that this splitting of training and application arises naturally, in the classical setting, from a simple independence requirement with a physical interpretation of being nonsignaling. Thus, two seemingly different definitions of inductive learning happen to coincide. This follows from the properties of classical information that break down in the quantum setup. We prove a quantum de Finetti theorem for quantum channels, which shows that in the quantum case, the equivalence holds in the asymptotic setting, that is, for large numbers of test instances. This reveals a natural analogy between classical learning protocols and their quantum counterparts, justifying a similar treatment, and allowing us to inquire about standard elements in computational learning theory, such as structural risk minimization and sample complexity.

  14. Weakly supervised visual dictionary learning by harnessing image attributes.

    Science.gov (United States)

    Gao, Yue; Ji, Rongrong; Liu, Wei; Dai, Qionghai; Hua, Gang

    2014-12-01

    Bag-of-features (BoFs) representation has been extensively applied to deal with various computer vision applications. To extract discriminative and descriptive BoF, one important step is to learn a good dictionary to minimize the quantization loss between local features and codewords. While most existing visual dictionary learning approaches are engaged with unsupervised feature quantization, the latest trend has turned to supervised learning by harnessing the semantic labels of images or regions. However, such labels are typically too expensive to acquire, which restricts the scalability of supervised dictionary learning approaches. In this paper, we propose to leverage image attributes to weakly supervise the dictionary learning procedure without requiring any actual labels. As a key contribution, our approach establishes a generative hidden Markov random field (HMRF), which models the quantized codewords as the observed states and the image attributes as the hidden states, respectively. Dictionary learning is then performed by supervised grouping the observed states, where the supervised information is stemmed from the hidden states of the HMRF. In such a way, the proposed dictionary learning approach incorporates the image attributes to learn a semantic-preserving BoF representation without any genuine supervision. Experiments in large-scale image retrieval and classification tasks corroborate that our approach significantly outperforms the state-of-the-art unsupervised dictionary learning approaches.

  15. Semi-Supervised and Unsupervised Novelty Detection using Nested Support Vector Machines

    OpenAIRE

    de Morsier, Frank; Borgeaud, Maurice; Gass, Volker; Küchler, Christoph; Thiran, Jean-Philippe

    2012-01-01

    Very often in change detection only few labels or even none are available. In order to perform change detection in these extreme scenarios, they can be considered as novelty detection problems, semi-supervised (SSND) if some labels are available otherwise unsupervised (UND). SSND can be seen as an unbalanced classification between labeled and unlabeled samples using the Cost-Sensitive Support Vector Machine (CS-SVM). UND assumes novelties in low density regions and can be approached using th...

  16. Unsupervised feature learning from finite data by message passing: Discontinuous versus continuous phase transition

    Science.gov (United States)

    Huang, Haiping; Toyoizumi, Taro

    2016-12-01

    Unsupervised neural network learning extracts hidden features from unlabeled training data. This is used as a pretraining step for further supervised learning in deep networks. Hence, understanding unsupervised learning is of fundamental importance. Here, we study the unsupervised learning from a finite number of data, based on the restricted Boltzmann machine where only one hidden neuron is considered. Our study inspires an efficient message-passing algorithm to infer the hidden feature and estimate the entropy of candidate features consistent with the data. Our analysis reveals that the learning requires only a few data if the feature is salient and extensively many if the feature is weak. Moreover, the entropy of candidate features monotonically decreases with data size and becomes negative (i.e., entropy crisis) before the message passing becomes unstable, suggesting a discontinuous phase transition. In terms of convergence time of the message-passing algorithm, the unsupervised learning exhibits an easy-hard-easy phenomenon as the training data size increases. All these properties are reproduced in an approximate Hopfield model, with an exception that the entropy crisis is absent, and only continuous phase transition is observed. This key difference is also confirmed in a handwritten digits dataset. This study deepens our understanding of unsupervised learning from a finite number of data and may provide insights into its role in training deep networks.

  17. Deep supervised, but not unsupervised, models may explain IT cortical representation.

    Directory of Open Access Journals (Sweden)

    Seyed-Mahdi Khaligh-Razavi

    2014-11-01

    Full Text Available Inferior temporal (IT cortex in human and nonhuman primates serves visual object recognition. Computational object-vision models, although continually improving, do not yet reach human performance. It is unclear to what extent the internal representations of computational models can explain the IT representation. Here we investigate a wide range of computational model representations (37 in total, testing their categorization performance and their ability to account for the IT representational geometry. The models include well-known neuroscientific object-recognition models (e.g. HMAX, VisNet along with several models from computer vision (e.g. SIFT, GIST, self-similarity features, and a deep convolutional neural network. We compared the representational dissimilarity matrices (RDMs of the model representations with the RDMs obtained from human IT (measured with fMRI and monkey IT (measured with cell recording for the same set of stimuli (not used in training the models. Better performing models were more similar to IT in that they showed greater clustering of representational patterns by category. In addition, better performing models also more strongly resembled IT in terms of their within-category representational dissimilarities. Representational geometries were significantly correlated between IT and many of the models. However, the categorical clustering observed in IT was largely unexplained by the unsupervised models. The deep convolutional network, which was trained by supervision with over a million category-labeled images, reached the highest categorization performance and also best explained IT, although it did not fully explain the IT data. Combining the features of this model with appropriate weights and adding linear combinations that maximize the margin between animate and inanimate objects and between faces and other objects yielded a representation that fully explained our IT data. Overall, our results suggest that explaining

  18. Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation

    Science.gov (United States)

    Khaligh-Razavi, Seyed-Mahdi; Kriegeskorte, Nikolaus

    2014-01-01

    Inferior temporal (IT) cortex in human and nonhuman primates serves visual object recognition. Computational object-vision models, although continually improving, do not yet reach human performance. It is unclear to what extent the internal representations of computational models can explain the IT representation. Here we investigate a wide range of computational model representations (37 in total), testing their categorization performance and their ability to account for the IT representational geometry. The models include well-known neuroscientific object-recognition models (e.g. HMAX, VisNet) along with several models from computer vision (e.g. SIFT, GIST, self-similarity features, and a deep convolutional neural network). We compared the representational dissimilarity matrices (RDMs) of the model representations with the RDMs obtained from human IT (measured with fMRI) and monkey IT (measured with cell recording) for the same set of stimuli (not used in training the models). Better performing models were more similar to IT in that they showed greater clustering of representational patterns by category. In addition, better performing models also more strongly resembled IT in terms of their within-category representational dissimilarities. Representational geometries were significantly correlated between IT and many of the models. However, the categorical clustering observed in IT was largely unexplained by the unsupervised models. The deep convolutional network, which was trained by supervision with over a million category-labeled images, reached the highest categorization performance and also best explained IT, although it did not fully explain the IT data. Combining the features of this model with appropriate weights and adding linear combinations that maximize the margin between animate and inanimate objects and between faces and other objects yielded a representation that fully explained our IT data. Overall, our results suggest that explaining IT requires

  19. Speech emotion recognition with unsupervised feature learning

    Institute of Scientific and Technical Information of China (English)

    Zheng-wei HUANG; Wen-tao XUE; Qi-rong MAO

    2015-01-01

    Emotion-based features are critical for achieving high performance in a speech emotion recognition (SER) system. In general, it is difficult to develop these features due to the ambiguity of the ground-truth. In this paper, we apply several unsupervised feature learning algorithms (including K-means clustering, the sparse auto-encoder, and sparse restricted Boltzmann machines), which have promise for learning task-related features by using unlabeled data, to speech emotion recognition. We then evaluate the performance of the proposed approach and present a detailed analysis of the effect of two important factors in the model setup, the content window size and the number of hidden layer nodes. Experimental results show that larger content windows and more hidden nodes contribute to higher performance. We also show that the two-layer network cannot explicitly improve performance compared to a single-layer network.

  20. Sleep Stage Classification Using Unsupervised Feature Learning

    Directory of Open Access Journals (Sweden)

    Martin Längkvist

    2012-01-01

    Full Text Available Most attempts at training computers for the difficult and time-consuming task of sleep stage classification involve a feature extraction step. Due to the complexity of multimodal sleep data, the size of the feature space can grow to the extent that it is also necessary to include a feature selection step. In this paper, we propose the use of an unsupervised feature learning architecture called deep belief nets (DBNs and show how to apply it to sleep data in order to eliminate the use of handmade features. Using a postprocessing step of hidden Markov model (HMM to accurately capture sleep stage switching, we compare our results to a feature-based approach. A study of anomaly detection with the application to home environment data collection is also presented. The results using raw data with a deep architecture, such as the DBN, were comparable to a feature-based approach when validated on clinical datasets.

  1. Learning Dynamics in Doctoral Supervision

    DEFF Research Database (Denmark)

    Kobayashi, Sofie

    This doctoral research explores doctoral supervision within life science research in a Danish university. From one angle it investigates doctoral students’ experiences with strengthening the relationship with their supervisors through a structured meeting with the supervisor, prepared as part...... investigates learning opportunities in supervision with multiple supervisors. This was investigated through observations and recording of supervision, and subsequent analysis of transcripts. The analyses used different perspectives on learning; learning as participation, positioning theory and variation theory....... The research illuminates how learning opportunities are created in the interaction through the scientific discussions. It also shows how multiple supervisors can contribute to supervision by providing new perspectives and opinions that have a potential for creating new understandings. The combination...

  2. Genetic classification of populations using supervised learning.

    LENUS (Irish Health Repository)

    Bridges, Michael

    2011-01-01

    There are many instances in genetics in which we wish to determine whether two candidate populations are distinguishable on the basis of their genetic structure. Examples include populations which are geographically separated, case-control studies and quality control (when participants in a study have been genotyped at different laboratories). This latter application is of particular importance in the era of large scale genome wide association studies, when collections of individuals genotyped at different locations are being merged to provide increased power. The traditional method for detecting structure within a population is some form of exploratory technique such as principal components analysis. Such methods, which do not utilise our prior knowledge of the membership of the candidate populations. are termed unsupervised. Supervised methods, on the other hand are able to utilise this prior knowledge when it is available.In this paper we demonstrate that in such cases modern supervised approaches are a more appropriate tool for detecting genetic differences between populations. We apply two such methods, (neural networks and support vector machines) to the classification of three populations (two from Scotland and one from Bulgaria). The sensitivity exhibited by both these methods is considerably higher than that attained by principal components analysis and in fact comfortably exceeds a recently conjectured theoretical limit on the sensitivity of unsupervised methods. In particular, our methods can distinguish between the two Scottish populations, where principal components analysis cannot. We suggest, on the basis of our results that a supervised learning approach should be the method of choice when classifying individuals into pre-defined populations, particularly in quality control for large scale genome wide association studies.

  3. Performance of some supervised and unsupervised multivariate techniques for grouping authentic and unauthentic Viagra and Cialis

    Directory of Open Access Journals (Sweden)

    Michel J. Anzanello

    2014-09-01

    Full Text Available A typical application of multivariate techniques in forensic analysis consists of discriminating between authentic and unauthentic samples of seized drugs, in addition to finding similar properties in the unauthentic samples. In this paper, the performance of several methods belonging to two different classes of multivariate techniques–supervised and unsupervised techniques–were compared. The supervised techniques (ST are the k-Nearest Neighbor (KNN, Support Vector Machine (SVM, Probabilistic Neural Networks (PNN and Linear Discriminant Analysis (LDA; the unsupervised techniques are the k-Means CA and the Fuzzy C-Means (FCM. The methods are applied to Infrared Spectroscopy by Fourier Transform (FTIR from authentic and unauthentic Cialis and Viagra. The FTIR data are also transformed by Principal Components Analysis (PCA and kernel functions aimed at improving the grouping performance. ST proved to be a more reasonable choice when the analysis is conducted on the original data, while the UT led to better results when applied to transformed data.

  4. Whole body vibration: unsupervised training or combined with a supervised multi-purpose exercise for fitness?

    Science.gov (United States)

    Emerenziani, Gian Pietro; Meucci, Marco; Gallotta, Maria Chiara; Buzzachera, Cosme Franklim; Guidetti, Laura; Baldari, Carlo

    2014-01-01

    The aim of the study was to compare the effect of an unsupervised whole body vibration (WBV) training and two different supervised multi-purpose exercise programmes, with and without WBV, on body composition, functional fitness and self-reported well-being in middle-aged adults. Fifty-four healthy participants (age 48.6 ± 6.7 years) were randomly assigned to a vibration group (VG), a multi-purpose exercise group (MG) and a multi-purpose exercise with vibration group (VMG) and trained 3 days a week for 4 months. VG performed a standardised unsupervised WBV protocol, MG a supervised multi-purpose exercise and VMG a multi-purpose exercise including vibration. After training, drop out was significantly higher in VG group (P = 0.016) when compared to VMG group. In both MG and VMG, body composition, sit-up, push-up, sit and reach, agility test, hopping test and self-reported general health significantly improved (P fitness and the best results in adherence could be achieved integrating WBV practice into a multi-purpose exercise training.

  5. Estimating extinction using unsupervised machine learning

    Science.gov (United States)

    Meingast, Stefan; Lombardi, Marco; Alves, João

    2017-05-01

    Dust extinction is the most robust tracer of the gas distribution in the interstellar medium, but measuring extinction is limited by the systematic uncertainties involved in estimating the intrinsic colors to background stars. In this paper we present a new technique, Pnicer, that estimates intrinsic colors and extinction for individual stars using unsupervised machine learning algorithms. This new method aims to be free from any priors with respect to the column density and intrinsic color distribution. It is applicable to any combination of parameters and works in arbitrary numbers of dimensions. Furthermore, it is not restricted to color space. Extinction toward single sources is determined by fitting Gaussian mixture models along the extinction vector to (extinction-free) control field observations. In this way it becomes possible to describe the extinction for observed sources with probability densities, rather than a single value. Pnicer effectively eliminates known biases found in similar methods and outperforms them in cases of deep observational data where the number of background galaxies is significant, or when a large number of parameters is used to break degeneracies in the intrinsic color distributions. This new method remains computationally competitive, making it possible to correctly de-redden millions of sources within a matter of seconds. With the ever-increasing number of large-scale high-sensitivity imaging surveys, Pnicer offers a fast and reliable way to efficiently calculate extinction for arbitrary parameter combinations without prior information on source characteristics. The Pnicer software package also offers access to the well-established Nicer technique in a simple unified interface and is capable of building extinction maps including the Nicest correction for cloud substructure. Pnicer is offered to the community as an open-source software solution and is entirely written in Python.

  6. Unsupervised process monitoring and fault diagnosis with machine learning methods

    CERN Document Server

    Aldrich, Chris

    2013-01-01

    This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data

  7. QUEST: Eliminating Online Supervised Learning for Efficient Classification Algorithms

    Directory of Open Access Journals (Sweden)

    Ardjan Zwartjes

    2016-10-01

    Full Text Available In this work, we introduce QUEST (QUantile Estimation after Supervised Training, an adaptive classification algorithm for Wireless Sensor Networks (WSNs that eliminates the necessity for online supervised learning. Online processing is important for many sensor network applications. Transmitting raw sensor data puts high demands on the battery, reducing network life time. By merely transmitting partial results or classifications based on the sampled data, the amount of traffic on the network can be significantly reduced. Such classifications can be made by learning based algorithms using sampled data. An important issue, however, is the training phase of these learning based algorithms. Training a deployed sensor network requires a lot of communication and an impractical amount of human involvement. QUEST is a hybrid algorithm that combines supervised learning in a controlled environment with unsupervised learning on the location of deployment. Using the SITEX02 dataset, we demonstrate that the presented solution works with a performance penalty of less than 10% in 90% of the tests. Under some circumstances, it even outperforms a network of classifiers completely trained with supervised learning. As a result, the need for on-site supervised learning and communication for training is completely eliminated by our solution.

  8. Supervision Learning as Conceptual Threshold Crossing: When Supervision Gets "Medieval"

    Science.gov (United States)

    Carter, Susan

    2016-01-01

    This article presumes that supervision is a category of teaching, and that we all "learn" how to teach better. So it enquires into what novice supervisors need to learn. An anonymised digital questionnaire sought data from supervisors [n226] on their experiences of supervision to find out what was difficult, and supervisor interviews…

  9. Supervision Learning as Conceptual Threshold Crossing: When Supervision Gets "Medieval"

    Science.gov (United States)

    Carter, Susan

    2016-01-01

    This article presumes that supervision is a category of teaching, and that we all "learn" how to teach better. So it enquires into what novice supervisors need to learn. An anonymised digital questionnaire sought data from supervisors [n226] on their experiences of supervision to find out what was difficult, and supervisor interviews…

  10. A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG) Pattern Recognition

    Science.gov (United States)

    Huang, Qi; Yang, Dapeng; Jiang, Li; Zhang, Huajie; Liu, Hong; Kotani, Kiyoshi

    2017-01-01

    Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC), by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC). We compared PAC performance with incremental support vector classifier (ISVC) and non-adapting SVC (NSVC) in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% ± 2.23%, p < 0.05) and ISVC (13.38% ± 2.62%, p = 0.001), and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle). PMID:28608824

  11. A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG Pattern Recognition

    Directory of Open Access Journals (Sweden)

    Qi Huang

    2017-06-01

    Full Text Available Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC, by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC. We compared PAC performance with incremental support vector classifier (ISVC and non-adapting SVC (NSVC in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% ± 2.23%, p < 0.05 and ISVC (13.38% ± 2.62%, p = 0.001, and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle.

  12. Plaque removal efficacy of powered and manual toothbrushes under supervised and unsupervised conditions: A comparative clinical study

    Directory of Open Access Journals (Sweden)

    S Kallar

    2011-01-01

    Full Text Available The present study was undertaken to determine and compare the efficacy of manual and powered toothbrushes under supervised and unsupervised conditions in 200 school-going children between 6 and 13 years of age. Two hundred school-going children aged between 6 and 13 years were selected. Children were randomly divided into two groups of 100 in each. Group 1 children were given manual brushes, while group 2 children were given powered brushes. The groups were further divided into two subgroups, with supervised brushing in subgroup A and unsupervised brushing in subgroup B. At 3, 6, 9 and 12 weeks, plaque was recorded according to Turseky-Gilmore-Glickman modification of Quingley Hein index and oral hygiene performance index. Data were statistically analyzed. Both brushes significantly reduced the plaque accumulation, though to different degrees. Powered brushes showed significant plaque reduction as compared to the manual brushes. Supervised group of both brushes showed a greater plaque reduction.

  13. The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data.

    Science.gov (United States)

    Chimienti, Marianna; Cornulier, Thomas; Owen, Ellie; Bolton, Mark; Davies, Ian M; Travis, Justin M J; Scott, Beth E

    2016-02-01

    The recent increase in data accuracy from high resolution accelerometers offers substantial potential for improved understanding and prediction of animal movements. However, current approaches used for analysing these multivariable datasets typically require existing knowledge of the behaviors of the animals to inform the behavioral classification process. These methods are thus not well-suited for the many cases where limited knowledge of the different behaviors performed exist. Here, we introduce the use of an unsupervised learning algorithm. To illustrate the method's capability we analyse data collected using a combination of GPS and Accelerometers on two seabird species: razorbills (Alca torda) and common guillemots (Uria aalge). We applied the unsupervised learning algorithm Expectation Maximization to characterize latent behavioral states both above and below water at both individual and group level. The application of this flexible approach yielded significant new insights into the foraging strategies of the two study species, both above and below the surface of the water. In addition to general behavioral modes such as flying, floating, as well as descending and ascending phases within the water column, this approach allowed an exploration of previously unstudied and important behaviors such as searching and prey chasing/capture events. We propose that this unsupervised learning approach provides an ideal tool for the systematic analysis of such complex multivariable movement data that are increasingly being obtained with accelerometer tags across species. In particular, we recommend its application in cases where we have limited current knowledge of the behaviors performed and existing supervised learning approaches may have limited utility.

  14. Supervised Dictionary Learning

    CERN Document Server

    Mairal, Julien; Ponce, Jean; Sapiro, Guillermo; Zisserman, Andrew

    2008-01-01

    It is now well established that sparse signal models are well suited to restoration tasks and can effectively be learned from audio, image, and video data. Recent research has been aimed at learning discriminative sparse models instead of purely reconstructive ones. This paper proposes a new step in that direction, with a novel sparse representation for signals belonging to different classes in terms of a shared dictionary and multiple class-decision functions. The linear variant of the proposed model admits a simple probabilistic interpretation, while its most general variant admits an interpretation in terms of kernels. An optimization framework for learning all the components of the proposed model is presented, along with experimental results on standard handwritten digit and texture classification tasks.

  15. Supervised Dictionary Learning

    Science.gov (United States)

    2008-11-01

    recently led to state-of-the-art results for numerous low-level image processing tasks such as denoising [2], show- ing that sparse models are well... denoising via sparse and redundant representations over learned dictio- naries. IEEE Trans. IP, 54(12), 2006. [3] K. Huang and S. Aviyente. Sparse...2006. [19] M. Aharon, M. Elad, and A. M. Bruckstein. The K- SVD : An algorithm for designing of overcomplete dictionaries for sparse representations

  16. Building high-level features using large scale unsupervised learning

    CERN Document Server

    Le, Quoc V; Devin, Matthieu; Corrado, Greg; Chen, Kai; Ranzato, Marc'Aurelio; Dean, Jeff; Ng, Andrew Y

    2011-01-01

    We consider the problem of building detectors for high-level concepts using only unsupervised feature learning. For example, we would like to understand if it is possible to learn a face detector using only unlabeled images downloaded from the internet. To answer this question, we trained a simple feature learning algorithm on a large dataset of images (10 million images, each image is 200x200). The simulation is performed on a cluster of 1000 machines with fast network hardware for one week. Extensive experimental results reveal surprising evidence that such high-level concepts can indeed be learned using only unlabeled data and a simple learning algorithm.

  17. Classification of damage in structural systems using time series analysis and supervised and unsupervised pattern recognition techniques

    Science.gov (United States)

    Omenzetter, Piotr; de Lautour, Oliver R.

    2010-04-01

    Developed for studying long, periodic records of various measured quantities, time series analysis methods are inherently suited and offer interesting possibilities for Structural Health Monitoring (SHM) applications. However, their use in SHM can still be regarded as an emerging application and deserves more studies. In this research, Autoregressive (AR) models were used to fit experimental acceleration time histories from two experimental structural systems, a 3- storey bookshelf-type laboratory structure and the ASCE Phase II SHM Benchmark Structure, in healthy and several damaged states. The coefficients of the AR models were chosen as damage sensitive features. Preliminary visual inspection of the large, multidimensional sets of AR coefficients to check the presence of clusters corresponding to different damage severities was achieved using Sammon mapping - an efficient nonlinear data compression technique. Systematic classification of damage into states based on the analysis of the AR coefficients was achieved using two supervised classification techniques: Nearest Neighbor Classification (NNC) and Learning Vector Quantization (LVQ), and one unsupervised technique: Self-organizing Maps (SOM). This paper discusses the performance of AR coefficients as damage sensitive features and compares the efficiency of the three classification techniques using experimental data.

  18. Unsupervised feature learning for autonomous rock image classification

    Science.gov (United States)

    Shu, Lei; McIsaac, Kenneth; Osinski, Gordon R.; Francis, Raymond

    2017-09-01

    Autonomous rock image classification can enhance the capability of robots for geological detection and enlarge the scientific returns, both in investigation on Earth and planetary surface exploration on Mars. Since rock textural images are usually inhomogeneous and manually hand-crafting features is not always reliable, we propose an unsupervised feature learning method to autonomously learn the feature representation for rock images. In our tests, rock image classification using the learned features shows that the learned features can outperform manually selected features. Self-taught learning is also proposed to learn the feature representation from a large database of unlabelled rock images of mixed class. The learned features can then be used repeatedly for classification of any subclass. This takes advantage of the large dataset of unlabelled rock images and learns a general feature representation for many kinds of rocks. We show experimental results supporting the feasibility of self-taught learning on rock images.

  19. Unsupervised learning in neurodynamics using example-interaction approach

    Science.gov (United States)

    Zak, Michail

    1989-01-01

    A new concept for unsupervised learning based upon examples introduced to the neural network is proposed. Each example is considered as an interpolation node of the velocity field in the phase space. The velocities at these nodes are selected such that all the streamlines diverge to an attracting set imbedded in the subspace occupied by the cluster of examples. The synaptic interconnections are found from the minimization of the strength energy, while the node velocities play the role of constraints.

  20. Novel Approach to Unsupervised Change Detection Based on a Robust Semi-Supervised FCM Clustering Algorithm

    Directory of Open Access Journals (Sweden)

    Pan Shao

    2016-03-01

    Full Text Available This study presents a novel approach for unsupervised change detection in multitemporal remotely sensed images. This method addresses the problem of the analysis of the difference image by proposing a novel and robust semi-supervised fuzzy C-means (RSFCM clustering algorithm. The advantage of the RSFCM is to further introduce the pseudolabels from the difference image compared with the existing change detection methods; these methods, mainly use difference intensity levels and spatial context. First, the patterns with a high probability of belonging to the changed or unchanged class are identified by selectively thresholding the difference image histogram. Second, the pseudolabels of these nearly certain pixel-patterns are jointly exploited with the intensity levels and spatial information in the properly defined RSFCM classifier in order to discriminate the changed pixels from the unchanged pixels. Specifically, labeling knowledge is used to guide the RSFCM clustering process to enhance the change information and obtain a more accurate membership; information on spatial context helps to lower the effect of noise and outliers by modifying the membership. RSFCM can detect more changes and provide noise immunity by the synergistic exploitation of pseudolabels and spatial context. The two main contributions of this study are as follows: (1 it proposes the idea of combining the three information types from the difference image, namely, (a intensity levels, (b labels, and (c spatial context; and (2 it develops the novel RSFCM algorithm for image segmentation and forms the proposed change detection framework. The proposed method is effective and efficient for change detection as confirmed by six experimental results of this study.

  1. Supervised and unsupervised self-testing for HIV in high- and low-risk populations: a systematic review.

    Directory of Open Access Journals (Sweden)

    Nitika Pant Pai

    Full Text Available BACKGROUND: Stigma, discrimination, lack of privacy, and long waiting times partly explain why six out of ten individuals living with HIV do not access facility-based testing. By circumventing these barriers, self-testing offers potential for more people to know their sero-status. Recent approval of an in-home HIV self test in the US has sparked self-testing initiatives, yet data on acceptability, feasibility, and linkages to care are limited. We systematically reviewed evidence on supervised (self-testing and counselling aided by a health care professional and unsupervised (performed by self-tester with access to phone/internet counselling self-testing strategies. METHODS AND FINDINGS: Seven databases (Medline [via PubMed], Biosis, PsycINFO, Cinahl, African Medicus, LILACS, and EMBASE and conference abstracts of six major HIV/sexually transmitted infections conferences were searched from 1st January 2000-30th October 2012. 1,221 citations were identified and 21 studies included for review. Seven studies evaluated an unsupervised strategy and 14 evaluated a supervised strategy. For both strategies, data on acceptability (range: 74%-96%, preference (range: 61%-91%, and partner self-testing (range: 80%-97% were high. A high specificity (range: 99.8%-100% was observed for both strategies, while a lower sensitivity was reported in the unsupervised (range: 92.9%-100%; one study versus supervised (range: 97.4%-97.9%; three studies strategy. Regarding feasibility of linkage to counselling and care, 96% (n = 102/106 of individuals testing positive for HIV stated they would seek post-test counselling (unsupervised strategy, one study. No extreme adverse events were noted. The majority of data (n = 11,019/12,402 individuals, 89% were from high-income settings and 71% (n = 15/21 of studies were cross-sectional in design, thus limiting our analysis. CONCLUSIONS: Both supervised and unsupervised testing strategies were highly acceptable, preferred, and more

  2. Supervised and Unsupervised Self-Testing for HIV in High- and Low-Risk Populations: A Systematic Review

    Science.gov (United States)

    Pant Pai, Nitika; Sharma, Jigyasa; Shivkumar, Sushmita; Pillay, Sabrina; Vadnais, Caroline; Joseph, Lawrence; Dheda, Keertan; Peeling, Rosanna W.

    2013-01-01

    Background Stigma, discrimination, lack of privacy, and long waiting times partly explain why six out of ten individuals living with HIV do not access facility-based testing. By circumventing these barriers, self-testing offers potential for more people to know their sero-status. Recent approval of an in-home HIV self test in the US has sparked self-testing initiatives, yet data on acceptability, feasibility, and linkages to care are limited. We systematically reviewed evidence on supervised (self-testing and counselling aided by a health care professional) and unsupervised (performed by self-tester with access to phone/internet counselling) self-testing strategies. Methods and Findings Seven databases (Medline [via PubMed], Biosis, PsycINFO, Cinahl, African Medicus, LILACS, and EMBASE) and conference abstracts of six major HIV/sexually transmitted infections conferences were searched from 1st January 2000–30th October 2012. 1,221 citations were identified and 21 studies included for review. Seven studies evaluated an unsupervised strategy and 14 evaluated a supervised strategy. For both strategies, data on acceptability (range: 74%–96%), preference (range: 61%–91%), and partner self-testing (range: 80%–97%) were high. A high specificity (range: 99.8%–100%) was observed for both strategies, while a lower sensitivity was reported in the unsupervised (range: 92.9%–100%; one study) versus supervised (range: 97.4%–97.9%; three studies) strategy. Regarding feasibility of linkage to counselling and care, 96% (n = 102/106) of individuals testing positive for HIV stated they would seek post-test counselling (unsupervised strategy, one study). No extreme adverse events were noted. The majority of data (n = 11,019/12,402 individuals, 89%) were from high-income settings and 71% (n = 15/21) of studies were cross-sectional in design, thus limiting our analysis. Conclusions Both supervised and unsupervised testing strategies were highly acceptable

  3. Towards unsupervised ontology learning from data

    CSIR Research Space (South Africa)

    Klarman, S

    2015-07-01

    Full Text Available in the present work. In this paper, we study the problem of learning Descrip- tion Logic (DL) TBoxes from interpretations, which natu- rally translates to the task of ontology learning from data. DLs are a popular family of knowledge representation for- malisms... in T . Obviously,M(T ) contains in principle sufficient information in order to enable correct identification of T , as the following correspondence implies: M(T ) |= C v D iff T |= C v D, for every C v D in L. However, as M(T ) might consist of infinitely many mod...

  4. Analog memristive synapse in spiking networks implementing unsupervised learning

    Directory of Open Access Journals (Sweden)

    Erika Covi

    2016-10-01

    Full Text Available Emerging brain-inspired architectures call for devices that can emulate the functionality of biological synapses in order to implement new efficient computational schemes able to solve ill-posed problems. Various devices and solutions are still under investigation and, in this respect, a challenge is opened to the researchers in the field. Indeed, the optimal candidate is a device able to reproduce the complete functionality of a synapse, i.e. the typical synaptic process underlying learning in biological systems (activity-dependent synaptic plasticity. This implies a device able to change its resistance (synaptic strength, or weight upon proper electrical stimuli (synaptic activity and showing several stable resistive states throughout its dynamic range (analog behavior. Moreover, it should be able to perform spike timing dependent plasticity (STDP, an associative homosynaptic plasticity learning rule based on the delay time between the two firing neurons the synapse is connected to. This rule is a fundamental learning protocol in state-of-art networks, because it allows unsupervised learning. Notwithstanding this fact, STDP-based unsupervised learning has been proposed several times mainly for binary synapses rather than multilevel synapses composed of many binary memristors. This paper proposes an HfO2-based analog memristor as a synaptic element which performs STDP within a small spiking neuromorphic network operating unsupervised learning for character recognition. The trained network is able to recognize five characters even in case incomplete or noisy characters are displayed and it is robust to a device-to-device variability of up to +/-30%.

  5. Unsupervised incremental online learning and prediction of musical audio signals

    DEFF Research Database (Denmark)

    Marxer, Richard; Purwins, Hendrik

    2016-01-01

    Guided by the idea that musical human-computer interaction may become more effective, intuitive, and creative when basing its computer part on cognitively more plausible learning principles, we employ unsupervised incremental online learning (i.e. clustering) to build a system that predicts...... the next event in a musical sequence, given as audio input. The flow of the system is as follows: 1) segmentation by onset detection, 2) timbre representation of each segment by Mel frequency cepstrum coefficients, 3) discretization by incremental clustering, yielding a tree of different sound classes (e...

  6. Interactive Algorithms for Unsupervised Machine Learning

    Science.gov (United States)

    2015-06-01

    Copetas, and Diane Stidle who greatly enriched my life at CMU. I am thankful to Zeeshan Syed and Eu-Jin Goh who supported me during my internship at Google...for a fun and productive internship . I am looking forward to spending another year at MSR and continuing to collaborate with and learn from everyone at...the nuclear norm minimization program to exactly 1As before this could equivalently be the column space with assumption on the maximal row coherence. 12

  7. Gymnasium-based unsupervised exercise maintains benefits in oxygen uptake kinetics obtained following supervised training in type 2 diabetes.

    Science.gov (United States)

    Macananey, Oscar; O'Shea, Donal; Warmington, Stuart A; Green, Simon; Egaña, Mikel

    2012-08-01

    Supervised exercise (SE) in patients with type 2 diabetes improves oxygen uptake kinetics at the onset of exercise. Maintenance of these improvements, however, has not been examined when supervision is removed. We explored if potential improvements in oxygen uptake kinetics following a 12-week SE that combined aerobic and resistance training were maintained after a subsequent 12-week unsupervised exercise (UE). The involvement of cardiac output (CO) in these improvements was also tested. Nineteen volunteers with type 2 diabetes were recruited. Oxygen uptake kinetics and CO (inert gas rebreathing) responses to constant-load cycling at 50% ventilatory threshold (V(T)), 80% V(T), and mid-point between V(T) and peak workload (50% Δ) were examined at baseline (on 2 occasions) and following each 12-week training period. Participants decided to exercise at a local gymnasium during the UE. Thirteen subjects completed all the interventions. The time constant of phase 2 of oxygen uptake was significantly faster (p heart rate kinetics at all 3 intensities and a larger increase in CO at 30 s in relation to 240 s at 80% V(T); and these responses were maintained post-UE. Unsupervised exercise maintained benefits in oxygen uptake kinetics obtained during a supervised exercise in subjects with diabetes, and these benefits were associated with a faster dynamic response of heart rate after training.

  8. Unsupervised single-particle deep classification via statistical manifold learning

    CERN Document Server

    Wu, Jiayi; Condgon, Charles; Brett, Bevin; Chen, Shuobing; Ouyang, Qi; Mao, Youdong

    2016-01-01

    Structural heterogeneity in single-particle images presents a major challenge for high-resolution cryo-electron microscopy (cryo-EM) structure determination. Here we introduce a statistical manifold learning approach for unsupervised single-particle deep classification. When optimized for Intel high-performance computing (HPC) processors, our approach can generate thousands of reference-free class averages within several hours from hundreds of thousands of single-particle cryo-EM images. Deep classification thus assists in computational purification of single-particle datasets for high-resolution reconstruction.

  9. Unsupervised Spike Sorting Based on Discriminative Subspace Learning

    CERN Document Server

    Keshtkaran, Mohammad Reza

    2014-01-01

    Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. In this paper, we present two unsupervised spike sorting algorithms based on discriminative subspace learning. The first algorithm simultaneously learns the discriminative feature subspace and performs clustering. It uses histogram of features in the most discriminative projection to detect the number of neurons. The second algorithm performs hierarchical divisive clustering that learns a discriminative 1-dimensional subspace for clustering in each level of the hierarchy until achieving almost unimodal distribution in the subspace. The algorithms are tested on synthetic and in-vivo data, and are compared against two widely used spike sorting methods. The comparative results demonstrate that our spike sorting methods can achieve substantially higher accuracy in lower dimensional feature space, and they are highly robust to noise. Moreover, they provide significantly better cluster separab...

  10. Unsupervised deep learning reveals prognostically relevant subtypes of glioblastoma.

    Science.gov (United States)

    Young, Jonathan D; Cai, Chunhui; Lu, Xinghua

    2017-10-03

    One approach to improving the personalized treatment of cancer is to understand the cellular signaling transduction pathways that cause cancer at the level of the individual patient. In this study, we used unsupervised deep learning to learn the hierarchical structure within cancer gene expression data. Deep learning is a group of machine learning algorithms that use multiple layers of hidden units to capture hierarchically related, alternative representations of the input data. We hypothesize that this hierarchical structure learned by deep learning will be related to the cellular signaling system. Robust deep learning model selection identified a network architecture that is biologically plausible. Our model selection results indicated that the 1st hidden layer of our deep learning model should contain about 1300 hidden units to most effectively capture the covariance structure of the input data. This agrees with the estimated number of human transcription factors, which is approximately 1400. This result lends support to our hypothesis that the 1st hidden layer of a deep learning model trained on gene expression data may represent signals related to transcription factor activation. Using the 3rd hidden layer representation of each tumor as learned by our unsupervised deep learning model, we performed consensus clustering on all tumor samples-leading to the discovery of clusters of glioblastoma multiforme with differential survival. One of these clusters contained all of the glioblastoma samples with G-CIMP, a known methylation phenotype driven by the IDH1 mutation and associated with favorable prognosis, suggesting that the hidden units in the 3rd hidden layer representations captured a methylation signal without explicitly using methylation data as input. We also found differentially expressed genes and well-known mutations (NF1, IDH1, EGFR) that were uniquely correlated with each of these clusters. Exploring these unique genes and mutations will allow us to

  11. Computational phenotype discovery using unsupervised feature learning over noisy, sparse, and irregular clinical data.

    Directory of Open Access Journals (Sweden)

    Thomas A Lasko

    Full Text Available Inferring precise phenotypic patterns from population-scale clinical data is a core computational task in the development of precision, personalized medicine. The traditional approach uses supervised learning, in which an expert designates which patterns to look for (by specifying the learning task and the class labels, and where to look for them (by specifying the input variables. While appropriate for individual tasks, this approach scales poorly and misses the patterns that we don't think to look for. Unsupervised feature learning overcomes these limitations by identifying patterns (or features that collectively form a compact and expressive representation of the source data, with no need for expert input or labeled examples. Its rising popularity is driven by new deep learning methods, which have produced high-profile successes on difficult standardized problems of object recognition in images. Here we introduce its use for phenotype discovery in clinical data. This use is challenging because the largest source of clinical data - Electronic Medical Records - typically contains noisy, sparse, and irregularly timed observations, rendering them poor substrates for deep learning methods. Our approach couples dirty clinical data to deep learning architecture via longitudinal probability densities inferred using Gaussian process regression. From episodic, longitudinal sequences of serum uric acid measurements in 4368 individuals we produced continuous phenotypic features that suggest multiple population subtypes, and that accurately distinguished (0.97 AUC the uric-acid signatures of gout vs. acute leukemia despite not being optimized for the task. The unsupervised features were as accurate as gold-standard features engineered by an expert with complete knowledge of the domain, the classification task, and the class labels. Our findings demonstrate the potential for achieving computational phenotype discovery at population scale. We expect such

  12. Computational phenotype discovery using unsupervised feature learning over noisy, sparse, and irregular clinical data.

    Science.gov (United States)

    Lasko, Thomas A; Denny, Joshua C; Levy, Mia A

    2013-01-01

    Inferring precise phenotypic patterns from population-scale clinical data is a core computational task in the development of precision, personalized medicine. The traditional approach uses supervised learning, in which an expert designates which patterns to look for (by specifying the learning task and the class labels), and where to look for them (by specifying the input variables). While appropriate for individual tasks, this approach scales poorly and misses the patterns that we don't think to look for. Unsupervised feature learning overcomes these limitations by identifying patterns (or features) that collectively form a compact and expressive representation of the source data, with no need for expert input or labeled examples. Its rising popularity is driven by new deep learning methods, which have produced high-profile successes on difficult standardized problems of object recognition in images. Here we introduce its use for phenotype discovery in clinical data. This use is challenging because the largest source of clinical data - Electronic Medical Records - typically contains noisy, sparse, and irregularly timed observations, rendering them poor substrates for deep learning methods. Our approach couples dirty clinical data to deep learning architecture via longitudinal probability densities inferred using Gaussian process regression. From episodic, longitudinal sequences of serum uric acid measurements in 4368 individuals we produced continuous phenotypic features that suggest multiple population subtypes, and that accurately distinguished (0.97 AUC) the uric-acid signatures of gout vs. acute leukemia despite not being optimized for the task. The unsupervised features were as accurate as gold-standard features engineered by an expert with complete knowledge of the domain, the classification task, and the class labels. Our findings demonstrate the potential for achieving computational phenotype discovery at population scale. We expect such data

  13. Supervised learning of short and high-dimensional temporal sequences for life science measurements

    CERN Document Server

    Schleif, F -M; Hammer, B

    2011-01-01

    The analysis of physiological processes over time are often given by spectrometric or gene expression profiles over time with only few time points but a large number of measured variables. The analysis of such temporal sequences is challenging and only few methods have been proposed. The information can be encoded time independent, by means of classical expression differences for a single time point or in expression profiles over time. Available methods are limited to unsupervised and semi-supervised settings. The predictive variables can be identified only by means of wrapper or post-processing techniques. This is complicated due to the small number of samples for such studies. Here, we present a supervised learning approach, termed Supervised Topographic Mapping Through Time (SGTM-TT). It learns a supervised mapping of the temporal sequences onto a low dimensional grid. We utilize a hidden markov model (HMM) to account for the time domain and relevance learning to identify the relevant feature dimensions mo...

  14. Unsupervised learning of cone spectral classes from natural images.

    Directory of Open Access Journals (Sweden)

    Noah C Benson

    2014-06-01

    Full Text Available The first step in the evolution of primate trichromatic color vision was the expression of a third cone class not present in ancestral mammals. This observation motivates a fundamental question about the evolution of any sensory system: how is it possible to detect and exploit the presence of a novel sensory class? We explore this question in the context of primate color vision. We present an unsupervised learning algorithm capable of both detecting the number of spectral cone classes in a retinal mosaic and learning the class of each cone using the inter-cone correlations obtained in response to natural image input. The algorithm's ability to classify cones is in broad agreement with experimental evidence about functional color vision for a wide range of mosaic parameters, including those characterizing dichromacy, typical trichromacy, anomalous trichromacy, and possible tetrachromacy.

  15. Unsupervised learning of cone spectral classes from natural images.

    Science.gov (United States)

    Benson, Noah C; Manning, Jeremy R; Brainard, David H

    2014-06-01

    The first step in the evolution of primate trichromatic color vision was the expression of a third cone class not present in ancestral mammals. This observation motivates a fundamental question about the evolution of any sensory system: how is it possible to detect and exploit the presence of a novel sensory class? We explore this question in the context of primate color vision. We present an unsupervised learning algorithm capable of both detecting the number of spectral cone classes in a retinal mosaic and learning the class of each cone using the inter-cone correlations obtained in response to natural image input. The algorithm's ability to classify cones is in broad agreement with experimental evidence about functional color vision for a wide range of mosaic parameters, including those characterizing dichromacy, typical trichromacy, anomalous trichromacy, and possible tetrachromacy.

  16. An entropy-based unsupervised anomaly detection pattern learning algorithm

    Institute of Scientific and Technical Information of China (English)

    YANG Ying-jie; MA Fan-yuan

    2005-01-01

    Currently, most anomaly detection pattern learning algorithms require a set of purely normal data from which they train their model. If the data contain some intrusions buried within the training data, the algorithm may not detect these attacks because it will assume that they are normal. In reality, it is very hard to guarantee that there are no attack items in the collected training data. Focusing on this problem, in this paper,firstly a new anomaly detection measurement is proposed according to the probability characteristics of intrusion instances and normal instances. Secondly, on the basis of anomaly detection measure, we present a clusteringbased unsupervised anomaly detection patterns learning algorithm, which can overcome the shortage above. Finally, some experiments are conducted to verify the proposed algorithm is valid.

  17. Unsupervised Behavior-Specific Dictionary Learning for Abnormal Event Detection

    DEFF Research Database (Denmark)

    Ren, Huamin; Liu, Weifeng; Olsen, Søren Ingvor

    2015-01-01

    the task very challenging. Sparse representation, as one of solutions, has shown its effectiveness. The basic principle is to find a collection (a dictionary) of atoms so that each training sample can only be represented by a few atoms. However, the relationship of atoms within the dictionary is commonly...... neglected, which brings a high risk of false alarm rate: atoms from infrequent normal patterns are difficult to be distinguished from real anomalies. In this paper, we propose behavior-specific dictionaries (BSD) through unsupervised learning, in which atoms from the same dictionary representing one type...... of normal behavior in the training video. Moreover, ‘missed atoms’ that are potentially from infrequent normal features are used to refine these behavior dictionaries. To further reduce false alarms, the detection of abnormal features is not only dependent on reconstruction error from the learned...

  18. How Supervisor Experience Influences Trust, Supervision, and Trainee Learning: A Qualitative Study.

    Science.gov (United States)

    Sheu, Leslie; Kogan, Jennifer R; Hauer, Karen E

    2017-09-01

    Appropriate trust and supervision facilitate trainees' growth toward unsupervised practice. The authors investigated how supervisor experience influences trust, supervision, and subsequently trainee learning. In a two-phase qualitative inductive content analysis, phase one entailed reviewing 44 internal medicine resident and attending supervisor interviews from two institutions (July 2013 to September 2014) for themes on how supervisor experience influences trust and supervision. Three supervisor exemplars (early, developing, experienced) were developed and shared in phase two focus groups at a single institution, wherein 23 trainees validated the exemplars and discussed how each impacted learning (November 2015). Phase one: Four domains of trust and supervision varying with experience emerged: data, approach, perspective, clinical. Early supervisors were detail oriented and determined trust depending on task completion (data), were rule based (approach), drew on their experiences as trainees to guide supervision (perspective), and felt less confident clinically compared with more experienced supervisors (clinical). Experienced supervisors determined trust holistically (data), checked key aspects of patient care selectively and covertly (approach), reflected on individual experiences supervising (perspective), and felt comfortable managing clinical problems and gauging trainee abilities (clinical). Phase two: Trainees felt the exemplars reflected their experiences, described their preferences and learning needs shifting over time, and emphasized the importance of supervisor flexibility to match their learning needs. With experience, supervisors differ in their approach to trust and supervision. Supervisors need to trust themselves before being able to trust others. Trainees perceive these differences and seek supervision approaches that align with their learning needs.

  19. Continuous Online Sequence Learning with an Unsupervised Neural Network Model.

    Science.gov (United States)

    Cui, Yuwei; Ahmad, Subutar; Hawkins, Jeff

    2016-09-14

    The ability to recognize and predict temporal sequences of sensory inputs is vital for survival in natural environments. Based on many known properties of cortical neurons, hierarchical temporal memory (HTM) sequence memory recently has been proposed as a theoretical framework for sequence learning in the cortex. In this letter, we analyze properties of HTM sequence memory and apply it to sequence learning and prediction problems with streaming data. We show the model is able to continuously learn a large number of variableorder temporal sequences using an unsupervised Hebbian-like learning rule. The sparse temporal codes formed by the model can robustly handle branching temporal sequences by maintaining multiple predictions until there is sufficient disambiguating evidence. We compare the HTM sequence memory with other sequence learning algorithms, including statistical methods: autoregressive integrated moving average; feedforward neural networks-time delay neural network and online sequential extreme learning machine; and recurrent neural networks-long short-term memory and echo-state networks on sequence prediction problems with both artificial and real-world data. The HTM model achieves comparable accuracy to other state-of-the-art algorithms. The model also exhibits properties that are critical for sequence learning, including continuous online learning, the ability to handle multiple predictions and branching sequences with high-order statistics, robustness to sensor noise and fault tolerance, and good performance without task-specific hyperparameter tuning. Therefore, the HTM sequence memory not only advances our understanding of how the brain may solve the sequence learning problem but is also applicable to real-world sequence learning problems from continuous data streams.

  20. Multi-Instance Learning from Supervised View

    Institute of Scientific and Technical Information of China (English)

    Zhi-Hua Zhou

    2006-01-01

    In multi-instance learning, the training set comprises labeled bags that are composed of unlabeled instances,and the task is to predict the labels of unseen bags. This paper studies multi-instance learning from the view of supervised learning. First, by analyzing some representative learning algorithms, this paper shows that multi-instance learners can be derived from supervised learners by shifting their focuses from the discrimination on the instances to the discrimination on the bags. Second, considering that ensemble learning paradigms can effectively enhance supervised learners, this paper proposes to build multi-instance ensembles to solve multi-instance problems. Experiments on a real-world benchmark test show that ensemble learning paradigms can significantly enhance multi-instance learners.

  1. Fixed-Rank Representation for Unsupervised Visual Learning

    CERN Document Server

    Liu, Risheng; De la Torre, Fernando; Su, Zhixun

    2012-01-01

    Subspace clustering and feature extraction are two of the most extensive unsupervised visual learning tasks in computer vision and pattern recognition. In this paper, we pose these two problems in a unified framework, named fixed-rank representation (FRR). For subspace clustering, our first contribution is to show that, when the data is clean, we can efficiently solve FRR in closed-form and the global optimal solution to FRR can exactly recover the multiple subspace structure. Furthermore, we prove that under some suitable conditions, even with insufficient observations, the memberships of data points still can be exactly recovered by FRR. In the case that the data is corrupted by noises and outliers, a sparse regularization is introduced to achieve robustness for FRR. For feature extraction, we provide some new insights to understand existing methods, which lead to a new approach for robust feature extraction. As a non-trivial byproduct, a fast numerical solver is developed for FRR. Experimental results on b...

  2. Unsupervised behaviour-specific dictionary learning for abnormal event detection

    DEFF Research Database (Denmark)

    Ren, Huamin; Liu, Weifeng; Olsen, Søren Ingvor

    2015-01-01

    Abnormal event detection has been a challenge due to the lack of complete normal information in the training data and the volatility of the definitions of both normality and abnormality. Recent research applying sparse representation has shown its effectiveness in the expression of normal patterns...... the training data is only a small proportion of the surveillance data. Therefore, we propose behavior-specific dictionaries (BSD) through unsupervised learning, pursuing atoms from the same type of behavior to represent one behavior dictionary. To further improve the dictionary by introducing information from....... Despite progress in this area, the relationship of atoms within the dictionary is commonly neglected, thereafter anomalies which are detected based on reconstruction error could brings high false alarm - noise or infrequent normal visual features could be wrongly detected as anomalies, especially when...

  3. Incremental Supervised Subspace Learning for Face Recognition

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Subspace learning algorithms have been well studied in face recognition. Among them, linear discriminant analysis (LDA) is one of the most widely used supervised subspace learning method. Due to the difficulty of designing an incremental solution of the eigen decomposition on the product of matrices, there is little work for computing LDA incrementally. To avoid this limitation, an incremental supervised subspace learning (ISSL) algorithm was proposed, which incrementally learns an adaptive subspace by optimizing the maximum margin criterion (MMC). With the dynamically added face images, ISSL can effectively constrain the computational cost. Feasibility of the new algorithm has been successfully tested on different face data sets.

  4. Effects of Supervised vs. Unsupervised Training Programs on Balance and Muscle Strength in Older Adults: A Systematic Review and Meta-Analysis.

    Science.gov (United States)

    Lacroix, André; Hortobágyi, Tibor; Beurskens, Rainer; Granacher, Urs

    2017-06-01

    Balance and resistance training can improve healthy older adults' balance and muscle strength. Delivering such exercise programs at home without supervision may facilitate participation for older adults because they do not have to leave their homes. To date, no systematic literature analysis has been conducted to determine if supervision affects the effectiveness of these programs to improve healthy older adults' balance and muscle strength/power. The objective of this systematic review and meta-analysis was to quantify the effectiveness of supervised vs. unsupervised balance and/or resistance training programs on measures of balance and muscle strength/power in healthy older adults. In addition, the impact of supervision on training-induced adaptive processes was evaluated in the form of dose-response relationships by analyzing randomized controlled trials that compared supervised with unsupervised trials. A computerized systematic literature search was performed in the electronic databases PubMed, Web of Science, and SportDiscus to detect articles examining the role of supervision in balance and/or resistance training in older adults. The initially identified 6041 articles were systematically screened. Studies were included if they examined balance and/or resistance training in adults aged ≥65 years with no relevant diseases and registered at least one behavioral balance (e.g., time during single leg stance) and/or muscle strength/power outcome (e.g., time for 5-Times-Chair-Rise-Test). Finally, 11 studies were eligible for inclusion in this meta-analysis. Weighted mean standardized mean differences between subjects (SMDbs) of supervised vs. unsupervised balance/resistance training studies were calculated. The included studies were coded for the following variables: number of participants, sex, age, number and type of interventions, type of balance/strength tests, and change (%) from pre- to post-intervention values. Additionally, we coded training according to

  5. Characterization of myocardial motion patterns by unsupervised multiple kernel learning.

    Science.gov (United States)

    Sanchez-Martinez, Sergio; Duchateau, Nicolas; Erdei, Tamas; Fraser, Alan G; Bijnens, Bart H; Piella, Gemma

    2017-01-01

    We propose an independent objective method to characterize different patterns of functional responses to stress in the heart failure with preserved ejection fraction (HFPEF) syndrome by combining multiple temporally-aligned myocardial velocity traces at rest and during exercise, together with temporal information on the occurrence of cardiac events (valves openings/closures and atrial activation). The method builds upon multiple kernel learning, a machine learning technique that allows the combination of data of different nature and the reduction of their dimensionality towards a meaningful representation (output space). The learning process is kept unsupervised, to study the variability of the input traces without being conditioned by data labels. To enhance the physiological interpretation of the output space, the variability that it encodes is analyzed in the space of input signals after reconstructing the velocity traces via multiscale kernel regression. The methodology was applied to 2D sequences from a stress echocardiography protocol from 55 subjects (22 healthy, 19 HFPEF and 14 breathless subjects). The results confirm that characterization of the myocardial functional response to stress in the HFPEF syndrome may be improved by the joint analysis of multiple relevant features.

  6. Supervised Learning in Multilayer Spiking Neural Networks

    CERN Document Server

    Sporea, Ioana

    2012-01-01

    The current article introduces a supervised learning algorithm for multilayer spiking neural networks. The algorithm presented here overcomes some limitations of existing learning algorithms as it can be applied to neurons firing multiple spikes and it can in principle be applied to any linearisable neuron model. The algorithm is applied successfully to various benchmarks, such as the XOR problem and the Iris data set, as well as complex classifications problems. The simulations also show the flexibility of this supervised learning algorithm which permits different encodings of the spike timing patterns, including precise spike trains encoding.

  7. Unsupervised Feature Learning Classification With Radial Basis Function Extreme Learning Machine Using Graphic Processors.

    Science.gov (United States)

    Lam, Dao; Wunsch, Donald

    2017-01-01

    Ever-increasing size and complexity of data sets create challenges and potential tradeoffs of accuracy and speed in learning algorithms. This paper offers progress on both fronts. It presents a mechanism to train the unsupervised learning features learned from only one layer to improve performance in both speed and accuracy. The features are learned by an unsupervised feature learning (UFL) algorithm. Then, those features are trained by a fast radial basis function (RBF) extreme learning machine (ELM). By exploiting the massive parallel computing attribute of modern graphics processing unit, a customized compute unified device architecture (CUDA) kernel is developed to further speed up the computing of the RBF kernel in the ELM. Results tested on Canadian Institute for Advanced Research and Mixed National Institute of Standards and Technology data sets confirm the UFL RBF ELM achieves high accuracy, and the CUDA implementation is up to 20 times faster than CPU and the naive parallel approach.

  8. Sequential Compact Code Learning for Unsupervised Image Hashing.

    Science.gov (United States)

    Liu, Li; Shao, Ling

    2016-12-01

    Effective hashing for large-scale image databases is a popular research area, attracting much attention in computer vision and visual information retrieval. Several recent methods attempt to learn either graph embedding or semantic coding for fast and accurate applications. In this paper, a novel unsupervised framework, termed evolutionary compact embedding (ECE), is introduced to automatically learn the task-specific binary hash codes. It can be regarded as an optimization algorithm that combines the genetic programming (GP) and a boosting trick. In our architecture, each bit of ECE is iteratively computed using a weak binary classification function, which is generated through GP evolving by jointly minimizing its empirical risk with the AdaBoost strategy on a training set. We address this as greedy optimization by embedding high-dimensional data points into a similarity-preserved Hamming space with a low dimension. We systematically evaluate ECE on two data sets, SIFT 1M and GIST 1M, showing the effectiveness and the accuracy of our method for a large-scale similarity search.

  9. Learning Dynamics in Doctoral Supervision

    DEFF Research Database (Denmark)

    Kobayashi, Sofie

    This doctoral research explores doctoral supervision within life science research in a Danish university. From one angle it investigates doctoral students’ experiences with strengthening the relationship with their supervisors through a structured meeting with the supervisor, prepared as part...... of an introduction course for new doctoral students. This study showed how the course provides an effective way build supervisee agency and strengthening supervisory relationships through clarification and alignment of expectations and sharing goals about doctoral studies. From the other angle the research...

  10. Action learning in undergraduate engineering thesis supervision

    Directory of Open Access Journals (Sweden)

    Brad Stappenbelt

    2017-03-01

    Full Text Available In the present action learning implementation, twelve action learning sets were conducted over eight years. The action learning sets consisted of students involved in undergraduate engineering research thesis work. The concurrent study accompanying this initiative, investigated the influence of the action learning environment on student approaches to learning and any accompanying academic, learning and personal benefits realised. The influence of preferred learning styles on set function and student adoption of the action learning process were also examined. The action learning environment implemented had a measurable significant positive effect on student academic performance, their ability to cope with the stresses associated with conducting a research thesis, the depth of learning, the development of autonomous learners and student perception of the research thesis experience. The present study acts as an addendum to a smaller scale implementation of this action learning approach, applied to supervision of third and fourth year research projects and theses, published in 2010.

  11. Factored Translation with Unsupervised Word Clusters

    DEFF Research Database (Denmark)

    Rishøj, Christian; Søgaard, Anders

    2011-01-01

    Unsupervised word clustering algorithms — which form word clusters based on a measure of distributional similarity — have proven to be useful in providing beneficial features for various natural language processing tasks involving supervised learning. This work explores the utility of such word c....... While such an “oracle” method is not identified, evaluations indicate that unsupervised word cluster are most beneficial in sentences without unknown words....

  12. Balancing Design Project Supervision and Learning Facilitation

    DEFF Research Database (Denmark)

    Nielsen, Louise Møller

    2012-01-01

    set of demands to the design lecturer. On one hand she is the facilitator of the learning process, where the students are in charge of their own projects, and where learning happens through the students’ own experiences, successes and mistakes and on the other hand she is a supervisor, who uses her...... experiences and expertise to guide the students’ decisions in relation to the design project. This paper focuses on project supervision in the context of design education – and more specifically on how this supervision is unfolded in a Problem Based Learning culture. The paper explores the supervisor......In design there is a long tradition for apprenticeship, as well as tradition for learning through design projects. Today many design educations are positioned within the University context, and have to be aligned with the learning culture and structure, which they represent. This raises a specific...

  13. Biologically Inspired Model for Visual Cognition Achieving Unsupervised Episodic and Semantic Feature Learning.

    Science.gov (United States)

    Qiao, Hong; Li, Yinlin; Li, Fengfu; Xi, Xuanyang; Wu, Wei

    2016-10-01

    Recently, many biologically inspired visual computational models have been proposed. The design of these models follows the related biological mechanisms and structures, and these models provide new solutions for visual recognition tasks. In this paper, based on the recent biological evidence, we propose a framework to mimic the active and dynamic learning and recognition process of the primate visual cortex. From principle point of view, the main contributions are that the framework can achieve unsupervised learning of episodic features (including key components and their spatial relations) and semantic features (semantic descriptions of the key components), which support higher level cognition of an object. From performance point of view, the advantages of the framework are as follows: 1) learning episodic features without supervision-for a class of objects without a prior knowledge, the key components, their spatial relations and cover regions can be learned automatically through a deep neural network (DNN); 2) learning semantic features based on episodic features-within the cover regions of the key components, the semantic geometrical values of these components can be computed based on contour detection; 3) forming the general knowledge of a class of objects-the general knowledge of a class of objects can be formed, mainly including the key components, their spatial relations and average semantic values, which is a concise description of the class; and 4) achieving higher level cognition and dynamic updating-for a test image, the model can achieve classification and subclass semantic descriptions. And the test samples with high confidence are selected to dynamically update the whole model. Experiments are conducted on face images, and a good performance is achieved in each layer of the DNN and the semantic description learning process. Furthermore, the model can be generalized to recognition tasks of other objects with learning ability.

  14. Semi-supervised and Unsupervised Methods for Categorizing Posts in Web Discussion Forums

    OpenAIRE

    Perumal, Krish

    2016-01-01

    Web discussion forums are used by millions of people worldwide to share information belonging to a variety of domains such as automotive vehicles, pets, sports, etc. They typically contain posts that fall into different categories such as problem, solution, feedback, spam, etc. Automatic identification of these categories can aid information retrieval that is tailored for specific user requirements. Previously, a number of supervised methods have attempted to solve this problem; however, thes...

  15. Robust Arm and Hand Tracking by Unsupervised Context Learning

    Directory of Open Access Journals (Sweden)

    Vincent Spruyt

    2014-07-01

    Full Text Available Hand tracking in video is an increasingly popular research field due to the rise of novel human-computer interaction methods. However, robust and real-time hand tracking in unconstrained environments remains a challenging task due to the high number of degrees of freedom and the non-rigid character of the human hand. In this paper, we propose an unsupervised method to automatically learn the context in which a hand is embedded. This context includes the arm and any other object that coherently moves along with the hand. We introduce two novel methods to incorporate this context information into a probabilistic tracking framework, and introduce a simple yet effective solution to estimate the position of the arm. Finally, we show that our method greatly increases robustness against occlusion and cluttered background, without degrading tracking performance if no contextual information is available. The proposed real-time algorithm is shown to outperform the current state-of-the-art by evaluating it on three publicly available video datasets. Furthermore, a novel dataset is created and made publicly available for the research community.

  16. Balancing Design Project Supervision and Learning Facilitation

    DEFF Research Database (Denmark)

    Nielsen, Louise Møller

    2012-01-01

    experiences and expertise to guide the students’ decisions in relation to the design project. This paper focuses on project supervision in the context of design education – and more specifically on how this supervision is unfolded in a Problem Based Learning culture. The paper explores the supervisor......’s balance between the roles: 1) Design Project Supervisor – and 2) Learning Facilitator – with the aim to understand when to apply the different roles, and what to be aware of when doing so. This paper represents the first pilot-study of a larger research effort. It is based on a Lego Serious Play workshop......In design there is a long tradition for apprenticeship, as well as tradition for learning through design projects. Today many design educations are positioned within the University context, and have to be aligned with the learning culture and structure, which they represent. This raises a specific...

  17. Equality of Opportunity in Supervised Learning

    OpenAIRE

    Hardt, Moritz; Price, Eric; Srebro, Nathan

    2016-01-01

    We propose a criterion for discrimination against a specified sensitive attribute in supervised learning, where the goal is to predict some target based on available features. Assuming data about the predictor, target, and membership in the protected group are available, we show how to optimally adjust any learned predictor so as to remove discrimination according to our definition. Our framework also improves incentives by shifting the cost of poor classification from disadvantaged groups to...

  18. Missing Data Imputation for Supervised Learning

    OpenAIRE

    Poulos, Jason; Valle, Rafael

    2016-01-01

    This paper compares methods for imputing missing categorical data for supervised learning tasks. The ability of researchers to accurately fit a model and yield unbiased estimates may be compromised by missing data, which are prevalent in survey-based social science research. We experiment on two machine learning benchmark datasets with missing categorical data, comparing classifiers trained on non-imputed (i.e., one-hot encoded) or imputed data with different degrees of missing-data perturbat...

  19. Supervised versus unsupervised antimalarial treatment with six-dose artemether-lumefantrine: pharmacokinetic and dosage-related findings from a clinical trial in Uganda

    Directory of Open Access Journals (Sweden)

    Ruzagira Eugene

    2006-07-01

    Full Text Available Abstract Background A six-dose antimalarial regimen of artemether-lumefantrine (A/L may soon become one of the most widely used drug combination in Africa, despite possible constraints with adherence and poor absorption due to inadequate nutrition, and a lack of pharmacokinetic and effectiveness data. Methods Within a trial of supervised versus unsupervised A/L treatment in a stable Ugandan Plasmodium falciparum transmission setting, plasma lumefantrine concentrations were measured in a subset of patients on day 3 (C [lum]day3 and day 7 (C [lum]day7 post-inclusion. Predictors of lumefantrine concentrations were analysed to show how both C [lum]day7 and the weight-adjusted lumefantrine dose affect 28-day recrudescence and re-infection risks. The implications of these novel findings are discussed in terms of the emergence of lumefantrine-resistant strains in Africa. Results C [lum]day3 and C [lum]day7 distributions among 241 supervised and 238 unsupervised patients were positively skewed. Unsupervised treatment and decreasing weight-adjusted lumefantrine dose were negatively associated with C [lum]day3. Unsupervised treatment and decreasing age showed strong negative associations with C [lum]day7. Both models were poorly predictive (R-squared day7 below 400 ng/mL (p Conclusion Maintaining the present six-dose regimen and ensuring high adherence and intake are essential to maximize the public health benefits of this valuable drug combination.

  20. Opportunities to Learn Scientific Thinking in Joint Doctoral Supervision

    Science.gov (United States)

    Kobayashi, Sofie; Grout, Brian W.; Rump, Camilla Østerberg

    2015-01-01

    Research into doctoral supervision has increased rapidly over the last decades, yet our understanding of how doctoral students learn scientific thinking from supervision is limited. Most studies are based on interviews with little work being reported that is based on observation of actual supervision. While joint supervision has become widely…

  1. Semi-supervised prediction of gene regulatory networks using machine learning algorithms

    Indian Academy of Sciences (India)

    Nihir Patel; T L Wang

    2015-10-01

    Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many studies have been conducted using unsupervised methods to fulfill the task; however, such methods usually yield low prediction accuracies due to the lack of training data. In this article, we propose semi-supervised methods for GRN prediction by utilizing two machine learning algorithms, namely, support vector machines (SVM) and random forests (RF). The semi-supervised methods make use of unlabelled data for training. We investigated inductive and transductive learning approaches, both of which adopt an iterative procedure to obtain reliable negative training data from the unlabelled data. We then applied our semi-supervised methods to gene expression data of Escherichia coli and Saccharomyces cerevisiae, and evaluated the performance of our methods using the expression data. Our analysis indicated that the transductive learning approach outperformed the inductive learning approach for both organisms. However, there was no conclusive difference identified in the performance of SVM and RF. Experimental results also showed that the proposed semi-supervised methods performed better than existing supervised methods for both organisms.

  2. Graph-based semi-supervised learning

    CERN Document Server

    Subramanya, Amarnag

    2014-01-01

    While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in a variety of domains. Graph-based SSL algorithms, which bring together these two lines of work, have been shown to outperform the state-of-the-art in many applications in speech processing, computer visi

  3. Towards Statistical Unsupervised Online Learning for Music Listening with Hearing Devices

    DEFF Research Database (Denmark)

    Purwins, Hendrik; Marchini, Marco; Marxer, Richard

    2017-01-01

    relationships between neighboring speech sounds [3] (statistical learning). In [1], Figure 1, unsupervised learning is implemented as agglomerative clustering, informed by the Gestalt principle of regularity. The model [1] performs statistical learning, applying variable length Markov chains. In [2], grouping...... instantaneously, i.e. the mental representation is continuously changed after every exposure to small batches of sound events (online learning). In [2] online learning is implemented via the interplay of Cobweb clustering and a hierarchical n-gram instantaneously updating the number of timbre groups...... and Retrieval, pages 205–218. Springer, 2011. [2] R. Marxer and H. Purwins. Unsupervised incremental online learning and prediction of musical audio signals. IEEE Transactions on Audio, Speech, and Language Processing, 24(5):863–74, 2016. [3] J. Saffran, R. Aslin, and E. Newport. Statistical learning by 8-month...

  4. Unsupervised learning in persistent sensing for target recognition by wireless ad hoc networks of ground-based sensors

    Science.gov (United States)

    Hortos, William S.

    2008-04-01

    In previous work by the author, effective persistent and pervasive sensing for recognition and tracking of battlefield targets were seen to be achieved, using intelligent algorithms implemented by distributed mobile agents over a composite system of unmanned aerial vehicles (UAVs) for persistence and a wireless network of unattended ground sensors for pervasive coverage of the mission environment. While simulated performance results for the supervised algorithms of the composite system are shown to provide satisfactory target recognition over relatively brief periods of system operation, this performance can degrade by as much as 50% as target dynamics in the environment evolve beyond the period of system operation in which the training data are representative. To overcome this limitation, this paper applies the distributed approach using mobile agents to the network of ground-based wireless sensors alone, without the UAV subsystem, to provide persistent as well as pervasive sensing for target recognition and tracking. The supervised algorithms used in the earlier work are supplanted by unsupervised routines, including competitive-learning neural networks (CLNNs) and new versions of support vector machines (SVMs) for characterization of an unknown target environment. To capture the same physical phenomena from battlefield targets as the composite system, the suite of ground-based sensors can be expanded to include imaging and video capabilities. The spatial density of deployed sensor nodes is increased to allow more precise ground-based location and tracking of detected targets by active nodes. The "swarm" mobile agents enabling WSN intelligence are organized in a three processing stages: detection, recognition and sustained tracking of ground targets. Features formed from the compressed sensor data are down-selected according to an information-theoretic algorithm that reduces redundancy within the feature set, reducing the dimension of samples used in the target

  5. The (un)supervised NMF methods for discovering overlapping communities as well as hubs and outliers in networks

    Science.gov (United States)

    Wang, Xiao; Cao, Xiaochun; Jin, Di; Cao, Yixin; He, Dongxiao

    2016-03-01

    For its crucial importance in the study of large-scale networks, many researchers devote to the detection of communities in various networks. It is now widely agreed that the communities usually overlap with each other. In some communities, there exist members that play a special role as hubs (also known as leaders), whose importance merits special attention. Moreover, it is also observed that some members of the network do not belong to any communities in a convincing way, and hence recognized as outliers. Failure to detect and exclude outliers will distort, sometimes significantly, the outcome of the detected communities. In short, it is preferable for a community detection method to detect all three structures altogether. This becomes even more interesting and also more challenging when we take the unsupervised assumption, that is, we do not assume the prior knowledge of the number K of communities. Our approach here is to define a novel generative model and formalize the detection of overlapping communities as well as hubs and outliers as an optimization problem on it. When K is given, we propose a normalized symmetric nonnegative matrix factorization algorithm based on Kullback-Leibler (KL) divergence to learn the parameters of the model. Otherwise, by combining KL divergence and prior model on parameters, we introduce another parameter learning method based on Bayesian symmetric nonnegative matrix factorization to learn the parameters of the model, while determining K. Therefore, we present a community detection method arguably in the most general sense, which detects all three structures altogether without prior knowledge of the number of communities. Finally, we test the proposed method on various real-world networks. The experimental results, in contrast to several state-of-art algorithms, indicate its superior performance over other ones in terms of both clustering accuracy and community quality.

  6. Unsupervised learning via self-organization a dynamic approach

    CERN Document Server

    Kyan, Matthew; Jarrah, Kambiz; Guan, Ling

    2014-01-01

    To aid in intelligent data mining, this book introduces a new family of unsupervised algorithms that have a basis in self-organization, yet are free from many of the constraints typical of other well known self-organizing architectures. It then moves through a series of pertinent real world applications with regards to the processing of multimedia data from its role in generic image processing techniques such as the automated modeling and removal of impulse noise in digital images, to problems in digital asset management, and its various roles in feature extraction, visual enhancement, segmentation, and analysis of microbiological image data.

  7. An Introduction to Topic Modeling as an Unsupervised Machine Learning Way to Organize Text Information

    Science.gov (United States)

    Snyder, Robin M.

    2015-01-01

    The field of topic modeling has become increasingly important over the past few years. Topic modeling is an unsupervised machine learning way to organize text (or image or DNA, etc.) information such that related pieces of text can be identified. This paper/session will present/discuss the current state of topic modeling, why it is important, and…

  8. Částečně řízené učení algoritmů strojového učení (semi-supervised learning)

    OpenAIRE

    Burda, Karel

    2014-01-01

    The final thesis summarizes in its theoretical part basic knowledge of machine learning algorithms that involves supervised, semi-supervised, and unsupervised learning. Experiments with textual data in natural spoken language involving different machine learning methods and parameterization are carried out in its practical part. Conclusions made in the thesis may be of use to individuals that are at least slightly interested in this domain.

  9. Adherence to Artesunate-Amodiaquine Therapy for Uncomplicated Malaria in Rural Ghana: A Randomised Trial of Supervised versus Unsupervised Drug Administration

    Directory of Open Access Journals (Sweden)

    Kwaku Poku Asante

    2009-01-01

    Full Text Available Introduction. To enhance effective treatment, african nations including Ghana changed its malaria treatment policy from monotherapy to combination treatment with artesunate-amodiaquine (AS+AQ. The major challenge to its use in loose form is adherence. Objective. The objectives of this study were to investigate adherence and treatment outcome among patients treated with AS+AQ combination therapy for acute uncomplicated malaria. Methodology. The study was conducted in two rural districts located in the middle belt of Ghana using quantitative methods. Patients diagnosed with acute uncomplicated malaria as per the Ghana Ministry of Health malaria case definitions were randomly allocated to one of two groups. All patients in both groups were educated about the dose regimen of AS+AQ therapy and the need for adherence. Treatment with AS+AQ was supervised in one group while the other group was not supervised. Adherence was assessed by direct observation of the blister package of AS+AQ left on day 2. Results. 401 participants were randomized into the supervised (211 and unsupervised (190 groups. Compliance in both supervised (95.7% and unsupervised (92.6% groups were similar (P=.18. The commonest side-effects reported on day 2 among both groups were headaches, and body weakness. Parasite clearance by day 28 was >95% in both groups. Discussion/Conclusions. Administration of AS-AQ in both groups resulted in high levels of adherence to treatment regimen among adolescent and adult population in central Ghana. It appears that high level of adherence to AS-AQ is achievable through a rigorous education programme during routine clinic visits.

  10. An unsupervised feature learning framework for basal cell carcinoma image analysis.

    Science.gov (United States)

    Arevalo, John; Cruz-Roa, Angel; Arias, Viviana; Romero, Eduardo; González, Fabio A

    2015-06-01

    The paper addresses the problem of automatic detection of basal cell carcinoma (BCC) in histopathology images. In particular, it proposes a framework to both, learn the image representation in an unsupervised way and visualize discriminative features supported by the learned model. This paper presents an integrated unsupervised feature learning (UFL) framework for histopathology image analysis that comprises three main stages: (1) local (patch) representation learning using different strategies (sparse autoencoders, reconstruct independent component analysis and topographic independent component analysis (TICA), (2) global (image) representation learning using a bag-of-features representation or a convolutional neural network, and (3) a visual interpretation layer to highlight the most discriminant regions detected by the model. The integrated unsupervised feature learning framework was exhaustively evaluated in a histopathology image dataset for BCC diagnosis. The experimental evaluation produced a classification performance of 98.1%, in terms of the area under receiver-operating-characteristic curve, for the proposed framework outperforming by 7% the state-of-the-art discrete cosine transform patch-based representation. The proposed UFL-representation-based approach outperforms state-of-the-art methods for BCC detection. Thanks to its visual interpretation layer, the method is able to highlight discriminative tissue regions providing a better diagnosis support. Among the different UFL strategies tested, TICA-learned features exhibited the best performance thanks to its ability to capture low-level invariances, which are inherent to the nature of the problem. Copyright © 2015 Elsevier B.V. All rights reserved.

  11. The structure of verbal sequences analyzed with unsupervised learning techniques

    CERN Document Server

    Recanati, Catherine; Bennani, Younès

    2007-01-01

    Data mining allows the exploration of sequences of phenomena, whereas one usually tends to focus on isolated phenomena or on the relation between two phenomena. It offers invaluable tools for theoretical analyses and exploration of the structure of sentences, texts, dialogues, and speech. We report here the results of an attempt at using it for inspecting sequences of verbs from French accounts of road accidents. This analysis comes from an original approach of unsupervised training allowing the discovery of the structure of sequential data. The entries of the analyzer were only made of the verbs appearing in the sentences. It provided a classification of the links between two successive verbs into four distinct clusters, allowing thus text segmentation. We give here an interpretation of these clusters by applying a statistical analysis to independent semantic annotations.

  12. Improving Landsat and IRS Image Classification: Evaluation of Unsupervised and Supervised Classification through Band Ratios and DEM in a Mountainous Landscape in Nepal

    Directory of Open Access Journals (Sweden)

    Krishna Bahadur K.C.

    2009-12-01

    Full Text Available Modification of the original bands and integration of ancillary data in digital image classification has been shown to improve land use land cover classification accuracy. There are not many studies demonstrating such techniques in the context of the mountains of Nepal. The objective of this study was to explore and evaluate the use of modified band and ancillary data in Landsat and IRS image classification, and to produce a land use land cover map of the Galaudu watershed of Nepal. Classification of land uses were explored using supervised and unsupervised classification for 12 feature sets containing the LandsatMSS, TM and IRS original bands, ratios, normalized difference vegetation index, principal components and a digital elevation model. Overall, the supervised classification method produced higher accuracy than the unsupervised approach. The result from the combination of bands ration 4/3, 5/4 and 5/7 ranked the highest in terms of accuracy (82.86%, while the combination of bands 2, 3 and 4 ranked the lowest (45.29%. Inclusion of DEM as a component band shows promising results.

  13. Coexistence of reward and unsupervised learning during the operant conditioning of neural firing rates.

    Directory of Open Access Journals (Sweden)

    Robert R Kerr

    Full Text Available A fundamental goal of neuroscience is to understand how cognitive processes, such as operant conditioning, are performed by the brain. Typical and well studied examples of operant conditioning, in which the firing rates of individual cortical neurons in monkeys are increased using rewards, provide an opportunity for insight into this. Studies of reward-modulated spike-timing-dependent plasticity (RSTDP, and of other models such as R-max, have reproduced this learning behavior, but they have assumed that no unsupervised learning is present (i.e., no learning occurs without, or independent of, rewards. We show that these models cannot elicit firing rate reinforcement while exhibiting both reward learning and ongoing, stable unsupervised learning. To fix this issue, we propose a new RSTDP model of synaptic plasticity based upon the observed effects that dopamine has on long-term potentiation and depression (LTP and LTD. We show, both analytically and through simulations, that our new model can exhibit unsupervised learning and lead to firing rate reinforcement. This requires that the strengthening of LTP by the reward signal is greater than the strengthening of LTD and that the reinforced neuron exhibits irregular firing. We show the robustness of our findings to spike-timing correlations, to the synaptic weight dependence that is assumed, and to changes in the mean reward. We also consider our model in the differential reinforcement of two nearby neurons. Our model aligns more strongly with experimental studies than previous models and makes testable predictions for future experiments.

  14. Learning Probabilistic Models of Word Sense Disambiguation

    CERN Document Server

    Pedersen, Ted

    1998-01-01

    This dissertation presents several new methods of supervised and unsupervised learning of word sense disambiguation models. The supervised methods focus on performing model searches through a space of probabilistic models, and the unsupervised methods rely on the use of Gibbs Sampling and the Expectation Maximization (EM) algorithm. In both the supervised and unsupervised case, the Naive Bayesian model is found to perform well. An explanation for this success is presented in terms of learning rates and bias-variance decompositions.

  15. Semi-supervised Learning with Deep Generative Models

    NARCIS (Netherlands)

    Kingma, D.P.; Rezende, D.J.; Mohamed, S.; Welling, M.

    2014-01-01

    The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised learning one of the problems of significant practical importance in modern data analysis. We revisit the approach to semi-supervised learning with generative models and

  16. Recent progresses of neural network unsupervised learning: I. Independent component analyses generalizing PCA

    Science.gov (United States)

    Szu, Harold H.

    1999-03-01

    The early vision principle of redundancy reduction of 108 sensor excitations is understandable from computer vision viewpoint toward sparse edge maps. It is only recently derived using a truly unsupervised learning paradigm of artificial neural networks (ANN). In fact, the biological vision, Hubel- Wiesel edge maps, is reproduced seeking the underlying independent components analyses (ICA) among 102 image samples by maximizing the ANN output entropy (partial)H(V)/(partial)[W] equals (partial)[W]/(partial)t. When a pair of newborn eyes or ears meet the bustling and hustling world without supervision, they seek ICA by comparing 2 sensory measurements (x1(t), x2(t))T equalsV X(t). Assuming a linear and instantaneous mixture model of the external world X(t) equals [A] S(t), where both the mixing matrix ([A] equalsV [a1, a2] of ICA vectors and the source percentages (s1(t), s2(t))T equalsV S(t) are unknown, we seek the independent sources approximately equals [I] where the approximated sign indicates that higher order statistics (HOS) may not be trivial. Without a teacher, the ANN weight matrix [W] equalsV [w1, w2] adjusts the outputs V(t) equals tanh([W]X(t)) approximately equals [W]X(t) until no desired outputs except the (Gaussian) 'garbage' (neither YES '1' nor NO '-1' but at linear may-be range 'origin 0') defined by Gaussian covariance G equals [I] equals [W][A] learning. Consequently, rather using the faithful but dumb computer: 'GARBAGE-IN, GARBAGE-OUT,' the smarter neurocomputer will be equipped with an unsupervised learning that extracts 'RAW INFO-IN, (until) GARBAGE-OUT' for sensory knowledge acquisition in enhancing Machine IQ. We must go beyond the LMS error energy, and apply HOS To ANN. We begin with the Auto- Regression (AR) which extrapolates from the past X(t) to the future ui(t+1) equals wiTX(t) by varying the weight vector in minimizing LMS error energy E equals at the fixed point (partial)E/(partial)wi equals 0 resulted in an exact Toplitz matrix

  17. Combining Hand-crafted Rules and Unsupervised Learning in Constraint-based Morphological Disambiguation

    CERN Document Server

    Oflazer, K; Oflazer, Kemal; Tur, Gokhan

    1996-01-01

    This paper presents a constraint-based morphological disambiguation approach that is applicable languages with complex morphology--specifically agglutinative languages with productive inflectional and derivational morphological phenomena. In certain respects, our approach has been motivated by Brill's recent work, but with the observation that his transformational approach is not directly applicable to languages like Turkish. Our system combines corpus independent hand-crafted constraint rules, constraint rules that are learned via unsupervised learning from a training corpus, and additional statistical information from the corpus to be morphologically disambiguated. The hand-crafted rules are linguistically motivated and tuned to improve precision without sacrificing recall. The unsupervised learning process produces two sets of rules: (i) choose rules which choose morphological parses of a lexical item satisfying constraint effectively discarding other parses, and (ii) delete rules, which delete parses sati...

  18. Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses

    Science.gov (United States)

    Serb, Alexander; Bill, Johannes; Khiat, Ali; Berdan, Radu; Legenstein, Robert; Prodromakis, Themis

    2016-09-01

    In an increasingly data-rich world the need for developing computing systems that cannot only process, but ideally also interpret big data is becoming continuously more pressing. Brain-inspired concepts have shown great promise towards addressing this need. Here we demonstrate unsupervised learning in a probabilistic neural network that utilizes metal-oxide memristive devices as multi-state synapses. Our approach can be exploited for processing unlabelled data and can adapt to time-varying clusters that underlie incoming data by supporting the capability of reversible unsupervised learning. The potential of this work is showcased through the demonstration of successful learning in the presence of corrupted input data and probabilistic neurons, thus paving the way towards robust big-data processors.

  19. Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses

    Science.gov (United States)

    Serb, Alexander; Bill, Johannes; Khiat, Ali; Berdan, Radu; Legenstein, Robert; Prodromakis, Themis

    2016-01-01

    In an increasingly data-rich world the need for developing computing systems that cannot only process, but ideally also interpret big data is becoming continuously more pressing. Brain-inspired concepts have shown great promise towards addressing this need. Here we demonstrate unsupervised learning in a probabilistic neural network that utilizes metal-oxide memristive devices as multi-state synapses. Our approach can be exploited for processing unlabelled data and can adapt to time-varying clusters that underlie incoming data by supporting the capability of reversible unsupervised learning. The potential of this work is showcased through the demonstration of successful learning in the presence of corrupted input data and probabilistic neurons, thus paving the way towards robust big-data processors. PMID:27681181

  20. The Learning Alliance: Ethics in Doctoral Supervision

    Science.gov (United States)

    Halse, Christine; Bansel, Peter

    2012-01-01

    This paper is concerned with the ethics of relationships in doctoral supervision. We give an overview of four paradigms of doctoral supervision that have endured over the past 25 years and elucidate some of their strengths and limitations, contextualise them historically and consider their implications for doctoral supervision in the contemporary…

  1. A new supervised learning algorithm for spiking neurons.

    Science.gov (United States)

    Xu, Yan; Zeng, Xiaoqin; Zhong, Shuiming

    2013-06-01

    The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by the precise firing times of spikes. If only running time is considered, the supervised learning for a spiking neuron is equivalent to distinguishing the times of desired output spikes and the other time during the running process of the neuron through adjusting synaptic weights, which can be regarded as a classification problem. Based on this idea, this letter proposes a new supervised learning method for spiking neurons with temporal encoding; it first transforms the supervised learning into a classification problem and then solves the problem by using the perceptron learning rule. The experiment results show that the proposed method has higher learning accuracy and efficiency over the existing learning methods, so it is more powerful for solving complex and real-time problems.

  2. Learning Outlier Ensembles

    DEFF Research Database (Denmark)

    Micenková, Barbora; McWilliams, Brian; Assent, Ira

    into the existing unsupervised algorithms. In this paper, we show how to use powerful machine learning approaches to combine labeled examples together with arbitrary unsupervised outlier scoring algorithms. We aim to get the best out of the two worlds—supervised and unsupervised. Our approach is also a viable...

  3. Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses

    OpenAIRE

    Serb, Alexander; Bill, Johannes; Khiat, Ali; Berdan, Radu; Legenstein, Robert; Prodromakis, Themis

    2016-01-01

    In an increasingly data-rich world the need for developing computing systems that cannot only process, but ideally also interpret big data is becoming continuously more pressing. Brain-inspired concepts have shown great promise towards addressing this need. Here we demonstrate unsupervised learning in a probabilistic neural network that utilizes metal-oxide memristive devices as multi-state synapses. Our approach can be exploited for processing unlabelled data and can adapt to time-varying cl...

  4. Supervised Speech Separation Based on Deep Learning: An Overview

    OpenAIRE

    Wang, DeLiang; Chen, Jitong

    2017-01-01

    Speech separation is the task of separating target speech from background interference. Traditionally, speech separation is studied as a signal processing problem. A more recent approach formulates speech separation as a supervised learning problem, where the discriminative patterns of speech, speakers, and background noise are learned from training data. Over the past decade, many supervised separation algorithms have been put forward. In particular, the recent introduction of deep learning ...

  5. Deep Unsupervised Learning on a Desktop PC: A Primer for Cognitive Scientists.

    Science.gov (United States)

    Testolin, Alberto; Stoianov, Ivilin; De Filippo De Grazia, Michele; Zorzi, Marco

    2013-01-01

    Deep belief networks hold great promise for the simulation of human cognition because they show how structured and abstract representations may emerge from probabilistic unsupervised learning. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. However, learning in deep networks typically requires big datasets and it can involve millions of connection weights, which implies that simulations on standard computers are unfeasible. Developing realistic, medium-to-large-scale learning models of cognition would therefore seem to require expertise in programing parallel-computing hardware, and this might explain why the use of this promising approach is still largely confined to the machine learning community. Here we show how simulations of deep unsupervised learning can be easily performed on a desktop PC by exploiting the processors of low cost graphic cards (graphic processor units) without any specific programing effort, thanks to the use of high-level programming routines (available in MATLAB or Python). We also show that even an entry-level graphic card can outperform a small high-performance computing cluster in terms of learning time and with no loss of learning quality. We therefore conclude that graphic card implementations pave the way for a widespread use of deep learning among cognitive scientists for modeling cognition and behavior.

  6. Deep unsupervised learning on a desktop PC: A primer for cognitive scientists

    Directory of Open Access Journals (Sweden)

    Alberto eTestolin

    2013-05-01

    Full Text Available Deep belief networks hold great promise for the simulation of human cognition because they show how structured and abstract representations may emerge from probabilistic unsupervised learning. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. However, learning in deep networks typically requires big datasets and it can involve millions of connection weights, which implies that simulations on standard computers are unfeasible. Developing realistic, medium-to-large-scale learning models of cognition would therefore seem to require expertise in programming parallel-computing hardware, and this might explain why the use of this promising approach is still largely confined to the machine learning community. Here we show how simulations of deep unsupervised learning can be easily performed on a desktop PC by exploiting the processors of low-cost graphic cards (GPUs without any specific programming effort, thanks to the use of high-level programming routines (available in MATLAB or Python. We also show that even an entry-level graphic card can outperform a small high-performance computing cluster in terms of learning time and with no loss of learning quality. We therefore conclude that graphic card implementations pave the way for a widespread use of deep learning among cognitive scientists for modeling cognition and behavior.

  7. Opportunities to learn scientific thinking in joint doctoral supervision

    DEFF Research Database (Denmark)

    Kobayashi, Sofie; Grout, Brian William Wilson; Rump, Camilla Østerberg

    2015-01-01

    Research into doctoral supervision has increased rapidly over the last decades, yet our understanding of how doctoral students learn scientific thinking from supervision is limited. Most studies are based on interviews with little work being reported that is based on observation of actual supervi...

  8. Advancing Affect Modeling via Preference Learning and Unsupervised Feature Extraction

    DEFF Research Database (Denmark)

    Martínez, Héctor Pérez

    over the other examined methods. The second challenge addressed in this thesis refers to the extraction of relevant information from physiological modalities. Deep learning is proposed as an automatic approach to extract input features for models of affect from physiological signals. Experiments...... difficulties, ordinal reports such as rankings and ratings can yield more reliable affect annotations than alternative tools. This thesis explores preference learning methods to automatically learn computational models from ordinal annotations of affect. In particular, an extensive collection of training...... the complexity of hand-crafting feature extractors that combine information across dissimilar modalities of input. Frequent sequence mining is presented as a method to learn feature extractors that fuse physiological and contextual information. This method is evaluated in a game-based dataset and compared...

  9. Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data

    OpenAIRE

    Kurth, Thorsten; Zhang, Jian; Satish, Nadathur; Mitliagkas, Ioannis; Racah, Evan; Patwary, Mostofa Ali; Malas, Tareq; Sundaram, Narayanan; Bhimji, Wahid; Smorkalov, Mikhail; Deslippe, Jack; Shiryaev, Mikhail; Sridharan, Srinivas; Prabhat; Dubey, Pradeep

    2017-01-01

    This paper presents the first, 15-PetaFLOP Deep Learning system for solving scientific pattern classification problems on contemporary HPC architectures. We develop supervised convolutional architectures for discriminating signals in high-energy physics data as well as semi-supervised architectures for localizing and classifying extreme weather in climate data. Our Intelcaffe-based implementation obtains $\\sim$2TFLOP/s on a single Cori Phase-II Xeon-Phi node. We use a hybrid strategy employin...

  10. Subsampled Hessian Newton Methods for Supervised Learning.

    Science.gov (United States)

    Wang, Chien-Chih; Huang, Chun-Heng; Lin, Chih-Jen

    2015-08-01

    Newton methods can be applied in many supervised learning approaches. However, for large-scale data, the use of the whole Hessian matrix can be time-consuming. Recently, subsampled Newton methods have been proposed to reduce the computational time by using only a subset of data for calculating an approximation of the Hessian matrix. Unfortunately, we find that in some situations, the running speed is worse than the standard Newton method because cheaper but less accurate search directions are used. In this work, we propose some novel techniques to improve the existing subsampled Hessian Newton method. The main idea is to solve a two-dimensional subproblem per iteration to adjust the search direction to better minimize the second-order approximation of the function value. We prove the theoretical convergence of the proposed method. Experiments on logistic regression, linear SVM, maximum entropy, and deep networks indicate that our techniques significantly reduce the running time of the subsampled Hessian Newton method. The resulting algorithm becomes a compelling alternative to the standard Newton method for large-scale data classification.

  11. Bayesian Unsupervised Learning of DNA Regulatory Binding Regions

    Directory of Open Access Journals (Sweden)

    Jukka Corander

    2009-01-01

    positions within a set of DNA sequences are very rare in the literature. Here we show how such a learning problem can be formulated using a Bayesian model that targets to simultaneously maximize the marginal likelihood of sequence data arising under multiple motif types as well as under the background DNA model, which equals a variable length Markov chain. It is demonstrated how the adopted Bayesian modelling strategy combined with recently introduced nonstandard stochastic computation tools yields a more tractable learning procedure than is possible with the standard Monte Carlo approaches. Improvements and extensions of the proposed approach are also discussed.

  12. DRACULA: Dimensionality Reduction And Clustering for Unsupervised Learning in Astronomy

    Science.gov (United States)

    Aguena, Michel; Busti, Vinicius C.; Camacho, Hugo; Sasdelli, Michele; Ishida, Emille E. O.; Vilalta, Ricardo; Trindade, Arlindo M. M.; Gieseke, Fabien; de Souza, Rafael S.; Fantaye, Yabebal T.; Mazzali, Paolo A.

    2015-12-01

    DRACULA classifies objects using dimensionality reduction and clustering. The code has an easy interface and can be applied to separate several types of objects. It is based on tools developed in scikit-learn, with some usage requiring also the H2O package.

  13. Large-Scale Unsupervised Hashing with Shared Structure Learning.

    Science.gov (United States)

    Liu, Xianglong; Mu, Yadong; Zhang, Danchen; Lang, Bo; Li, Xuelong

    2015-09-01

    Hashing methods are effective in generating compact binary signatures for images and videos. This paper addresses an important open issue in the literature, i.e., how to learn compact hash codes by enhancing the complementarity among different hash functions. Most of prior studies solve this problem either by adopting time-consuming sequential learning algorithms or by generating the hash functions which are subject to some deliberately-designed constraints (e.g., enforcing hash functions orthogonal to one another). We analyze the drawbacks of past works and propose a new solution to this problem. Our idea is to decompose the feature space into a subspace shared by all hash functions and its complementary subspace. On one hand, the shared subspace, corresponding to the common structure across different hash functions, conveys most relevant information for the hashing task. Similar to data de-noising, irrelevant information is explicitly suppressed during hash function generation. On the other hand, in case that the complementary subspace also contains useful information for specific hash functions, the final form of our proposed hashing scheme is a compromise between these two kinds of subspaces. To make hash functions not only preserve the local neighborhood structure but also capture the global cluster distribution of the whole data, an objective function incorporating spectral embedding loss, binary quantization loss, and shared subspace contribution is introduced to guide the hash function learning. We propose an efficient alternating optimization method to simultaneously learn both the shared structure and the hash functions. Experimental results on three well-known benchmarks CIFAR-10, NUS-WIDE, and a-TRECVID demonstrate that our approach significantly outperforms state-of-the-art hashing methods.

  14. Unsupervised learning in neural networks with short range synapses

    Science.gov (United States)

    Brunnet, L. G.; Agnes, E. J.; Mizusaki, B. E. P.; Erichsen, R., Jr.

    2013-01-01

    Different areas of the brain are involved in specific aspects of the information being processed both in learning and in memory formation. For example, the hippocampus is important in the consolidation of information from short-term memory to long-term memory, while emotional memory seems to be dealt by the amygdala. On the microscopic scale the underlying structures in these areas differ in the kind of neurons involved, in their connectivity, or in their clustering degree but, at this level, learning and memory are attributed to neuronal synapses mediated by longterm potentiation and long-term depression. In this work we explore the properties of a short range synaptic connection network, a nearest neighbor lattice composed mostly by excitatory neurons and a fraction of inhibitory ones. The mechanism of synaptic modification responsible for the emergence of memory is Spike-Timing-Dependent Plasticity (STDP), a Hebbian-like rule, where potentiation/depression is acquired when causal/non-causal spikes happen in a synapse involving two neurons. The system is intended to store and recognize memories associated to spatial external inputs presented as simple geometrical forms. The synaptic modifications are continuously applied to excitatory connections, including a homeostasis rule and STDP. In this work we explore the different scenarios under which a network with short range connections can accomplish the task of storing and recognizing simple connected patterns.

  15. Document Classification Using Expectation Maximization with Semi Supervised Learning

    CERN Document Server

    Nigam, Bhawna; Salve, Sonal; Vamney, Swati

    2011-01-01

    As the amount of online document increases, the demand for document classification to aid the analysis and management of document is increasing. Text is cheap, but information, in the form of knowing what classes a document belongs to, is expensive. The main purpose of this paper is to explain the expectation maximization technique of data mining to classify the document and to learn how to improve the accuracy while using semi-supervised approach. Expectation maximization algorithm is applied with both supervised and semi-supervised approach. It is found that semi-supervised approach is more accurate and effective. The main advantage of semi supervised approach is "Dynamically Generation of New Class". The algorithm first trains a classifier using the labeled document and probabilistically classifies the unlabeled documents. The car dataset for the evaluation purpose is collected from UCI repository dataset in which some changes have been done from our side.

  16. Modeling language and cognition with deep unsupervised learning: a tutorial overview

    Science.gov (United States)

    Zorzi, Marco; Testolin, Alberto; Stoianov, Ivilin P.

    2013-01-01

    Deep unsupervised learning in stochastic recurrent neural networks with many layers of hidden units is a recent breakthrough in neural computation research. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. In this article we discuss the theoretical foundations of this approach and we review key issues related to training, testing and analysis of deep networks for modeling language and cognitive processing. The classic letter and word perception problem of McClelland and Rumelhart (1981) is used as a tutorial example to illustrate how structured and abstract representations may emerge from deep generative learning. We argue that the focus on deep architectures and generative (rather than discriminative) learning represents a crucial step forward for the connectionist modeling enterprise, because it offers a more plausible model of cortical learning as well as a way to bridge the gap between emergentist connectionist models and structured Bayesian models of cognition. PMID:23970869

  17. Modeling Language and Cognition with Deep Unsupervised Learning:A Tutorial Overview

    Directory of Open Access Journals (Sweden)

    Marco eZorzi

    2013-08-01

    Full Text Available Deep unsupervised learning in stochastic recurrent neural networks with many layers of hidden units is a recent breakthrough in neural computation research. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. In this article we discuss the theoretical foundations of this approach and we review key issues related to training, testing and analysis of deep networks for modeling language and cognitive processing. The classic letter and word perception problem of McClelland and Rumelhart (1981 is used as a tutorial example to illustrate how structured and abstract representations may emerge from deep generative learning. We argue that the focus on deep architectures and generative (rather than discriminative learning represents a crucial step forward for the connectionist modeling enterprise, because it offers a more plausible model of cortical learning as well as way to bridge the gap between emergentist connectionist models and structured Bayesian models of cognition.

  18. Modeling language and cognition with deep unsupervised learning: a tutorial overview.

    Science.gov (United States)

    Zorzi, Marco; Testolin, Alberto; Stoianov, Ivilin P

    2013-01-01

    Deep unsupervised learning in stochastic recurrent neural networks with many layers of hidden units is a recent breakthrough in neural computation research. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. In this article we discuss the theoretical foundations of this approach and we review key issues related to training, testing and analysis of deep networks for modeling language and cognitive processing. The classic letter and word perception problem of McClelland and Rumelhart (1981) is used as a tutorial example to illustrate how structured and abstract representations may emerge from deep generative learning. We argue that the focus on deep architectures and generative (rather than discriminative) learning represents a crucial step forward for the connectionist modeling enterprise, because it offers a more plausible model of cortical learning as well as a way to bridge the gap between emergentist connectionist models and structured Bayesian models of cognition.

  19. Unsupervised learning and recall of temporal sequences: an application to robotics.

    Science.gov (United States)

    Barretto, G A; Araújo, A F

    1999-06-01

    This paper describes an unsupervised neural network model for learning and recall of temporal patterns. The model comprises two groups of synaptic weights, named competitive feedforward and Hebbian feedback, which are responsible for encoding the static and temporal features of the sequence respectively. Three additional mechanisms allow the network to deal with complex sequences: context units, a neuron commitment equation, and redundancy in the representation of sequence states. The proposed network encodes a set of robot trajectories which may contain states in common, and retrieves them accurately in the correct order. Further tests evaluate the fault-tolerance and noise sensitivity of the proposed model.

  20. Full-body gestures and movements recognition: user descriptive and unsupervised learning approaches in GDL classifier

    Science.gov (United States)

    Hachaj, Tomasz; Ogiela, Marek R.

    2014-09-01

    Gesture Description Language (GDL) is a classifier that enables syntactic description and real time recognition of full-body gestures and movements. Gestures are described in dedicated computer language named Gesture Description Language script (GDLs). In this paper we will introduce new GDLs formalisms that enable recognition of selected classes of movement trajectories. The second novelty is new unsupervised learning method with which it is possible to automatically generate GDLs descriptions. We have initially evaluated both proposed extensions of GDL and we have obtained very promising results. Both the novel methodology and evaluation results will be described in this paper.

  1. Discovering phases, phase transitions, and crossovers through unsupervised machine learning: A critical examination

    Science.gov (United States)

    Hu, Wenjian; Singh, Rajiv R. P.; Scalettar, Richard T.

    2017-06-01

    We apply unsupervised machine learning techniques, mainly principal component analysis (PCA), to compare and contrast the phase behavior and phase transitions in several classical spin models—the square- and triangular-lattice Ising models, the Blume-Capel model, a highly degenerate biquadratic-exchange spin-1 Ising (BSI) model, and the two-dimensional X Y model—and we examine critically what machine learning is teaching us. We find that quantified principal components from PCA not only allow the exploration of different phases and symmetry-breaking, but they can distinguish phase-transition types and locate critical points. We show that the corresponding weight vectors have a clear physical interpretation, which is particularly interesting in the frustrated models such as the triangular antiferromagnet, where they can point to incipient orders. Unlike the other well-studied models, the properties of the BSI model are less well known. Using both PCA and conventional Monte Carlo analysis, we demonstrate that the BSI model shows an absence of phase transition and macroscopic ground-state degeneracy. The failure to capture the "charge" correlations (vorticity) in the BSI model (X Y model) from raw spin configurations points to some of the limitations of PCA. Finally, we employ a nonlinear unsupervised machine learning procedure, the "autoencoder method," and we demonstrate that it too can be trained to capture phase transitions and critical points.

  2. A single-layer network unsupervised feature learning method for white matter hyperintensity segmentation

    Science.gov (United States)

    Vijverberg, Koen; Ghafoorian, Mohsen; van Uden, Inge W. M.; de Leeuw, Frank-Erik; Platel, Bram; Heskes, Tom

    2016-03-01

    Cerebral small vessel disease (SVD) is a disorder frequently found among the old people and is associated with deterioration in cognitive performance, parkinsonism, motor and mood impairments. White matter hyperintensities (WMH) as well as lacunes, microbleeds and subcortical brain atrophy are part of the spectrum of image findings, related to SVD. Accurate segmentation of WMHs is important for prognosis and diagnosis of multiple neurological disorders such as MS and SVD. Almost all of the published (semi-)automated WMH detection models employ multiple complex hand-crafted features, which require in-depth domain knowledge. In this paper we propose to apply a single-layer network unsupervised feature learning (USFL) method to avoid hand-crafted features, but rather to automatically learn a more efficient set of features. Experimental results show that a computer aided detection system with a USFL system outperforms a hand-crafted approach. Moreover, since the two feature sets have complementary properties, a hybrid system that makes use of both hand-crafted and unsupervised learned features, shows a significant performance boost compared to each system separately, getting close to the performance of an independent human expert.

  3. Statistical mechanics of unsupervised feature learning in a restricted Boltzmann machine with binary synapses

    Science.gov (United States)

    Huang, Haiping

    2017-05-01

    Revealing hidden features in unlabeled data is called unsupervised feature learning, which plays an important role in pretraining a deep neural network. Here we provide a statistical mechanics analysis of the unsupervised learning in a restricted Boltzmann machine with binary synapses. A message passing equation to infer the hidden feature is derived, and furthermore, variants of this equation are analyzed. A statistical analysis by replica theory describes the thermodynamic properties of the model. Our analysis confirms an entropy crisis preceding the non-convergence of the message passing equation, suggesting a discontinuous phase transition as a key characteristic of the restricted Boltzmann machine. Continuous phase transition is also confirmed depending on the embedded feature strength in the data. The mean-field result under the replica symmetric assumption agrees with that obtained by running message passing algorithms on single instances of finite sizes. Interestingly, in an approximate Hopfield model, the entropy crisis is absent, and a continuous phase transition is observed instead. We also develop an iterative equation to infer the hyper-parameter (temperature) hidden in the data, which in physics corresponds to iteratively imposing Nishimori condition. Our study provides insights towards understanding the thermodynamic properties of the restricted Boltzmann machine learning, and moreover important theoretical basis to build simplified deep networks.

  4. Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP

    Science.gov (United States)

    Staras, Kevin

    2016-01-01

    We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP). The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events. A detailed analysis of this model provides insights that allow it to be extended into a full, biologically plausible, computational implementation of the architecture which is demonstrated on a visual classification task. The extended model makes use of a style of spiking network, first introduced as a model of cortical microcircuits, that is capable of Bayesian inference, effectively performing expectation maximization. The unsupervised ensemble learning mechanism, based around such spiking expectation maximization (SEM) networks whose combined outputs are mediated by ITDP, is shown to perform the visual classification task well and to generalize to unseen data. The combined ensemble performance is significantly better than that of the individual classifiers, validating the ensemble architecture and learning mechanisms. The properties of the full model are analysed in the light of extensive experiments with the classification task, including an investigation into the influence of different input feature selection schemes and a comparison with a hierarchical STDP based ensemble architecture. PMID:27760125

  5. Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP.

    Science.gov (United States)

    Shim, Yoonsik; Philippides, Andrew; Staras, Kevin; Husbands, Phil

    2016-10-01

    We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP). The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events. A detailed analysis of this model provides insights that allow it to be extended into a full, biologically plausible, computational implementation of the architecture which is demonstrated on a visual classification task. The extended model makes use of a style of spiking network, first introduced as a model of cortical microcircuits, that is capable of Bayesian inference, effectively performing expectation maximization. The unsupervised ensemble learning mechanism, based around such spiking expectation maximization (SEM) networks whose combined outputs are mediated by ITDP, is shown to perform the visual classification task well and to generalize to unseen data. The combined ensemble performance is significantly better than that of the individual classifiers, validating the ensemble architecture and learning mechanisms. The properties of the full model are analysed in the light of extensive experiments with the classification task, including an investigation into the influence of different input feature selection schemes and a comparison with a hierarchical STDP based ensemble architecture.

  6. Enhancing Adult Learning in Clinical Supervision

    Science.gov (United States)

    Goldman, Stuart

    2011-01-01

    Objective/Background: For decades, across almost every training site, clinical supervision has been considered "central to the development of skills" in psychiatry. The crucial supervisor/supervisee relationship has been described extensively in the literature, most often framed as a clinical apprenticeship of the novice to the master craftsman.…

  7. The Drive-Reinforcement Neuronal Model: A Real-Time Learning Mechanism For Unsupervised Learning

    Science.gov (United States)

    Klopf, A. H.

    1988-05-01

    The drive-reinforcement neuronal model is described as an example of a newly discovered class of real-time learning mechanisms that correlate earlier derivatives of inputs with later derivatives of outputs. The drive-reinforcement neuronal model has been demonstrated to predict a wide range of classical conditioning phenomena in animal learning. A variety of classes of connectionist and neural network models have been investigated in recent years (Hinton and Anderson, 1981; Levine, 1983; Barto, 1985; Feldman, 1985; Rumelhart and McClelland, 1986). After a brief review of these models, discussion will focus on the class of real-time models because they appear to be making the strongest contact with the experimental evidence of animal learning. Theoretical models in physics have inspired Boltzmann machines (Ackley, Hinton, and Sejnowski, 1985) and what are sometimes called Hopfield networks (Hopfield, 1982; Hopfield and Tank, 1986). These connectionist models utilize symmetric connections and adaptive equilibrium processes during which the networks settle into minimal energy states. Networks utilizing error-correction learning mechanisms go back to Rosenblatt's (1962) perception and Widrow's (1962) adaline and currently take the form of back propagation networks (Parker, 1985; Rumelhart, Hinton, and Williams, 1985, 1986). These networks require a "teacher" or "trainer" to provide error signals indicating the difference between desired and actual responses. Networks employing real-time learning mechanisms, in which the temporal association of signals is of fundamental importance, go back to Hebb (1949). Real-time learning mechanisms may require no teacher or trainer and thus may lend themselves to unsupervised learning. Such models have been extended by Klopf (1972, 1982), who introduced the notions of synaptic eligibility and generalized reinforcement. Sutton and Barto (1981) advanced this class of models by proposing that a derivative of the theoretical neuron's out

  8. Exploring the spectroscopic diversity of type Ia supernovae with Deep Learning and Unsupervised Clustering

    Science.gov (United States)

    Ishida, Emille E. O.; Sasdelli, Michele; Vilalta, Ricardo; Aguena, Michel; Busti, Vinicius C.; Camacho, Hugo; Trindade, Arlindo M. M.; Gieseke, Fabian; de Souza, Rafael S.; Fantaye, Yabebal T.; Mazzali, Paolo A.

    2017-06-01

    The existence of multiple subclasses of type Ia supernovae (SNeIa) has been the subject of great debate in the last decade. In this work, we show how machine learning tools facilitate identification of subtypes of SNe Ia. Using Deep Learning for dimensionality reduction, we were capable of performing such identification in a parameter space of significantly lower dimension than its principal component analysis counterpart. This is evidence that the progenitor system and the explosion mechanism can be described with a small number of initial physical parameters. All tools used here are publicly available in the Python package DRACULA (Dimensionality Reduction And Clustering for Unsupervised Learning in Astronomy) and can be found within COINtoolbox (https://github.com/COINtoolbox/DRACULA).

  9. 监督学习的发展动态%Current Directions in Supervised Learning Research

    Institute of Scientific and Technical Information of China (English)

    蒋艳凰; 周海芳; 杨学军

    2003-01-01

    Supervised learning is very important in machine learning area. It has been making great progress in manydirections. This article summarizes three of these directions ,which are the hot problems in supervised learning field.These three directions are (a) improving classification accuracy by learning ensembles of classifiers, (b) methods forscaling up supervised learning algorithm, (c) extracting understandable rules from classifiers.

  10. Semi-supervised Eigenvectors for Locally-biased Learning

    DEFF Research Database (Denmark)

    Hansen, Toke Jansen; Mahoney, Michael W.

    2012-01-01

    of this sort are particularly challenging for popular eigenvector-based machine learning and data analysis tools. At root, the reason is that eigenvectors are inherently global quantities. In this paper, we address this issue by providing a methodology to construct semi-supervised eigenvectors of a graph......In many applications, one has side information, e.g., labels that are provided in a semi-supervised manner, about a specific target region of a large data set, and one wants to perform machine learning and data analysis tasks "nearby" that pre-specified target region. Locally-biased problems...... Laplacian, and we illustrate how these locally-biased eigenvectors can be used to perform locally-biased machine learning. These semi-supervised eigenvectors capture successively-orthogonalized directions of maximum variance, conditioned on being well-correlated with an input seed set of nodes...

  11. Learning representation hierarchies by sharing visual features: a computational investigation of Persian character recognition with unsupervised deep learning.

    Science.gov (United States)

    Sadeghi, Zahra; Testolin, Alberto

    2017-08-01

    In humans, efficient recognition of written symbols is thought to rely on a hierarchical processing system, where simple features are progressively combined into more abstract, high-level representations. Here, we present a computational model of Persian character recognition based on deep belief networks, where increasingly more complex visual features emerge in a completely unsupervised manner by fitting a hierarchical generative model to the sensory data. Crucially, high-level internal representations emerging from unsupervised deep learning can be easily read out by a linear classifier, achieving state-of-the-art recognition accuracy. Furthermore, we tested the hypothesis that handwritten digits and letters share many common visual features: A generative model that captures the statistical structure of the letters distribution should therefore also support the recognition of written digits. To this aim, deep networks trained on Persian letters were used to build high-level representations of Persian digits, which were indeed read out with high accuracy. Our simulations show that complex visual features, such as those mediating the identification of Persian symbols, can emerge from unsupervised learning in multilayered neural networks and can support knowledge transfer across related domains.

  12. Semi-supervised manifold learning with affinity regularization for Alzheimer's disease identification using positron emission tomography imaging.

    Science.gov (United States)

    Lu, Shen; Xia, Yong; Cai, Tom Weidong; Feng, David Dagan

    2015-01-01

    Dementia, Alzheimer's disease (AD) in particular is a global problem and big threat to the aging population. An image based computer-aided dementia diagnosis method is needed to providing doctors help during medical image examination. Many machine learning based dementia classification methods using medical imaging have been proposed and most of them achieve accurate results. However, most of these methods make use of supervised learning requiring fully labeled image dataset, which usually is not practical in real clinical environment. Using large amount of unlabeled images can improve the dementia classification performance. In this study we propose a new semi-supervised dementia classification method based on random manifold learning with affinity regularization. Three groups of spatial features are extracted from positron emission tomography (PET) images to construct an unsupervised random forest which is then used to regularize the manifold learning objective function. The proposed method, stat-of-the-art Laplacian support vector machine (LapSVM) and supervised SVM are applied to classify AD and normal controls (NC). The experiment results show that learning with unlabeled images indeed improves the classification performance. And our method outperforms LapSVM on the same dataset.

  13. Action Learning in Undergraduate Engineering Thesis Supervision

    Science.gov (United States)

    Stappenbelt, Brad

    2017-01-01

    In the present action learning implementation, twelve action learning sets were conducted over eight years. The action learning sets consisted of students involved in undergraduate engineering research thesis work. The concurrent study accompanying this initiative investigated the influence of the action learning environment on student approaches…

  14. Improving Semi-Supervised Learning with Auxiliary Deep Generative Models

    DEFF Research Database (Denmark)

    Maaløe, Lars; Sønderby, Casper Kaae; Sønderby, Søren Kaae

    Deep generative models based upon continuous variational distributions parameterized by deep networks give state-of-the-art performance. In this paper we propose a framework for extending the latent representation with extra auxiliary variables in order to make the variational distribution more...... expressive for semi-supervised learning. By utilizing the stochasticity of the auxiliary variable we demonstrate how to train discriminative classifiers resulting in state-of-the-art performance within semi-supervised learning exemplified by an 0.96% error on MNIST using 100 labeled data points. Furthermore...

  15. A NEW APPROACH FOR UNSUPERVISED RESTORING IMAGES BASED ON WAVELET-DOMAIN PROJECTION PURSUIT LEARNING NETWORK

    Institute of Scientific and Technical Information of China (English)

    Lin Wei; Tian Zheng; Wen Xianbin

    2003-01-01

    The Wavelet-Domain Projection Pursuit Learning Network (WDPPLN) is proposedfor restoring degraded image. The new network combines the advantages of both projectionpursuit and wavelet shrinkage. Restoring image is very difficult when little is known about apriori knowledge for multisource degraded factors. WDPPLN successfully resolves this problemby separately processing wavelet coefficients and scale coefficients. Parameters in WDPPLN,which are used to simulate degraded factors, are estimated via WDPPLN training, using scalecoefficients. Also, WDPPLN uses soft-threshold of wavelet shrinkage technique to suppress noisein three high frequency subbands. The new method is compared with the traditional methodsand the Projection Pursuit Learning Network (PPLN) method. Experimental results demonstratethat it is an effective method for unsupervised restoring degraded image.

  16. Unsupervised learning of phase transitions: From principal component analysis to variational autoencoders

    Science.gov (United States)

    Wetzel, Sebastian J.

    2017-08-01

    We examine unsupervised machine learning techniques to learn features that best describe configurations of the two-dimensional Ising model and the three-dimensional X Y model. The methods range from principal component analysis over manifold and clustering methods to artificial neural-network-based variational autoencoders. They are applied to Monte Carlo-sampled configurations and have, a priori, no knowledge about the Hamiltonian or the order parameter. We find that the most promising algorithms are principal component analysis and variational autoencoders. Their predicted latent parameters correspond to the known order parameters. The latent representations of the models in question are clustered, which makes it possible to identify phases without prior knowledge of their existence. Furthermore, we find that the reconstruction loss function can be used as a universal identifier for phase transitions.

  17. 基于无监督特征学习的手势识别方法%A Gesture Recognition Research Based on Unsupervised Feature Learning

    Institute of Scientific and Technical Information of China (English)

    陶美平; 马力; 黄文静; 吴雨隆

    2016-01-01

    In order to solve static gesture image classification problems ,the paper presents a unsupervised learning process combining the static supervised classification .According to the unsupervised sparse auto-encoder neural network ,the image patches were trained to extract the edge feature of the image ,and these edge features are the input of a classifier ,and finally .And the classifier parameter was used to improve the classification accuracy .%针对静态手势图像的分类识别问题 ,提出了一种将无监督的特征学习和有监督的分类识别相结合的静态手势图像识别方法 ,通过无监督的稀疏自编码神经网络训练图像小块提取手势图像的边缘特征 ,并将此边缘特征作为训练分类器的输入 ,最后提出对训练好的分类器的参数进行调优从而提高准确率 .

  18. Cognitive navigation based on nonuniform Gabor space sampling, unsupervised growing networks, and reinforcement learning.

    Science.gov (United States)

    Arleo, Angelo; Smeraldi, Fabrizio; Gerstner, Wulfram

    2004-05-01

    We study spatial learning and navigation for autonomous agents. A state space representation is constructed by unsupervised Hebbian learning during exploration. As a result of learning, a representation of the continuous two-dimensional (2-D) manifold in the high-dimensional input space is found. The representation consists of a population of localized overlapping place fields covering the 2-D space densely and uniformly. This space coding is comparable to the representation provided by hippocampal place cells in rats. Place fields are learned by extracting spatio-temporal properties of the environment from sensory inputs. The visual scene is modeled using the responses of modified Gabor filters placed at the nodes of a sparse Log-polar graph. Visual sensory aliasing is eliminated by taking into account self-motion signals via path integration. This solves the hidden state problem and provides a suitable representation for applying reinforcement learning in continuous space for action selection. A temporal-difference prediction scheme is used to learn sensorimotor mappings to perform goal-oriented navigation. Population vector coding is employed to interpret ensemble neural activity. The model is validated on a mobile Khepera miniature robot.

  19. Semi-supervised Eigenvectors for Locally-biased Learning

    DEFF Research Database (Denmark)

    Hansen, Toke Jansen; Mahoney, Michael W.

    2012-01-01

    of this sort are particularly challenging for popular eigenvector-based machine learning and data analysis tools. At root, the reason is that eigenvectors are inherently global quantities. In this paper, we address this issue by providing a methodology to construct semi-supervised eigenvectors of a graph...

  20. SPATIALLY ADAPTIVE SEMI-SUPERVISED LEARNING WITH GAUSSIAN PROCESSES FOR HYPERSPECTRAL DATA ANALYSIS

    Data.gov (United States)

    National Aeronautics and Space Administration — SPATIALLY ADAPTIVE SEMI-SUPERVISED LEARNING WITH GAUSSIAN PROCESSES FOR HYPERSPECTRAL DATA ANALYSIS GOO JUN * AND JOYDEEP GHOSH* Abstract. A semi-supervised learning...

  1. SLEAS: Supervised Learning using Entropy as Attribute Selection Measure

    Directory of Open Access Journals (Sweden)

    Kishor Kumar Reddy C

    2014-10-01

    Full Text Available There is embryonic importance in scaling up the broadly used decision tree learning algorithms to huge datasets. Even though abundant diverse methodologies have been proposed, a fast tree growing algorithm without substantial decrease in accuracy and substantial increase in space complexity is essential to a greater extent. This paper aims at improving the performance of the SLIQ (Supervised Learning in Quest decision tree algorithm for classification in data mining. In the present research, we adopted entropy as attribute selection measure, which overcomes the problems facing with Gini Index. Classification accuracy of the proposed supervised learning using entropy as attribute selection measure (SLEAS algorithm is compared with the existing SLIQ algorithm using twelve datasets taken from UCI Machine Learning Repository, and the results yields that the SLEAS outperforms when compared with SLIQ decision tree. Further, error rate is also computed and the results clearly show that the SLEAS algorithm is giving less error rate when compared with SLIQ decision tree.

  2. Transfer learning improves supervised image segmentation across imaging protocols

    DEFF Research Database (Denmark)

    van Opbroek, Annegreet; Ikram, M. Arfan; Vernooij, Meike W.;

    2015-01-01

    well, often require a large amount of labeled training data that is exactly representative of the target data. We therefore propose to use transfer learning for image segmentation. Transfer-learning techniques can cope with differences in distributions between training and target data, and therefore......The variation between images obtained with different scanners or different imaging protocols presents a major challenge in automatic segmentation of biomedical images. This variation especially hampers the application of otherwise successful supervised-learning techniques which, in order to perform...... may improve performance over supervised learning for segmentation across scanners and scan protocols. We present four transfer classifiers that can train a classification scheme with only a small amount of representative training data, in addition to a larger amount of other training data...

  3. A parametric study of unsupervised anomaly detection performance in maritime imagery using manifold learning techniques

    Science.gov (United States)

    Olson, C. C.; Doster, T.

    2016-05-01

    We investigate the parameters that govern an unsupervised anomaly detection framework that uses nonlinear techniques to learn a better model of the non-anomalous data. A manifold or kernel-based model is learned from a small, uniformly sampled subset in order to reduce computational burden and under the assumption that anomalous data will have little effect on the learned model because their rarity reduces the likelihood of their inclusion in the subset. The remaining data are then projected into the learned space and their projection errors used as detection statistics. Here, kernel principal component analysis is considered for learning the background model. We consider spectral data from an 8-band multispectral sensor as well as panchromatic infrared images treated by building a data set composed of overlapping image patches. We consider detection performance as a function of patch neighborhood size as well as embedding parameters such as kernel bandwidth and dimension. ROC curves are generated over a range of parameters and compared to RX performance.

  4. A review of supervised machine learning applied to ageing research.

    Science.gov (United States)

    Fabris, Fabio; Magalhães, João Pedro de; Freitas, Alex A

    2017-04-01

    Broadly speaking, supervised machine learning is the computational task of learning correlations between variables in annotated data (the training set), and using this information to create a predictive model capable of inferring annotations for new data, whose annotations are not known. Ageing is a complex process that affects nearly all animal species. This process can be studied at several levels of abstraction, in different organisms and with different objectives in mind. Not surprisingly, the diversity of the supervised machine learning algorithms applied to answer biological questions reflects the complexities of the underlying ageing processes being studied. Many works using supervised machine learning to study the ageing process have been recently published, so it is timely to review these works, to discuss their main findings and weaknesses. In summary, the main findings of the reviewed papers are: the link between specific types of DNA repair and ageing; ageing-related proteins tend to be highly connected and seem to play a central role in molecular pathways; ageing/longevity is linked with autophagy and apoptosis, nutrient receptor genes, and copper and iron ion transport. Additionally, several biomarkers of ageing were found by machine learning. Despite some interesting machine learning results, we also identified a weakness of current works on this topic: only one of the reviewed papers has corroborated the computational results of machine learning algorithms through wet-lab experiments. In conclusion, supervised machine learning has contributed to advance our knowledge and has provided novel insights on ageing, yet future work should have a greater emphasis in validating the predictions.

  5. Unsupervised Feature Subset Selection

    DEFF Research Database (Denmark)

    Søndberg-Madsen, Nicolaj; Thomsen, C.; Pena, Jose

    2003-01-01

    This paper studies filter and hybrid filter-wrapper feature subset selection for unsupervised learning (data clustering). We constrain the search for the best feature subset by scoring the dependence of every feature on the rest of the features, conjecturing that these scores discriminate some...... irrelevant features. We report experimental results on artificial and real data for unsupervised learning of naive Bayes models. Both the filter and hybrid approaches perform satisfactorily....

  6. Hybridization of Evolutionary Mechanisms for Feature Subset Selection in Unsupervised Learning

    Science.gov (United States)

    Torres, Dolores; Ponce-de-León, Eunice; Torres, Aurora; Ochoa, Alberto; Díaz, Elva

    Feature subset selection for unsupervised learning, is a very important topic in artificial intelligence because it is the base for saving computational resources. In this implementation we use a typical testor’s methodology in order to incorporate an importance index for each variable. This paper presents the general framework and the way two hybridized meta-heuristics work in this NP-complete problem. The evolutionary mechanisms are based on the Univariate Marginal Distribution Algorithm (UMDA) and the Genetic Algorithm (GA). GA and UMDA - Estimation of Distribution Algorithm (EDA) use a very useful rapid operator implemented for finding typical testors on a very large dataset and also, both algorithms, have a local search mechanism for improving time and fitness. Experiments show that EDA is faster than GA because it has a better exploitation performance; nevertheless, GA’ solutions are more consistent.

  7. Bayesian and L$_\\mathbf{1}$ Approaches to Sparse Unsupervised Learning

    CERN Document Server

    Mohamed, Shakir; Ghahramani, Zoubin

    2011-01-01

    The use of $L_1$ regularisation for sparse learning has generated immense research interest, with successful application in such diverse areas as signal acquisition, image coding, genomics and collaborative filtering. While existing work highlights the many advantages of $L_1$ methods, in this paper we find that $L_1$ regularisation often dramatically underperforms in terms of predictive performance when compared with other methods for inferring sparsity. We focus on unsupervised latent variable models, and develop $L_1$ minimising factor models, Bayesian variants of "$L_1$", and Bayesian models with a stronger $L_0$-like sparsity induced through spike-and-slab distributions. These spike-and-slab Bayesian factor models encourage sparsity while accounting for uncertainty in a principled manner and avoiding unnecessary shrinkage of non-zero values. We demonstrate on a number of data sets that in practice spike-and-slab Bayesian methods outperform $L_1$ minimisation, even on a computational budget. We thus highl...

  8. Characterizing Interference in Radio Astronomy Observations through Active and Unsupervised Learning

    Science.gov (United States)

    Doran, G.

    2013-01-01

    In the process of observing signals from astronomical sources, radio astronomers must mitigate the effects of manmade radio sources such as cell phones, satellites, aircraft, and observatory equipment. Radio frequency interference (RFI) often occurs as short bursts (< 1 ms) across a broad range of frequencies, and can be confused with signals from sources of interest such as pulsars. With ever-increasing volumes of data being produced by observatories, automated strategies are required to detect, classify, and characterize these short "transient" RFI events. We investigate an active learning approach in which an astronomer labels events that are most confusing to a classifier, minimizing the human effort required for classification. We also explore the use of unsupervised clustering techniques, which automatically group events into classes without user input. We apply these techniques to data from the Parkes Multibeam Pulsar Survey to characterize several million detected RFI events from over a thousand hours of observation.

  9. An unsupervised machine learning model for discovering latent infectious diseases using social media data.

    Science.gov (United States)

    Lim, Sunghoon; Tucker, Conrad S; Kumara, Soundar

    2017-02-01

    The authors of this work propose an unsupervised machine learning model that has the ability to identify real-world latent infectious diseases by mining social media data. In this study, a latent infectious disease is defined as a communicable disease that has not yet been formalized by national public health institutes and explicitly communicated to the general public. Most existing approaches to modeling infectious-disease-related knowledge discovery through social media networks are top-down approaches that are based on already known information, such as the names of diseases and their symptoms. In existing top-down approaches, necessary but unknown information, such as disease names and symptoms, is mostly unidentified in social media data until national public health institutes have formalized that disease. Most of the formalizing processes for latent infectious diseases are time consuming. Therefore, this study presents a bottom-up approach for latent infectious disease discovery in a given location without prior information, such as disease names and related symptoms. Social media messages with user and temporal information are extracted during the data preprocessing stage. An unsupervised sentiment analysis model is then presented. Users' expressions about symptoms, body parts, and pain locations are also identified from social media data. Then, symptom weighting vectors for each individual and time period are created, based on their sentiment and social media expressions. Finally, latent-infectious-disease-related information is retrieved from individuals' symptom weighting vectors. Twitter data from August 2012 to May 2013 are used to validate this study. Real electronic medical records for 104 individuals, who were diagnosed with influenza in the same period, are used to serve as ground truth validation. The results are promising, with the highest precision, recall, and F1 score values of 0.773, 0.680, and 0.724, respectively. This work uses individuals

  10. Effects of coaching supervision, mentoring supervision and abusive supervision on talent development among trainee doctors in public hospitals: moderating role of clinical learning environment.

    Science.gov (United States)

    Subramaniam, Anusuiya; Silong, Abu Daud; Uli, Jegak; Ismail, Ismi Arif

    2015-08-13

    Effective talent development requires robust supervision. However, the effects of supervisory styles (coaching, mentoring and abusive supervision) on talent development and the moderating effects of clinical learning environment in the relationship between supervisory styles and talent development among public hospital trainee doctors have not been thoroughly researched. In this study, we aim to achieve the following, (1) identify the extent to which supervisory styles (coaching, mentoring and abusive supervision) can facilitate talent development among trainee doctors in public hospital and (2) examine whether coaching, mentoring and abusive supervision are moderated by clinical learning environment in predicting talent development among trainee doctors in public hospital. A questionnaire-based critical survey was conducted among trainee doctors undergoing housemanship at six public hospitals in the Klang Valley, Malaysia. Prior permission was obtained from the Ministry of Health Malaysia to conduct the research in the identified public hospitals. The survey yielded 355 responses. The results were analysed using SPSS 20.0 and SEM with AMOS 20.0. The findings of this research indicate that coaching and mentoring supervision are positively associated with talent development, and that there is no significant relationship between abusive supervision and talent development. The findings also support the moderating role of clinical learning environment on the relationships between coaching supervision-talent development, mentoring supervision-talent development and abusive supervision-talent development among public hospital trainee doctors. Overall, the proposed model indicates a 26 % variance in talent development. This study provides an improved understanding on the role of the supervisory styles (coaching and mentoring supervision) on facilitating talent development among public hospital trainee doctors. Furthermore, this study extends the literature to better

  11. Enhancing fieldwork learning using blended learning, GIS and remote supervision

    Science.gov (United States)

    Marra, Wouter A.; Alberti, Koko; Karssenberg, Derek

    2015-04-01

    Fieldwork is an important part of education in geosciences and essential to put theoretical knowledge into an authentic context. Fieldwork as teaching tool can take place in various forms, such as field-tutorial, excursion, or supervised research. Current challenges with fieldwork in education are to incorporate state-of-the art methods for digital data collection, on-site GIS-analysis and providing high-quality feedback to large groups of students in the field. We present a case on first-year earth-sciences fieldwork with approximately 80 students in the French Alps focused on geological and geomorphological mapping. Here, students work in couples and each couple maps their own fieldwork area to reconstruct the formative history. We present several major improvements for this fieldwork using a blended-learning approach, relying on open source software only. An important enhancement to the French Alps fieldwork is improving students' preparation. In a GIS environment, students explore their fieldwork areas using existing remote sensing data, a digital elevation model and derivatives to formulate testable hypotheses before the actual fieldwork. The advantage of this is that the students already know their area when arriving in the field, have started to apply the empirical cycle prior to their field visit, and are therefore eager to investigate their own research questions. During the fieldwork, students store and analyze their field observations in the same GIS environment. This enables them to get a better overview of their own collected data, and to integrate existing data sources also used in the preparation phase. This results in a quicker and enhanced understanding by the students. To enable remote access to observational data collected by students, the students synchronize their data daily with a webserver running a web map application. Supervisors can review students' progress remotely, examine and evaluate their observations in a GIS, and provide

  12. Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling

    Science.gov (United States)

    Keshtkaran, Mohammad Reza; Yang, Zhi

    2017-06-01

    Objective. Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. Approach. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Main results. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. Significance. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

  13. Collaborative Supervised Learning for Sensor Networks

    Science.gov (United States)

    Wagstaff, Kiri L.; Rebbapragada, Umaa; Lane, Terran

    2011-01-01

    Collaboration methods for distributed machine-learning algorithms involve the specification of communication protocols for the learners, which can query other learners and/or broadcast their findings preemptively. Each learner incorporates information from its neighbors into its own training set, and they are thereby able to bootstrap each other to higher performance. Each learner resides at a different node in the sensor network and makes observations (collects data) independently of the other learners. After being seeded with an initial labeled training set, each learner proceeds to learn in an iterative fashion. New data is collected and classified. The learner can then either broadcast its most confident classifications for use by other learners, or can query neighbors for their classifications of its least confident items. As such, collaborative learning combines elements of both passive (broadcast) and active (query) learning. It also uses ideas from ensemble learning to combine the multiple responses to a given query into a single useful label. This approach has been evaluated against current non-collaborative alternatives, including training a single classifier and deploying it at all nodes with no further learning possible, and permitting learners to learn from their own most confident judgments, absent interaction with their neighbors. On several data sets, it has been consistently found that active collaboration is the best strategy for a distributed learner network. The main advantages include the ability for learning to take place autonomously by collaboration rather than by requiring intervention from an oracle (usually human), and also the ability to learn in a distributed environment, permitting decisions to be made in situ and to yield faster response time.

  14. Age and gender classification in the wild with unsupervised feature learning

    Science.gov (United States)

    Wan, Lihong; Huo, Hong; Fang, Tao

    2017-03-01

    Inspired by unsupervised feature learning (UFL) within the self-taught learning framework, we propose a method based on UFL, convolution representation, and part-based dimensionality reduction to handle facial age and gender classification, which are two challenging problems under unconstrained circumstances. First, UFL is introduced to learn selective receptive fields (filters) automatically by applying whitening transformation and spherical k-means on random patches collected from unlabeled data. The learning process is fast and has no hyperparameters to tune. Then, the input image is convolved with these filters to obtain filtering responses on which local contrast normalization is applied. Average pooling and feature concatenation are then used to form global face representation. Finally, linear discriminant analysis with part-based strategy is presented to reduce the dimensions of the global representation and to improve classification performances further. Experiments on three challenging databases, namely, Labeled faces in the wild, Gallagher group photos, and Adience, demonstrate the effectiveness of the proposed method relative to that of state-of-the-art approaches.

  15. Facial nerve image enhancement from CBCT using supervised learning technique.

    Science.gov (United States)

    Ping Lu; Barazzetti, Livia; Chandran, Vimal; Gavaghan, Kate; Weber, Stefan; Gerber, Nicolas; Reyes, Mauricio

    2015-08-01

    Facial nerve segmentation plays an important role in surgical planning of cochlear implantation. Clinically available CBCT images are used for surgical planning. However, its relatively low resolution renders the identification of the facial nerve difficult. In this work, we present a supervised learning approach to enhance facial nerve image information from CBCT. A supervised learning approach based on multi-output random forest was employed to learn the mapping between CBCT and micro-CT images. Evaluation was performed qualitatively and quantitatively by using the predicted image as input for a previously published dedicated facial nerve segmentation, and cochlear implantation surgical planning software, OtoPlan. Results show the potential of the proposed approach to improve facial nerve image quality as imaged by CBCT and to leverage its segmentation using OtoPlan.

  16. The clustering-based case-based reasoning for imbalanced business failure prediction: a hybrid approach through integrating unsupervised process with supervised process

    Science.gov (United States)

    Li, Hui; Yu, Jun-Ling; Yu, Le-An; Sun, Jie

    2014-05-01

    Case-based reasoning (CBR) is one of the main forecasting methods in business forecasting, which performs well in prediction and holds the ability of giving explanations for the results. In business failure prediction (BFP), the number of failed enterprises is relatively small, compared with the number of non-failed ones. However, the loss is huge when an enterprise fails. Therefore, it is necessary to develop methods (trained on imbalanced samples) which forecast well for this small proportion of failed enterprises and performs accurately on total accuracy meanwhile. Commonly used methods constructed on the assumption of balanced samples do not perform well in predicting minority samples on imbalanced samples consisting of the minority/failed enterprises and the majority/non-failed ones. This article develops a new method called clustering-based CBR (CBCBR), which integrates clustering analysis, an unsupervised process, with CBR, a supervised process, to enhance the efficiency of retrieving information from both minority and majority in CBR. In CBCBR, various case classes are firstly generated through hierarchical clustering inside stored experienced cases, and class centres are calculated out by integrating cases information in the same clustered class. When predicting the label of a target case, its nearest clustered case class is firstly retrieved by ranking similarities between the target case and each clustered case class centre. Then, nearest neighbours of the target case in the determined clustered case class are retrieved. Finally, labels of the nearest experienced cases are used in prediction. In the empirical experiment with two imbalanced samples from China, the performance of CBCBR was compared with the classical CBR, a support vector machine, a logistic regression and a multi-variant discriminate analysis. The results show that compared with the other four methods, CBCBR performed significantly better in terms of sensitivity for identifying the

  17. Extracting Canopy Surface Texture from Airborne Laser Scanning Data for the Supervised and Unsupervised Prediction of Area-Based Forest Characteristics

    Directory of Open Access Journals (Sweden)

    Mikko T. Niemi

    2016-07-01

    Full Text Available Area-based analyses of airborne laser scanning (ALS data are an established approach to obtain wall-to-wall predictions of forest characteristics for vast areas. The analyses of sparse data in particular are based on the height value distributions, which do not produce optimal information on the horizontal forest structure. We evaluated the complementary potential of features quantifying the textural variation of ALS-based canopy height models (CHMs for both supervised (linear regression and unsupervised (k-Means clustering analyses. Based on a comprehensive literature review, we identified a total of four texture analysis methods that produced rotation-invariant features of different order and scale. The CHMs and the textural features were derived from practical sparse-density, leaf-off ALS data originally acquired for ground elevation modeling. The features were extracted from a circular window of 254 m2 and related with boreal forest characteristics observed from altogether 155 field sample plots. Features based on gray-level histograms, distribution of forest patches, and gray-level co-occurrence matrices were related with plot volume, basal area, and mean diameter with coefficients of determination (R2 of up to 0.63–0.70, whereas features that measured the uniformity of local binary patterns of the CHMs performed poorer. Overall, the textural features compared favorably with benchmark features based on the point data, indicating that the textural features contain additional information useful for the prediction of forest characteristics. Due to the developed processing routines for raster data, the CHM features may potentially be extracted with a lower computational burden, which promotes their use for applications such as pre-stratification or guiding the field plot sampling based solely on ALS data.

  18. Chemical named entity recognition in patents by domain knowledge and unsupervised feature learning

    Science.gov (United States)

    Zhang, Yaoyun; Xu, Jun; Chen, Hui; Wang, Jingqi; Wu, Yonghui; Prakasam, Manu; Xu, Hua

    2016-01-01

    Medicinal chemistry patents contain rich information about chemical compounds. Although much effort has been devoted to extracting chemical entities from scientific literature, limited numbers of patent mining systems are publically available, probably due to the lack of large manually annotated corpora. To accelerate the development of information extraction systems for medicinal chemistry patents, the 2015 BioCreative V challenge organized a track on Chemical and Drug Named Entity Recognition from patent text (CHEMDNER patents). This track included three individual subtasks: (i) Chemical Entity Mention Recognition in Patents (CEMP), (ii) Chemical Passage Detection (CPD) and (iii) Gene and Protein Related Object task (GPRO). We participated in the two subtasks of CEMP and CPD using machine learning-based systems. Our machine learning-based systems employed the algorithms of conditional random fields (CRF) and structured support vector machines (SSVMs), respectively. To improve the performance of the NER systems, two strategies were proposed for feature engineering: (i) domain knowledge features of dictionaries, chemical structural patterns and semantic type information present in the context of the candidate chemical and (ii) unsupervised feature learning algorithms to generate word representation features by Brown clustering and a novel binarized Word embedding to enhance the generalizability of the system. Further, the system output for the CPD task was yielded based on the patent titles and abstracts with chemicals recognized in the CEMP task. The effects of the proposed feature strategies on both the machine learning-based systems were investigated. Our best system achieved the second best performance among 21 participating teams in CEMP with a precision of 87.18%, a recall of 90.78% and a F-measure of 88.94% and was the top performing system among nine participating teams in CPD with a sensitivity of 98.60%, a specificity of 87.21%, an accuracy of 94.75%, a

  19. Chemical named entity recognition in patents by domain knowledge and unsupervised feature learning.

    Science.gov (United States)

    Zhang, Yaoyun; Xu, Jun; Chen, Hui; Wang, Jingqi; Wu, Yonghui; Prakasam, Manu; Xu, Hua

    2016-01-01

    Medicinal chemistry patents contain rich information about chemical compounds. Although much effort has been devoted to extracting chemical entities from scientific literature, limited numbers of patent mining systems are publically available, probably due to the lack of large manually annotated corpora. To accelerate the development of information extraction systems for medicinal chemistry patents, the 2015 BioCreative V challenge organized a track on Chemical and Drug Named Entity Recognition from patent text (CHEMDNER patents). This track included three individual subtasks: (i) Chemical Entity Mention Recognition in Patents (CEMP), (ii) Chemical Passage Detection (CPD) and (iii) Gene and Protein Related Object task (GPRO). We participated in the two subtasks of CEMP and CPD using machine learning-based systems. Our machine learning-based systems employed the algorithms of conditional random fields (CRF) and structured support vector machines (SSVMs), respectively. To improve the performance of the NER systems, two strategies were proposed for feature engineering: (i) domain knowledge features of dictionaries, chemical structural patterns and semantic type information present in the context of the candidate chemical and (ii) unsupervised feature learning algorithms to generate word representation features by Brown clustering and a novel binarized Word embedding to enhance the generalizability of the system. Further, the system output for the CPD task was yielded based on the patent titles and abstracts with chemicals recognized in the CEMP task.The effects of the proposed feature strategies on both the machine learning-based systems were investigated. Our best system achieved the second best performance among 21 participating teams in CEMP with a precision of 87.18%, a recall of 90.78% and aF-measure of 88.94% and was the top performing system among nine participating teams in CPD with a sensitivity of 98.60%, a specificity of 87.21%, an accuracy of 94.75%, a

  20. Modeling Time Series Data for Supervised Learning

    Science.gov (United States)

    Baydogan, Mustafa Gokce

    2012-01-01

    Temporal data are increasingly prevalent and important in analytics. Time series (TS) data are chronological sequences of observations and an important class of temporal data. Fields such as medicine, finance, learning science and multimedia naturally generate TS data. Each series provide a high-dimensional data vector that challenges the learning…

  1. Semi-supervised learning for ordinal Kernel Discriminant Analysis.

    Science.gov (United States)

    Pérez-Ortiz, M; Gutiérrez, P A; Carbonero-Ruz, M; Hervás-Martínez, C

    2016-12-01

    Ordinal classification considers those classification problems where the labels of the variable to predict follow a given order. Naturally, labelled data is scarce or difficult to obtain in this type of problems because, in many cases, ordinal labels are given by a user or expert (e.g. in recommendation systems). Firstly, this paper develops a new strategy for ordinal classification where both labelled and unlabelled data are used in the model construction step (a scheme which is referred to as semi-supervised learning). More specifically, the ordinal version of kernel discriminant learning is extended for this setting considering the neighbourhood information of unlabelled data, which is proposed to be computed in the feature space induced by the kernel function. Secondly, a new method for semi-supervised kernel learning is devised in the context of ordinal classification, which is combined with our developed classification strategy to optimise the kernel parameters. The experiments conducted compare 6 different approaches for semi-supervised learning in the context of ordinal classification in a battery of 30 datasets, showing (1) the good synergy of the ordinal version of discriminant analysis and the use of unlabelled data and (2) the advantage of computing distances in the feature space induced by the kernel function.

  2. Biomedical data analysis by supervised manifold learning.

    Science.gov (United States)

    Alvarez-Meza, A M; Daza-Santacoloma, G; Castellanos-Dominguez, G

    2012-01-01

    Biomedical data analysis is usually carried out by assuming that the information structure embedded into the biomedical recordings is linear, but that statement actually does not corresponds to the real behavior of the extracted features. In order to improve the accuracy of an automatic system to diagnostic support, and to reduce the computational complexity of the employed classifiers, we propose a nonlinear dimensionality reduction methodology based on manifold learning with multiple kernel representations, which learns the underlying data structure of biomedical information. Moreover, our approach can be used as a tool that allows the specialist to do a visual analysis and interpretation about the studied variables describing the health condition. Obtained results show how our approach maps the original high dimensional features into an embedding space where simple and straightforward classification strategies achieve a suitable system performance.

  3. Supervised Learning with Complex-valued Neural Networks

    CERN Document Server

    Suresh, Sundaram; Savitha, Ramasamy

    2013-01-01

    Recent advancements in the field of telecommunications, medical imaging and signal processing deal with signals that are inherently time varying, nonlinear and complex-valued. The time varying, nonlinear characteristics of these signals can be effectively analyzed using artificial neural networks.  Furthermore, to efficiently preserve the physical characteristics of these complex-valued signals, it is important to develop complex-valued neural networks and derive their learning algorithms to represent these signals at every step of the learning process. This monograph comprises a collection of new supervised learning algorithms along with novel architectures for complex-valued neural networks. The concepts of meta-cognition equipped with a self-regulated learning have been known to be the best human learning strategy. In this monograph, the principles of meta-cognition have been introduced for complex-valued neural networks in both the batch and sequential learning modes. For applications where the computati...

  4. Using Supervised Deep Learning for Human Age Estimation Problem

    Science.gov (United States)

    Drobnyh, K. A.; Polovinkin, A. N.

    2017-05-01

    Automatic facial age estimation is a challenging task upcoming in recent years. In this paper, we propose using the supervised deep learning features to improve an accuracy of the existing age estimation algorithms. There are many approaches solving the problem, an active appearance model and the bio-inspired features are two of them which showed the best accuracy. For experiments we chose popular publicly available FG-NET database, which contains 1002 images with a broad variety of light, pose, and expression. LOPO (leave-one-person-out) method was used to estimate the accuracy. Experiments demonstrated that adding supervised deep learning features has improved accuracy for some basic models. For example, adding the features to an active appearance model gave the 4% gain (the error decreased from 4.59 to 4.41).

  5. Semi-supervised Learning with Density Based Distances

    CERN Document Server

    Bijral, Avleen S; Srebro, Nathan

    2012-01-01

    We present a simple, yet effective, approach to Semi-Supervised Learning. Our approach is based on estimating density-based distances (DBD) using a shortest path calculation on a graph. These Graph-DBD estimates can then be used in any distance-based supervised learning method, such as Nearest Neighbor methods and SVMs with RBF kernels. In order to apply the method to very large data sets, we also present a novel algorithm which integrates nearest neighbor computations into the shortest path search and can find exact shortest paths even in extremely large dense graphs. Significant runtime improvement over the commonly used Laplacian regularization method is then shown on a large scale dataset.

  6. Very Short Literature Survey From Supervised Learning To Surrogate Modeling

    CERN Document Server

    Brusan, Altay

    2012-01-01

    The past century was era of linear systems. Either systems (especially industrial ones) were simple (quasi)linear or linear approximations were accurate enough. In addition, just at the ending decades of the century profusion of computing devices were available, before then due to lack of computational resources it was not easy to evaluate available nonlinear system studies. At the moment both these two conditions changed, systems are highly complex and also pervasive amount of computation strength is cheap and easy to achieve. For recent era, a new branch of supervised learning well known as surrogate modeling (meta-modeling, surface modeling) has been devised which aimed at answering new needs of modeling realm. This short literature survey is on to introduce surrogate modeling to whom is familiar with the concepts of supervised learning. Necessity, challenges and visions of the topic are considered.

  7. Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning.

    Science.gov (United States)

    Wu, Guorong; Kim, Minjeong; Wang, Qian; Munsell, Brent C; Shen, Dinggang

    2016-07-01

    Feature selection is a critical step in deformable image registration. In particular, selecting the most discriminative features that accurately and concisely describe complex morphological patterns in image patches improves correspondence detection, which in turn improves image registration accuracy. Furthermore, since more and more imaging modalities are being invented to better identify morphological changes in medical imaging data, the development of deformable image registration method that scales well to new image modalities or new image applications with little to no human intervention would have a significant impact on the medical image analysis community. To address these concerns, a learning-based image registration framework is proposed that uses deep learning to discover compact and highly discriminative features upon observed imaging data. Specifically, the proposed feature selection method uses a convolutional stacked autoencoder to identify intrinsic deep feature representations in image patches. Since deep learning is an unsupervised learning method, no ground truth label knowledge is required. This makes the proposed feature selection method more flexible to new imaging modalities since feature representations can be directly learned from the observed imaging data in a very short amount of time. Using the LONI and ADNI imaging datasets, image registration performance was compared to two existing state-of-the-art deformable image registration methods that use handcrafted features. To demonstrate the scalability of the proposed image registration framework, image registration experiments were conducted on 7.0-T brain MR images. In all experiments, the results showed that the new image registration framework consistently demonstrated more accurate registration results when compared to state of the art.

  8. Scalable High Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning

    Science.gov (United States)

    Wu, Guorong; Kim, Minjeong; Wang, Qian; Munsell, Brent C.

    2015-01-01

    Feature selection is a critical step in deformable image registration. In particular, selecting the most discriminative features that accurately and concisely describe complex morphological patterns in image patches improves correspondence detection, which in turn improves image registration accuracy. Furthermore, since more and more imaging modalities are being invented to better identify morphological changes in medical imaging data,, the development of deformable image registration method that scales well to new image modalities or new image applications with little to no human intervention would have a significant impact on the medical image analysis community. To address these concerns, a learning-based image registration framework is proposed that uses deep learning to discover compact and highly discriminative features upon observed imaging data. Specifically, the proposed feature selection method uses a convolutional stacked auto-encoder to identify intrinsic deep feature representations in image patches. Since deep learning is an unsupervised learning method, no ground truth label knowledge is required. This makes the proposed feature selection method more flexible to new imaging modalities since feature representations can be directly learned from the observed imaging data in a very short amount of time. Using the LONI and ADNI imaging datasets, image registration performance was compared to two existing state-of-the-art deformable image registration methods that use handcrafted features. To demonstrate the scalability of the proposed image registration framework image registration experiments were conducted on 7.0-tesla brain MR images. In all experiments, the results showed the new image registration framework consistently demonstrated more accurate registration results when compared to state-of-the-art. PMID:26552069

  9. CAPES: Unsupervised Storage Performance Tuning Using Neural Network-Based Deep Reinforcement Learning

    CERN Document Server

    CERN. Geneva

    2017-01-01

    Parameter tuning is an important task of storage performance optimization. Current practice usually involves numerous tweak-benchmark cycles that are slow and costly. To address this issue, we developed CAPES, a model-less deep reinforcement learning-based unsupervised parameter tuning system driven by a deep neural network (DNN). It is designed to nd the optimal values of tunable parameters in computer systems, from a simple client-server system to a large data center, where human tuning can be costly and often cannot achieve optimal performance. CAPES takes periodic measurements of a target computer system’s state, and trains a DNN which uses Q-learning to suggest changes to the system’s current parameter values. CAPES is minimally intrusive, and can be deployed into a production system to collect training data and suggest tuning actions during the system’s daily operation. Evaluation of a prototype on a Lustre system demonstrates an increase in I/O throughput up to 45% at saturation point. About the...

  10. Empirical Comparison of Evaluation Methods for Unsupervised Learning of Morphology Comparaison empirique des méthodes d'évaluation de l'apprentissage non-supervisé de la morphologie

    Directory of Open Access Journals (Sweden)

    Sami Virpioja

    2012-03-01

    Full Text Available Unsupervised and semi-supervised learning of morphology provide practical solutions for processing morphologically rich languages with less human labor than the traditional rule-based analyzers. Direct evaluation of the learning methods using linguistic reference analyses is important for their development, as evaluation through the final applications is often time consuming. However, even linguistic evaluation is not straightforward for full morphological analysis, because the morpheme labels generated by the learning method can be arbitrary. We review the previous evaluation methods for the learning tasks and propose new variations. In order to compare the methods, we perform an extensive meta-evaluation using the large collection of results from the Morpho Challenge competitions.

  11. Unsupervised learning of mixture models based on swarm intelligence and neural networks with optimal completion using incomplete data

    Directory of Open Access Journals (Sweden)

    Ahmed R. Abas

    2012-07-01

    Full Text Available In this paper, a new algorithm is presented for unsupervised learning of finite mixture models (FMMs using data set with missing values. This algorithm overcomes the local optima problem of the Expectation-Maximization (EM algorithm via integrating the EM algorithm with Particle Swarm Optimization (PSO. In addition, the proposed algorithm overcomes the problem of biased estimation due to overlapping clusters in estimating missing values in the input data set by integrating locally-tuned general regression neural networks with Optimal Completion Strategy (OCS. A comparison study shows the superiority of the proposed algorithm over other algorithms commonly used in the literature in unsupervised learning of FMM parameters that result in minimum mis-classification errors when used in clustering incomplete data set that is generated from overlapping clusters and these clusters are largely different in their sizes.

  12. Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification

    OpenAIRE

    Javier Juan-Albarracín; Elies Fuster-Garcia; Manjón, José V.; Montserrat Robles; Aparici, F.; L Martí-Bonmatí; García-Gómez, Juan M.

    2015-01-01

    Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised appro...

  13. Pulsar Search Using Supervised Machine Learning

    Science.gov (United States)

    Ford, John M.

    2017-05-01

    Pulsars are rapidly rotating neutron stars which emit a strong beam of energy through mechanisms that are not entirely clear to physicists. These very dense stars are used by astrophysicists to study many basic physical phenomena, such as the behavior of plasmas in extremely dense environments, behavior of pulsar-black hole pairs, and tests of general relativity. Many of these tasks require a large ensemble of pulsars to provide enough statistical information to answer the scientific questions posed by physicists. In order to provide more pulsars to study, there are several large-scale pulsar surveys underway, which are generating a huge backlog of unprocessed data. Searching for pulsars is a very labor-intensive process, currently requiring skilled people to examine and interpret plots of data output by analysis programs. An automated system for screening the plots will speed up the search for pulsars by a very large factor. Research to date on using machine learning and pattern recognition has not yielded a completely satisfactory system, as systems with the desired near 100% recall have false positive rates that are higher than desired, causing more manual labor in the classification of pulsars. This work proposed to research, identify, propose and develop methods to overcome the barriers to building an improved classification system with a false positive rate of less than 1% and a recall of near 100% that will be useful for the current and next generation of large pulsar surveys. The results show that it is possible to generate classifiers that perform as needed from the available training data. While a false positive rate of 1% was not reached, recall of over 99% was achieved with a false positive rate of less than 2%. Methods of mitigating the imbalanced training and test data were explored and found to be highly effective in enhancing classification accuracy.

  14. Semi-supervised Adapted HMMs for Unusual Event Detection

    OpenAIRE

    Zhang, Dong; Gatica-Perez, Daniel; Bengio, Samy

    2004-01-01

    We address the problem of temporal unusual event detection. Unusual events are characterized by a number of features (rarity, unexpectedness, and relevance) that limit the application of traditional supervised model-based approaches. We propose a semi-supervised adapted Hidden Markov Model (HMM) framework, in which usual event models are first learned from a large amount of (commonly available) training data, while unusual event models are learned by Bayesian adaptation in an unsupervised man...

  15. Semi-supervised Adapted HMMs for Unusual Event Detection

    OpenAIRE

    Zhang, Dong; Gatica-Perez, Daniel; Bengio, Samy; McCowan, Iain A.

    2005-01-01

    We address the problem of temporal unusual event detection. Unusual events are characterized by a number of features (rarity, unexpectedness, and relevance) that limit the application of traditional supervised model-based approaches. We propose a semi-supervised adapted Hidden Markov Model (HMM) framework, in which usual event models are first learned from a large amount of (commonly available) training data, while unusual event models are learned by Bayesian adaptation in an unsupervised man...

  16. Sequence-structure relationship study in all-α transmembrane proteins using an unsupervised learning approach.

    Science.gov (United States)

    Esque, Jérémy; Urbain, Aurélie; Etchebest, Catherine; de Brevern, Alexandre G

    2015-11-01

    Transmembrane proteins (TMPs) are major drug targets, but the knowledge of their precise topology structure remains highly limited compared with globular proteins. In spite of the difficulties in obtaining their structures, an important effort has been made these last years to increase their number from an experimental and computational point of view. In view of this emerging challenge, the development of computational methods to extract knowledge from these data is crucial for the better understanding of their functions and in improving the quality of structural models. Here, we revisit an efficient unsupervised learning procedure, called Hybrid Protein Model (HPM), which is applied to the analysis of transmembrane proteins belonging to the all-α structural class. HPM method is an original classification procedure that efficiently combines sequence and structure learning. The procedure was initially applied to the analysis of globular proteins. In the present case, HPM classifies a set of overlapping protein fragments, extracted from a non-redundant databank of TMP 3D structure. After fine-tuning of the learning parameters, the optimal classification results in 65 clusters. They represent at best similar relationships between sequence and local structure properties of TMPs. Interestingly, HPM distinguishes among the resulting clusters two helical regions with distinct hydrophobic patterns. This underlines the complexity of the topology of these proteins. The HPM classification enlightens unusual relationship between amino acids in TMP fragments, which can be useful to elaborate new amino acids substitution matrices. Finally, two challenging applications are described: the first one aims at annotating protein functions (channel or not), the second one intends to assess the quality of the structures (X-ray or models) via a new scoring function deduced from the HPM classification.

  17. Semi-supervised Learning for Photometric Supernova Classification

    CERN Document Server

    Richards, Joseph W; Freeman, Peter E; Schafer, Chad M; Poznanski, Dovi

    2011-01-01

    We present a semi-supervised method for photometric supernova typing. Our approach is to first use the nonlinear dimension reduction technique diffusion map to detect structure in a database of supernova light curves and subsequently employ random forest classification on a spectroscopically confirmed training set to learn a model that can predict the type of each newly observed supernova. We demonstrate that this is an effective method for supernova typing. As supernova numbers increase, our semi-supervised method efficiently utilizes this information to improve classification, a property not enjoyed by template based methods. Applied to supernova data simulated by Kessler et al. (2010b) to mimic those of the Dark Energy Survey, our methods achieve (cross-validated) 96% Type Ia purity and 86% Type Ia efficiency on the spectroscopic sample, but only 56% Type Ia purity and 48% efficiency on the photometric sample due to their spectroscopic followup strategy. To improve the performance on the photometric sample...

  18. Unsupervised Learning for Efficient Texture Estimation From Limited Discrete Orientation Data

    Science.gov (United States)

    Niezgoda, Stephen R.; Glover, Jared

    2013-11-01

    The estimation of orientation distribution functions (ODFs) from discrete orientation data, as produced by electron backscatter diffraction or crystal plasticity micromechanical simulations, is typically achieved via techniques such as the Williams-Imhof-Matthies-Vinel (WIMV) algorithm or generalized spherical harmonic expansions, which were originally developed for computing an ODF from pole figures measured by X-ray or neutron diffraction. These techniques rely on ad-hoc methods for choosing parameters, such as smoothing half-width and bandwidth, and for enforcing positivity constraints and appropriate normalization. In general, such approaches provide little or no information-theoretic guarantees as to their optimality in describing the given dataset. In the current study, an unsupervised learning algorithm is proposed which uses a finite mixture of Bingham distributions for the estimation of ODFs from discrete orientation data. The Bingham distribution is an antipodally-symmetric, max-entropy distribution on the unit quaternion hypersphere. The proposed algorithm also introduces a minimum message length criterion, a common tool in information theory for balancing data likelihood with model complexity, to determine the number of components in the Bingham mixture. This criterion leads to ODFs which are less likely to overfit (or underfit) the data, eliminating the need for a priori parameter choices.

  19. Baccalaureate nursing students' perceptions of learning and supervision in the clinical environment.

    Science.gov (United States)

    Dimitriadou, Maria; Papastavrou, Evridiki; Efstathiou, Georgios; Theodorou, Mamas

    2015-06-01

    This study is an exploration of nursing students' experiences within the clinical learning environment (CLE) and supervision provided in hospital settings. A total of 357 second-year nurse students from all universities in Cyprus participated in the study. Data were collected using the Clinical Learning Environment, Supervision and Nurse Teacher instrument. The dimension "supervisory relationship (mentor)", as well as the frequency of individualized supervision meetings, were found to be important variables in the students' clinical learning. However, no statistically-significant connection was established between successful mentor relationship and team supervision. The majority of students valued their mentor's supervision more highly than a nurse teacher's supervision toward the fulfillment of learning outcomes. The dimensions "premises of nursing care" and "premises of learning" were highly correlated, indicating that a key component of a quality clinical learning environment is the quality of care delivered. The results suggest the need to modify educational strategies that foster desirable learning for students in response to workplace demands.

  20. [The Identification of Lettuce Varieties by Using Unsupervised Possibilistic Fuzzy Learning Vector Quantization and Near Infrared Spectroscopy].

    Science.gov (United States)

    Wu, Xiao-hong; Cai, Pei-qiang; Wu, Bin; Sun, Jun; Ji, Gang

    2016-03-01

    To solve the noisy sensitivity problem of fuzzy learning vector quantization (FLVQ), unsupervised possibilistic fuzzy learning vector quantization (UPFLVQ) was proposed based on unsupervised possibilistic fuzzy clustering (UPFC). UPFLVQ aimed to use fuzzy membership values and typicality values of UPFC to update the learning rate of learning vector quantization network and cluster centers. UPFLVQ is an unsupervised machine learning algorithm and it can be applied to classify without learning samples. UPFLVQ was used in the identification of lettuce varieties by near infrared spectroscopy (NIS). Short wave and long wave near infrared spectra of three types of lettuces were collected by FieldSpec@3 portable spectrometer in the wave-length range of 350-2 500 nm. When the near infrared spectra were compressed by principal component analysis (PCA), the first three principal components explained 97.50% of the total variance in near infrared spectra. After fuzzy c-means (FCM). clustering was performed for its cluster centers as the initial cluster centers of UPFLVQ, UPFLVQ could classify lettuce varieties with the terminal fuzzy membership values and typicality values. The experimental results showed that UPFLVQ together with NIS provided an effective method of identification of lettuce varieties with advantages such as fast testing, high accuracy rate and non-destructive characteristics. UPFLVQ is a clustering algorithm by combining UPFC and FLVQ, and it need not prepare any learning samples for the identification of lettuce varieties by NIS. UPFLVQ is suitable for linear separable data clustering and it provides a novel method for fast and nondestructive identification of lettuce varieties.

  1. Robust head pose estimation via supervised manifold learning.

    Science.gov (United States)

    Wang, Chao; Song, Xubo

    2014-05-01

    Head poses can be automatically estimated using manifold learning algorithms, with the assumption that with the pose being the only variable, the face images should lie in a smooth and low-dimensional manifold. However, this estimation approach is challenging due to other appearance variations related to identity, head location in image, background clutter, facial expression, and illumination. To address the problem, we propose to incorporate supervised information (pose angles of training samples) into the process of manifold learning. The process has three stages: neighborhood construction, graph weight computation and projection learning. For the first two stages, we redefine inter-point distance for neighborhood construction as well as graph weight by constraining them with the pose angle information. For Stage 3, we present a supervised neighborhood-based linear feature transformation algorithm to keep the data points with similar pose angles close together but the data points with dissimilar pose angles far apart. The experimental results show that our method has higher estimation accuracy than the other state-of-art algorithms and is robust to identity and illumination variations.

  2. Semi-Supervised Learning Based on Manifold in BCI

    Institute of Scientific and Technical Information of China (English)

    Ji-Ying Zhong; Xu Lei; De-Zhong Yao

    2009-01-01

    A Laplacian support vector machine (LapSVM) algorithm,a semi-supervised learning based on manifold,is introduced to brain-computer interface (BCI) to raise the classification precision and reduce the subjects' training complexity.The data are collected from three subjects in a three-task mental imagery experiment.LapSVM and transductive SVM (TSVM) are trained with a few labeled samples and a large number of unlabeled samples.The results confirm that LapSVM has a much better classification than TSVM.

  3. Generalization of Supervised Learning for Binary Mask Estimation

    DEFF Research Database (Denmark)

    May, Tobias; Gerkmann, Timo

    2014-01-01

    This paper addresses the problem of speech segregation by es- timating the ideal binary mask (IBM) from noisy speech. Two methods will be compared, one supervised learning approach that incorporates a priori knowledge about the feature distri- bution observed during training. The second method...... solely relies on a frame-based speech presence probability (SPP) es- timation, and therefore, does not depend on the acoustic con- dition seen during training. We investigate the influence of mismatches between the acoustic conditions used for training and testing on the IBM estimation performance...

  4. Learning outcomes using video in supervision and peer feedback during clinical skills training

    DEFF Research Database (Denmark)

    Lauridsen, Henrik Hein; Toftgård, Rie Castella; Nørgaard, Cita

    supervision of clinical skills (formative assessment). Demonstrations of these principles will be presented as video podcasts during the session. The learning outcomes of video supervision and peer-feedback were assessed in an online questionnaire survey. Results Results of the supervision showed large self...

  5. ZeitZeiger: supervised learning for high-dimensional data from an oscillatory system

    National Research Council Canada - National Science Library

    Hughey, Jacob J; Hastie, Trevor; Butte, Atul J

    2016-01-01

    Numerous biological systems oscillate over time or space. Despite these oscillators' importance, data from an oscillatory system is problematic for existing methods of regularized supervised learning...

  6. Unsupervised nonlinear dimensionality reduction machine learning methods applied to multiparametric MRI in cerebral ischemia: preliminary results

    Science.gov (United States)

    Parekh, Vishwa S.; Jacobs, Jeremy R.; Jacobs, Michael A.

    2014-03-01

    The evaluation and treatment of acute cerebral ischemia requires a technique that can determine the total area of tissue at risk for infarction using diagnostic magnetic resonance imaging (MRI) sequences. Typical MRI data sets consist of T1- and T2-weighted imaging (T1WI, T2WI) along with advanced MRI parameters of diffusion-weighted imaging (DWI) and perfusion weighted imaging (PWI) methods. Each of these parameters has distinct radiological-pathological meaning. For example, DWI interrogates the movement of water in the tissue and PWI gives an estimate of the blood flow, both are critical measures during the evolution of stroke. In order to integrate these data and give an estimate of the tissue at risk or damaged; we have developed advanced machine learning methods based on unsupervised non-linear dimensionality reduction (NLDR) techniques. NLDR methods are a class of algorithms that uses mathematically defined manifolds for statistical sampling of multidimensional classes to generate a discrimination rule of guaranteed statistical accuracy and they can generate a two- or three-dimensional map, which represents the prominent structures of the data and provides an embedded image of meaningful low-dimensional structures hidden in their high-dimensional observations. In this manuscript, we develop NLDR methods on high dimensional MRI data sets of preclinical animals and clinical patients with stroke. On analyzing the performance of these methods, we observed that there was a high of similarity between multiparametric embedded images from NLDR methods and the ADC map and perfusion map. It was also observed that embedded scattergram of abnormal (infarcted or at risk) tissue can be visualized and provides a mechanism for automatic methods to delineate potential stroke volumes and early tissue at risk.

  7. Path Control Experiment of Mobile Robot Based on Supervised Learning

    Directory of Open Access Journals (Sweden)

    Gao Chi

    2013-07-01

    Full Text Available To solve the weak capacity and low control accuracy of the robots which adapt to the complex working conditions, proposed that a path control method based on the driving experience and supervised learning. According to the slope road geometry characteristics, established the modeling study due to ramp pavement path control method and the control structure based on monitoring and self-learning. Made use of the Global Navigation Satellite System did the experiment. The test data illustrates that when the running speed is not greater than 5 m / s, the straight-line trajectory path transverse vertical deviation within 士20cm ,which proved that the control method has a high feasibility. 

  8. SUPERVISED LEARNING METHODS FOR BANGLA WEB DOCUMENT CATEGORIZATION

    Directory of Open Access Journals (Sweden)

    Ashis Kumar Mandal

    2014-09-01

    Full Text Available This paper explores the use of machine learning approaches, or more specifically, four supervised learning Methods, namely Decision Tree(C 4.5, K-Nearest Neighbour (KNN, Naïve Bays (NB, and Support Vector Machine (SVM for categorization of Bangla web documents. This is a task of automatically sorting a set of documents into categories from a predefined set. Whereas a wide range of methods have been applied to English text categorization, relatively few studies have been conducted on Bangla language text categorization. Hence, we attempt to analyze the efficiency of those four methods for categorization of Bangla documents. In order to validate, Bangla corpus from various websites has been developed and used as examples for the experiment. For Bangla, empirical results support that all four methods produce satisfactory performance with SVM attaining good result in terms of high dimensional and relatively noisy document feature vectors.

  9. Mining visual collocation patterns via self-supervised subspace learning.

    Science.gov (United States)

    Yuan, Junsong; Wu, Ying

    2012-04-01

    Traditional text data mining techniques are not directly applicable to image data which contain spatial information and are characterized by high-dimensional visual features. It is not a trivial task to discover meaningful visual patterns from images because the content variations and spatial dependence in visual data greatly challenge most existing data mining methods. This paper presents a novel approach to coping with these difficulties for mining visual collocation patterns. Specifically, the novelty of this work lies in the following new contributions: 1) a principled solution to the discovery of visual collocation patterns based on frequent itemset mining and 2) a self-supervised subspace learning method to refine the visual codebook by feeding back discovered patterns via subspace learning. The experimental results show that our method can discover semantically meaningful patterns efficiently and effectively.

  10. Prototype Vector Machine for Large Scale Semi-Supervised Learning

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Kai; Kwok, James T.; Parvin, Bahram

    2009-04-29

    Practicaldataminingrarelyfalls exactlyinto the supervisedlearning scenario. Rather, the growing amount of unlabeled data poses a big challenge to large-scale semi-supervised learning (SSL). We note that the computationalintensivenessofgraph-based SSLarises largely from the manifold or graph regularization, which in turn lead to large models that are dificult to handle. To alleviate this, we proposed the prototype vector machine (PVM), a highlyscalable,graph-based algorithm for large-scale SSL. Our key innovation is the use of"prototypes vectors" for effcient approximation on both the graph-based regularizer and model representation. The choice of prototypes are grounded upon two important criteria: they not only perform effective low-rank approximation of the kernel matrix, but also span a model suffering the minimum information loss compared with the complete model. We demonstrate encouraging performance and appealing scaling properties of the PVM on a number of machine learning benchmark data sets.

  11. Multicultural supervision: lessons learned about an ongoing struggle.

    Science.gov (United States)

    Christiansen, Abigail Tolhurst; Thomas, Volker; Kafescioglu, Nilufer; Karakurt, Gunnur; Lowe, Walter; Smith, William; Wittenborn, Andrea

    2011-01-01

    This article examines the experiences of seven diverse therapists in a supervision course as they wrestled with the real-world application of multicultural supervision. Existing literature on multicultural supervision does not address the difficulties that arise in addressing multicultural issues in the context of the supervision relationship. The experiences of six supervisory candidates and one mentoring supervisor in addressing multicultural issues in supervision are explored. Guidelines for conversations regarding multicultural issues are provided.

  12. Unsupervised Learning of Structural Representation of Percussive Audio Using a Hierarchical Dirichlet Process Hidden Markov Model

    DEFF Research Database (Denmark)

    Antich, Jose Luis Diez; Paterna, Mattia; Marxer, Richard

    2016-01-01

    A method is proposed that extracts a structural representation of percussive audio in an unsupervised manner. It consists of two parts: 1) The input signal is segmented into blocks of approximately even duration, aligned to a metrical grid, using onset and timbre feature extraction, agglomerative...

  13. Descriptor Learning via Supervised Manifold Regularization for Multioutput Regression.

    Science.gov (United States)

    Zhen, Xiantong; Yu, Mengyang; Islam, Ali; Bhaduri, Mousumi; Chan, Ian; Li, Shuo

    2016-06-08

    Multioutput regression has recently shown great ability to solve challenging problems in both computer vision and medical image analysis. However, due to the huge image variability and ambiguity, it is fundamentally challenging to handle the highly complex input-target relationship of multioutput regression, especially with indiscriminate high-dimensional representations. In this paper, we propose a novel supervised descriptor learning (SDL) algorithm for multioutput regression, which can establish discriminative and compact feature representations to improve the multivariate estimation performance. The SDL is formulated as generalized low-rank approximations of matrices with a supervised manifold regularization. The SDL is able to simultaneously extract discriminative features closely related to multivariate targets and remove irrelevant and redundant information by transforming raw features into a new low-dimensional space aligned to targets. The achieved discriminative while compact descriptor largely reduces the variability and ambiguity for multioutput regression, which enables more accurate and efficient multivariate estimation. We conduct extensive evaluation of the proposed SDL on both synthetic data and real-world multioutput regression tasks for both computer vision and medical image analysis. Experimental results have shown that the proposed SDL can achieve high multivariate estimation accuracy on all tasks and largely outperforms the algorithms in the state of the arts. Our method establishes a novel SDL framework for multioutput regression, which can be widely used to boost the performance in different applications.

  14. Supervised learning of semantic classes for image annotation and retrieval.

    Science.gov (United States)

    Carneiro, Gustavo; Chan, Antoni B; Moreno, Pedro J; Vasconcelos, Nuno

    2007-03-01

    A probabilistic formulation for semantic image annotation and retrieval is proposed. Annotation and retrieval are posed as classification problems where each class is defined as the group of database images labeled with a common semantic label. It is shown that, by establishing this one-to-one correspondence between semantic labels and semantic classes, a minimum probability of error annotation and retrieval are feasible with algorithms that are 1) conceptually simple, 2) computationally efficient, and 3) do not require prior semantic segmentation of training images. In particular, images are represented as bags of localized feature vectors, a mixture density estimated for each image, and the mixtures associated with all images annotated with a common semantic label pooled into a density estimate for the corresponding semantic class. This pooling is justified by a multiple instance learning argument and performed efficiently with a hierarchical extension of expectation-maximization. The benefits of the supervised formulation over the more complex, and currently popular, joint modeling of semantic label and visual feature distributions are illustrated through theoretical arguments and extensive experiments. The supervised formulation is shown to achieve higher accuracy than various previously published methods at a fraction of their computational cost. Finally, the proposed method is shown to be fairly robust to parameter tuning.

  15. An AdaBoost algorithm for multiclass semi-supervised learning

    NARCIS (Netherlands)

    Tanha, J.; van Someren, M.; Afsarmanesh, H.; Zaki, M.J.; Siebes, A.; Yu, J.X.; Goethals, B.; Webb, G.; Wu, X.

    2012-01-01

    We present an algorithm for multiclass Semi-Supervised learning which is learning from a limited amount of labeled data and plenty of unlabeled data. Existing semi-supervised algorithms use approaches such as one-versus-all to convert the multiclass problem to several binary classification problems

  16. Phenotype classification of zebrafish embryos by supervised learning.

    Directory of Open Access Journals (Sweden)

    Nathalie Jeanray

    Full Text Available Zebrafish is increasingly used to assess biological properties of chemical substances and thus is becoming a specific tool for toxicological and pharmacological studies. The effects of chemical substances on embryo survival and development are generally evaluated manually through microscopic observation by an expert and documented by several typical photographs. Here, we present a methodology to automatically classify brightfield images of wildtype zebrafish embryos according to their defects by using an image analysis approach based on supervised machine learning. We show that, compared to manual classification, automatic classification results in 90 to 100% agreement with consensus voting of biological experts in nine out of eleven considered defects in 3 days old zebrafish larvae. Automation of the analysis and classification of zebrafish embryo pictures reduces the workload and time required for the biological expert and increases the reproducibility and objectivity of this classification.

  17. Detection of money laundering groups using supervised learning in networks

    CERN Document Server

    Savage, David; Chou, Pauline; Zhang, Xiuzhen; Yu, Xinghuo

    2016-01-01

    Money laundering is a major global problem, enabling criminal organisations to hide their ill-gotten gains and to finance further operations. Prevention of money laundering is seen as a high priority by many governments, however detection of money laundering without prior knowledge of predicate crimes remains a significant challenge. Previous detection systems have tended to focus on individuals, considering transaction histories and applying anomaly detection to identify suspicious behaviour. However, money laundering involves groups of collaborating individuals, and evidence of money laundering may only be apparent when the collective behaviour of these groups is considered. In this paper we describe a detection system that is capable of analysing group behaviour, using a combination of network analysis and supervised learning. This system is designed for real-world application and operates on networks consisting of millions of interacting parties. Evaluation of the system using real-world data indicates th...

  18. Online Semi-Supervised Learning on Quantized Graphs

    CERN Document Server

    Valko, Michal; Huang, Ling; Ting, Daniel

    2012-01-01

    In this paper, we tackle the problem of online semi-supervised learning (SSL). When data arrive in a stream, the dual problems of computation and data storage arise for any SSL method. We propose a fast approximate online SSL algorithm that solves for the harmonic solution on an approximate graph. We show, both empirically and theoretically, that good behavior can be achieved by collapsing nearby points into a set of local "representative points" that minimize distortion. Moreover, we regularize the harmonic solution to achieve better stability properties. We apply our algorithm to face recognition and optical character recognition applications to show that we can take advantage of the manifold structure to outperform the previous methods. Unlike previous heuristic approaches, we show that our method yields provable performance bounds.

  19. Using Supervised Learning to Improve Monte Carlo Integral Estimation

    CERN Document Server

    Tracey, Brendan; Alonso, Juan J

    2011-01-01

    Monte Carlo (MC) techniques are often used to estimate integrals of a multivariate function using randomly generated samples of the function. In light of the increasing interest in uncertainty quantification and robust design applications in aerospace engineering, the calculation of expected values of such functions (e.g. performance measures) becomes important. However, MC techniques often suffer from high variance and slow convergence as the number of samples increases. In this paper we present Stacked Monte Carlo (StackMC), a new method for post-processing an existing set of MC samples to improve the associated integral estimate. StackMC is based on the supervised learning techniques of fitting functions and cross validation. It should reduce the variance of any type of Monte Carlo integral estimate (simple sampling, importance sampling, quasi-Monte Carlo, MCMC, etc.) without adding bias. We report on an extensive set of experiments confirming that the StackMC estimate of an integral is more accurate than ...

  20. Phenotype classification of zebrafish embryos by supervised learning.

    Science.gov (United States)

    Jeanray, Nathalie; Marée, Raphaël; Pruvot, Benoist; Stern, Olivier; Geurts, Pierre; Wehenkel, Louis; Muller, Marc

    2015-01-01

    Zebrafish is increasingly used to assess biological properties of chemical substances and thus is becoming a specific tool for toxicological and pharmacological studies. The effects of chemical substances on embryo survival and development are generally evaluated manually through microscopic observation by an expert and documented by several typical photographs. Here, we present a methodology to automatically classify brightfield images of wildtype zebrafish embryos according to their defects by using an image analysis approach based on supervised machine learning. We show that, compared to manual classification, automatic classification results in 90 to 100% agreement with consensus voting of biological experts in nine out of eleven considered defects in 3 days old zebrafish larvae. Automation of the analysis and classification of zebrafish embryo pictures reduces the workload and time required for the biological expert and increases the reproducibility and objectivity of this classification.

  1. Unsupervised feature learning improves prediction of human brain activity in response to natural images.

    Directory of Open Access Journals (Sweden)

    Umut Güçlü

    2014-08-01

    Full Text Available Encoding and decoding in functional magnetic resonance imaging has recently emerged as an area of research to noninvasively characterize the relationship between stimulus features and human brain activity. To overcome the challenge of formalizing what stimulus features should modulate single voxel responses, we introduce a general approach for making directly testable predictions of single voxel responses to statistically adapted representations of ecologically valid stimuli. These representations are learned from unlabeled data without supervision. Our approach is validated using a parsimonious computational model of (i how early visual cortical representations are adapted to statistical regularities in natural images and (ii how populations of these representations are pooled by single voxels. This computational model is used to predict single voxel responses to natural images and identify natural images from stimulus-evoked multiple voxel responses. We show that statistically adapted low-level sparse and invariant representations of natural images better span the space of early visual cortical representations and can be more effectively exploited in stimulus identification than hand-designed Gabor wavelets. Our results demonstrate the potential of our approach to better probe unknown cortical representations.

  2. Supervised dictionary learning for inferring concurrent brain networks.

    Science.gov (United States)

    Zhao, Shijie; Han, Junwei; Lv, Jinglei; Jiang, Xi; Hu, Xintao; Zhao, Yu; Ge, Bao; Guo, Lei; Liu, Tianming

    2015-10-01

    Task-based fMRI (tfMRI) has been widely used to explore functional brain networks via predefined stimulus paradigm in the fMRI scan. Traditionally, the general linear model (GLM) has been a dominant approach to detect task-evoked networks. However, GLM focuses on task-evoked or event-evoked brain responses and possibly ignores the intrinsic brain functions. In comparison, dictionary learning and sparse coding methods have attracted much attention recently, and these methods have shown the promise of automatically and systematically decomposing fMRI signals into meaningful task-evoked and intrinsic concurrent networks. Nevertheless, two notable limitations of current data-driven dictionary learning method are that the prior knowledge of task paradigm is not sufficiently utilized and that the establishment of correspondences among dictionary atoms in different brains have been challenging. In this paper, we propose a novel supervised dictionary learning and sparse coding method for inferring functional networks from tfMRI data, which takes both of the advantages of model-driven method and data-driven method. The basic idea is to fix the task stimulus curves as predefined model-driven dictionary atoms and only optimize the other portion of data-driven dictionary atoms. Application of this novel methodology on the publicly available human connectome project (HCP) tfMRI datasets has achieved promising results.

  3. Unsupervised invariance learning of transformation sequences in a model of object recognition yields selectivity for non-accidental properties

    Directory of Open Access Journals (Sweden)

    Thomas eSerre

    2015-10-01

    Full Text Available Non-accidental properties (NAPs correspond to image properties that are invariant to changes in viewpoint (e.g., straight vs. curved contours and are distinguished from metric properties (MPs that can change continuously with in-depth object rotation (e.g., aspect ratio, degree of curvature, etc. Behavioral and electrophysiological studies of shape processing have demonstrated greater sensitivity to differences in NAPs than in MPs. However, previous work has shown that such sensitivity is lacking in multiple-views models of object recognition such as textsc{Hmax}. These models typically assume that object processing is based on populations of view-tuned neurons with distributed symmetrical bell-shaped tuning that are modulated at least as much by differences in MPs as in NAPs.Here, we test the hypothesis that unsupervised learning of invariances to object transformations may increase the sensitivity to differences in NAPs vs. MPs in textsc{Hmax}. We collected a database of video sequences with objects slowly rotating in-depth in an attempt to mimic sequences viewed during object manipulation by young children during early developmental stages. We show that unsupervised learning yields shape-tuning in higher stages with greater sensitivity to differences in NAPs vs. MPs in agreement with monkey IT data. Together, these results suggest that greater NAP sensitivity may arise from experiencing different in-depth rotations of objects.

  4. I’m just thinking - How learning opportunities are created in doctoral supervision

    DEFF Research Database (Denmark)

    Kobayashi, Sofie; Berge, Maria; Grout, Brian William Wilson;

    With this paper we aim to contribute towards an understanding of learning dynamics in doctoral supervision by analysing how learning opportunities are created in the interaction. We analyse interaction between supervisors and doctoral students using the notion of experiencing variation as a key...... for learning. Earlier research into doctoral supervision has been rather vague on how doctoral students learn to carry out research. Empirically, we have based the study on four cases each with one doctoral student and their supervisors. The supervision sessions were captured on video and audio to provide...

  5. Supervised Filter Learning for Representation Based Face Recognition.

    Directory of Open Access Journals (Sweden)

    Chao Bi

    Full Text Available Representation based classification methods, such as Sparse Representation Classification (SRC and Linear Regression Classification (LRC have been developed for face recognition problem successfully. However, most of these methods use the original face images without any preprocessing for recognition. Thus, their performances may be affected by some problematic factors (such as illumination and expression variances in the face images. In order to overcome this limitation, a novel supervised filter learning algorithm is proposed for representation based face recognition in this paper. The underlying idea of our algorithm is to learn a filter so that the within-class representation residuals of the faces' Local Binary Pattern (LBP features are minimized and the between-class representation residuals of the faces' LBP features are maximized. Therefore, the LBP features of filtered face images are more discriminative for representation based classifiers. Furthermore, we also extend our algorithm for heterogeneous face recognition problem. Extensive experiments are carried out on five databases and the experimental results verify the efficacy of the proposed algorithm.

  6. Unsupervised discovery of information structure in biomedical documents.

    Science.gov (United States)

    Kiela, Douwe; Guo, Yufan; Stenius, Ulla; Korhonen, Anna

    2015-04-01

    Information structure (IS) analysis is a text mining technique, which classifies text in biomedical articles into categories that capture different types of information, such as objectives, methods, results and conclusions of research. It is a highly useful technique that can support a range of Biomedical Text Mining tasks and can help readers of biomedical literature find information of interest faster, accelerating the highly time-consuming process of literature review. Several approaches to IS analysis have been presented in the past, with promising results in real-world biomedical tasks. However, all existing approaches, even weakly supervised ones, require several hundreds of hand-annotated training sentences specific to the domain in question. Because biomedicine is subject to considerable domain variation, such annotations are expensive to obtain. This makes the application of IS analysis across biomedical domains difficult. In this article, we investigate an unsupervised approach to IS analysis and evaluate the performance of several unsupervised methods on a large corpus of biomedical abstracts collected from PubMed. Our best unsupervised algorithm (multilevel-weighted graph clustering algorithm) performs very well on the task, obtaining over 0.70 F scores for most IS categories when applied to well-known IS schemes. This level of performance is close to that of lightly supervised IS methods and has proven sufficient to aid a range of practical tasks. Thus, using an unsupervised approach, IS could be applied to support a wide range of tasks across sub-domains of biomedicine. We also demonstrate that unsupervised learning brings novel insights into IS of biomedical literature and discovers information categories that are not present in any of the existing IS schemes. The annotated corpus and software are available at http://www.cl.cam.ac.uk/∼dk427/bio14info.html. © The Author 2014. Published by Oxford University Press. All rights reserved. For

  7. SPAM CLASSIFICATION BASED ON SUPERVISED LEARNING USING MACHINE LEARNING TECHNIQUES

    Directory of Open Access Journals (Sweden)

    T. Hamsapriya

    2011-12-01

    Full Text Available E-mail is one of the most popular and frequently used ways of communication due to its worldwide accessibility, relatively fast message transfer, and low sending cost. The flaws in the e-mail protocols and the increasing amount of electronic business and financial transactions directly contribute to the increase in e-mail-based threats. Email spam is one of the major problems of the today’s Internet, bringing financial damage to companies and annoying individual users. Spam emails are invading users without their consent and filling their mail boxes. They consume more network capacity as well as time in checking and deleting spam mails. The vast majority of Internet users are outspoken in their disdain for spam, although enough of them respond to commercial offers that spam remains a viable source of income to spammers. While most of the users want to do right think to avoid and get rid of spam, they need clear and simple guidelines on how to behave. In spite of all the measures taken to eliminate spam, they are not yet eradicated. Also when the counter measures are over sensitive, even legitimate emails will be eliminated. Among the approaches developed to stop spam, filtering is the one of the most important technique. Many researches in spam filtering have been centered on the more sophisticated classifier-related issues. In recent days, Machine learning for spam classification is an important research issue. The effectiveness of the proposed work is explores and identifies the use of different learning algorithms for classifying spam messages from e-mail. A comparative analysis among the algorithms has also been presented.

  8. Nonlinear system identification by Gustafson-Kessel fuzzy clustering and supervised local model network learning for the drug absorption spectra process.

    Science.gov (United States)

    Teslic, Luka; Hartmann, Benjamin; Nelles, Oliver; Skrjanc, Igor

    2011-12-01

    This paper deals with the problem of fuzzy nonlinear model identification in the framework of a local model network (LMN). A new iterative identification approach is proposed, where supervised and unsupervised learning are combined to optimize the structure of the LMN. For the purpose of fitting the cluster-centers to the process nonlinearity, the Gustafsson-Kessel (GK) fuzzy clustering, i.e., unsupervised learning, is applied. In combination with the LMN learning procedure, a new incremental method to define the number and the initial locations of the cluster centers for the GK clustering algorithm is proposed. Each data cluster corresponds to a local region of the process and is modeled with a local linear model. Since the validity functions are calculated from the fuzzy covariance matrices of the clusters, they are highly adaptable and thus the process can be described with a very sparse amount of local models, i.e., with a parsimonious LMN model. The proposed method for constructing the LMN is finally tested on a drug absorption spectral process and compared to two other methods, namely, Lolimot and Hilomot. The comparison between the experimental results when using each method shows the usefulness of the proposed identification algorithm.

  9. Stacked autoencoders for unsupervised feature learning and multiple organ detection in a pilot study using 4D patient data.

    Science.gov (United States)

    Shin, Hoo-Chang; Orton, Matthew R; Collins, David J; Doran, Simon J; Leach, Martin O

    2013-08-01

    Medical image analysis remains a challenging application area for artificial intelligence. When applying machine learning, obtaining ground-truth labels for supervised learning is more difficult than in many more common applications of machine learning. This is especially so for datasets with abnormalities, as tissue types and the shapes of the organs in these datasets differ widely. However, organ detection in such an abnormal dataset may have many promising potential real-world applications, such as automatic diagnosis, automated radiotherapy planning, and medical image retrieval, where new multimodal medical images provide more information about the imaged tissues for diagnosis. Here, we test the application of deep learning methods to organ identification in magnetic resonance medical images, with visual and temporal hierarchical features learned to categorize object classes from an unlabeled multimodal DCE-MRI dataset so that only a weakly supervised training is required for a classifier. A probabilistic patch-based method was employed for multiple organ detection, with the features learned from the deep learning model. This shows the potential of the deep learning model for application to medical images, despite the difficulty of obtaining libraries of correctly labeled training datasets and despite the intrinsic abnormalities present in patient datasets.

  10. Active semi-supervised learning method with hybrid deep belief networks.

    Science.gov (United States)

    Zhou, Shusen; Chen, Qingcai; Wang, Xiaolong

    2014-01-01

    In this paper, we develop a novel semi-supervised learning algorithm called active hybrid deep belief networks (AHD), to address the semi-supervised sentiment classification problem with deep learning. First, we construct the previous several hidden layers using restricted Boltzmann machines (RBM), which can reduce the dimension and abstract the information of the reviews quickly. Second, we construct the following hidden layers using convolutional restricted Boltzmann machines (CRBM), which can abstract the information of reviews effectively. Third, the constructed deep architecture is fine-tuned by gradient-descent based supervised learning with an exponential loss function. Finally, active learning method is combined based on the proposed deep architecture. We did several experiments on five sentiment classification datasets, and show that AHD is competitive with previous semi-supervised learning algorithm. Experiments are also conducted to verify the effectiveness of our proposed method with different number of labeled reviews and unlabeled reviews respectively.

  11. The Practice of Supervision for Professional Learning: The Example of Future Forensic Specialists

    Science.gov (United States)

    Köpsén, Susanne; Nyström, Sofia

    2015-01-01

    Supervision intended to support learning is of great interest in professional knowledge development. No single definition governs the implementation and enactment of supervision because of different conditions, intentions, and pedagogical approaches. Uncertainty exists at a time when knowledge and methods are undergoing constant development. This…

  12. The Practice of Supervision for Professional Learning: The Example of Future Forensic Specialists

    Science.gov (United States)

    Köpsén, Susanne; Nyström, Sofia

    2015-01-01

    Supervision intended to support learning is of great interest in professional knowledge development. No single definition governs the implementation and enactment of supervision because of different conditions, intentions, and pedagogical approaches. Uncertainty exists at a time when knowledge and methods are undergoing constant development. This…

  13. Automated glioblastoma segmentation based on a multiparametric structured unsupervised classification.

    Science.gov (United States)

    Juan-Albarracín, Javier; Fuster-Garcia, Elies; Manjón, José V; Robles, Montserrat; Aparici, F; Martí-Bonmatí, L; García-Gómez, Juan M

    2015-01-01

    Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation.

  14. Automated glioblastoma segmentation based on a multiparametric structured unsupervised classification.

    Directory of Open Access Journals (Sweden)

    Javier Juan-Albarracín

    Full Text Available Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM, whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF. An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation.

  15. Semi-Supervised Learning for Classification of Protein Sequence Data

    Directory of Open Access Journals (Sweden)

    Brian R. King

    2008-01-01

    Full Text Available Protein sequence data continue to become available at an exponential rate. Annotation of functional and structural attributes of these data lags far behind, with only a small fraction of the data understood and labeled by experimental methods. Classification methods that are based on semi-supervised learning can increase the overall accuracy of classifying partly labeled data in many domains, but very few methods exist that have shown their effect on protein sequence classification. We show how proven methods from text classification can be applied to protein sequence data, as we consider both existing and novel extensions to the basic methods, and demonstrate restrictions and differences that must be considered. We demonstrate comparative results against the transductive support vector machine, and show superior results on the most difficult classification problems. Our results show that large repositories of unlabeled protein sequence data can indeed be used to improve predictive performance, particularly in situations where there are fewer labeled protein sequences available, and/or the data are highly unbalanced in nature.

  16. Unsupervised learning applied in MER and ECG signals through Gaussians mixtures with the Expectation-Maximization algorithm and Variational Bayesian Inference.

    Science.gov (United States)

    Vargas Cardona, Hernán Darío; Orozco, Álvaro Ángel; Álvarez, Mauricio A

    2013-01-01

    Automatic identification of biosignals is one of the more studied fields in biomedical engineering. In this paper, we present an approach for the unsupervised recognition of biomedical signals: Microelectrode Recordings (MER) and Electrocardiography signals (ECG). The unsupervised learning is based in classic and bayesian estimation theory. We employ gaussian mixtures models with two estimation methods. The first is derived from the frequentist estimation theory, known as Expectation-Maximization (EM) algorithm. The second is obtained from bayesian probabilistic estimation and it is called variational inference. In this framework, both methods are used for parameters estimation of Gaussian mixtures. The mixtures models are used for unsupervised pattern classification, through the responsibility matrix. The algorithms are applied in two real databases acquired in Parkinson's disease surgeries and electrocardiograms. The results show an accuracy over 85% in MER and 90% in ECG for identification of two classes. These results are statistically equal or even better than parametric (Naive Bayes) and nonparametric classifiers (K-nearest neighbor).

  17. Unsupervised Quick Reduct Algorithm Using Rough Set Theory

    Institute of Scientific and Technical Information of China (English)

    C. Velayutham; K. Thangavel

    2011-01-01

    Feature selection (FS) is a process to select features which are more informative.It is one of the important steps in knowledge discovery.The problem is that not all features are important.Some of the features may be redundant,and others may be irrelevant and noisy.The conventional supervised FS methods evaluate various feature subsets using an evaluation function or metric to select only those features which are related to the decision classes of the data under consideration.However,for many data mining applications,decision class labels are often unknown or incomplete,thus indicating the significance of unsupervised feature selection.However,in unsupervised learning,decision class labels are not provided.In this paper,we propose a new unsupervised quick reduct (QR) algorithm using rough set theory.The quality of the reduced data is measured by the classification performance and it is evaluated using WEKA classifier tool.The method is compared with existing supervised methods and the result demonstrates the efficiency of the proposed algorithm.

  18. Combining theories to reach multi-faceted insights into learning opportunities in doctoral supervision

    DEFF Research Database (Denmark)

    Kobayashi, Sofie; Rump, Camilla Østerberg

    The aim of this paper is to illustrate how theories can be combined to explore opportunities for learning in doctoral supervision. While our earlier research into learning dynamics in doctoral supervision in life science research (Kobayashi, 2014) has focused on illustrating learning opportunities...... this paper focuses on the methodological advantages and potential criticism of combining theories. Learning in doctoral education, as in classroom learning, can be analysed from different perspectives. Zembylas (2005) suggests three perspectives with the aim of linking the cognitive and the emotional...

  19. Semi-Supervised Learning Techniques in AO Applications: A Novel Approach To Drift Counteraction

    Science.gov (United States)

    De Vito, S.; Fattoruso, G.; Pardo, M.; Tortorella, F.; Di Francia, G.

    2011-11-01

    In this work we proposed and tested the use of SSL techniques in the AO domain. The SSL characteristics have been exploited to reduce the need for costly supervised samples and the effects of time dependant drift of state-of-the-art statistical learning approaches. For this purpose, an on-field recorded one year long atmospheric pollution dataset has been used. The semi-supervised approach benefitted from the use of updated unlabeled samples, adapting its knowledge to the slowly changing drift effects. We expect that semi-supervised learning can provide significant advantages to the performance of sensor fusion subsystems in artificial olfaction exhibiting an interesting drift counteraction effect.

  20. Rapid analysis of microbial systems using vibrational spectroscopy and supervised learning methods: application to the discrimination between methicillin-resistant and methicillin-susceptible Staphy

    Science.gov (United States)

    Goodacre, Royston; Rooney, Paul J.; Kell, Douglas B.

    1998-04-01

    FTIR spectra were obtained from 15 methicillin-resistant and 22 methicillin-susceptible Staphylococcus aureus strains using our DRASTIC approach. Cluster analysis showed that the major source of variation between the IR spectra was not due to their resistance or susceptibility to methicillin; indeed early studies suing pyrolysis mass spectrometry had shown that this unsupervised analysis gave information on the phage group of the bacteria. By contrast, artificial neural networks, based on a supervised learning, could be trained to recognize those aspects of the IR spectra which differentiated methicillin-resistant from methicillin- susceptible strains. These results give the first demonstration that the combination of FTIR with neural networks can provide a very rapid and accurate antibiotic susceptibility testing technique.

  1. Combining theories to reach multi-faceted insights into learning opportunities in doctoral supervision

    DEFF Research Database (Denmark)

    Kobayashi, Sofie; Rump, Camilla Østerberg

    in science learning; conceptual change, socio-constructivism and post-structuralism. In the present study we employ variation theory (Marton & Tsui, 2004) to study the individual acquisition perspective, what Zembylas terms conceptual change. As for the post-structural perspective we employ positioning......The aim of this paper is to illustrate how theories can be combined to explore opportunities for learning in doctoral supervision. While our earlier research into learning dynamics in doctoral supervision in life science research (Kobayashi, 2014) has focused on illustrating learning opportunities......-another when intertwining the analyses to get a multi-faceted insight into the phenomenon of learning to be a life science researcher. The data was derived from four observations of supervision of doctoral students in life science, each with a doctoral student and two supervisors. The storylines hypothesized...

  2. Unsupervised Language Acquisition

    CERN Document Server

    De Marcken, C

    1996-01-01

    This thesis presents a computational theory of unsupervised language acquisition, precisely defining procedures for learning language from ordinary spoken or written utterances, with no explicit help from a teacher. The theory is based heavily on concepts borrowed from machine learning and statistical estimation. In particular, learning takes place by fitting a stochastic, generative model of language to the evidence. Much of the thesis is devoted to explaining conditions that must hold for this general learning strategy to arrive at linguistically desirable grammars. The thesis introduces a variety of technical innovations, among them a common representation for evidence and grammars, and a learning strategy that separates the ``content'' of linguistic parameters from their representation. Algorithms based on it suffer from few of the search problems that have plagued other computational approaches to language acquisition. The theory has been tested on problems of learning vocabularies and grammars from unse...

  3. Integrative gene network construction to analyze cancer recurrence using semi-supervised learning.

    Directory of Open Access Journals (Sweden)

    Chihyun Park

    Full Text Available BACKGROUND: The prognosis of cancer recurrence is an important research area in bioinformatics and is challenging due to the small sample sizes compared to the vast number of genes. There have been several attempts to predict cancer recurrence. Most studies employed a supervised approach, which uses only a few labeled samples. Semi-supervised learning can be a great alternative to solve this problem. There have been few attempts based on manifold assumptions to reveal the detailed roles of identified cancer genes in recurrence. RESULTS: In order to predict cancer recurrence, we proposed a novel semi-supervised learning algorithm based on a graph regularization approach. We transformed the gene expression data into a graph structure for semi-supervised learning and integrated protein interaction data with the gene expression data to select functionally-related gene pairs. Then, we predicted the recurrence of cancer by applying a regularization approach to the constructed graph containing both labeled and unlabeled nodes. CONCLUSIONS: The average improvement rate of accuracy for three different cancer datasets was 24.9% compared to existing supervised and semi-supervised methods. We performed functional enrichment on the gene networks used for learning. We identified that those gene networks are significantly associated with cancer-recurrence-related biological functions. Our algorithm was developed with standard C++ and is available in Linux and MS Windows formats in the STL library. The executable program is freely available at: http://embio.yonsei.ac.kr/~Park/ssl.php.

  4. Spectrum Hole Identification in IEEE 802.22 WRAN using Unsupervised Learning

    Directory of Open Access Journals (Sweden)

    V. Balaji

    2016-01-01

    Full Text Available In this paper we present a Cooperative Spectrum Sensing (CSS algorithm for Cognitive Radios (CR based on IEEE 802.22Wireless Regional Area Network (WRAN standard. The core objective is to improve cooperative sensing efficiency which specifies how fast a decision can be reached in each round of cooperation (iteration to sense an appropriate number of channels/bands (i.e. 86 channels of 7MHz bandwidth as per IEEE 802.22 within a time constraint (channel sensing time. To meet this objective, we have developed CSS algorithm using unsupervised K-means clustering classification approach. The received energy level of each Secondary User (SU is considered as the parameter for determining channel availability. The performance of proposed algorithm is quantified in terms of detection accuracy, training and classification delay time. Further, the detection accuracy of our proposed scheme meets the requirement of IEEE 802.22 WRAN with the target probability of falsealrm as 0.1. All the simulations are carried out using Matlab tool.

  5. An efficient flow-based botnet detection using supervised machine learning

    DEFF Research Database (Denmark)

    Stevanovic, Matija; Pedersen, Jens Myrup

    2014-01-01

    Botnet detection represents one of the most crucial prerequisites of successful botnet neutralization. This paper explores how accurate and timely detection can be achieved by using supervised machine learning as the tool of inferring about malicious botnet traffic. In order to do so, the paper...... introduces a novel flow-based detection system that relies on supervised machine learning for identifying botnet network traffic. For use in the system we consider eight highly regarded machine learning algorithms, indicating the best performing one. Furthermore, the paper evaluates how much traffic needs...... to accurately and timely detect botnet traffic using purely flow-based traffic analysis and supervised machine learning. Additionally, the results show that in order to achieve accurate detection traffic flows need to be monitored for only a limited time period and number of packets per flow. This indicates...

  6. Modeling Multiple Annotator Expertise in the Semi-Supervised Learning Scenario

    CERN Document Server

    Yan, Yan; Fung, Glenn; Dy, Jennifer

    2012-01-01

    Learning algorithms normally assume that there is at most one annotation or label per data point. However, in some scenarios, such as medical diagnosis and on-line collaboration,multiple annotations may be available. In either case, obtaining labels for data points can be expensive and time-consuming (in some circumstances ground-truth may not exist). Semi-supervised learning approaches have shown that utilizing the unlabeled data is often beneficial in these cases. This paper presents a probabilistic semi-supervised model and algorithm that allows for learning from both unlabeled and labeled data in the presence of multiple annotators. We assume that it is known what annotator labeled which data points. The proposed approach produces annotator models that allow us to provide (1) estimates of the true label and (2) annotator variable expertise for both labeled and unlabeled data. We provide numerical comparisons under various scenarios and with respect to standard semi-supervised learning. Experiments showed ...

  7. Multiclass Semi-Supervised Boosting and Similarity Learning

    NARCIS (Netherlands)

    Tanha, J.; Saberian, M.J.; van Someren, M.; Xiong, H.; Karypis, G.; Thuraisingham, B.; Cook, D.; Wu, X.

    2013-01-01

    In this paper, we consider the multiclass semi-supervised classification problem. A boosting algorithm is proposed to solve the multiclass problem directly. The proposed multiclass approach uses a new multiclass loss function, which includes two terms. The first term is the cost of the multiclass ma

  8. Learning to Teach: Teaching Internships in Counselor Education and Supervision

    Science.gov (United States)

    Hunt, Brandon; Gilmore, Genevieve Weber

    2011-01-01

    In an effort to ensure the efficacy of preparing emerging counselors in the field, CACREP standards require that by 2013 all core faculty at accredited universities have a doctorate in Counselor Education and Supervision. However, literature suggests that a disparity may exist in the preparation of counselor educators and the actual…

  9. Predicting incomplete gene microarray data with the use of supervised learning algorithms

    CSIR Research Space (South Africa)

    Twala, B

    2010-10-01

    Full Text Available of many well-established supervised learning (SL) algorithms in an attempt to provide more accurate and automatic diagnosis class (cancer/non cancer) prediction. Virtually all research on SL addresses the task of learning to classify complete domain...

  10. Can Elementary School Students Be Taught Touchtyping in Unsupervised Environments?

    Science.gov (United States)

    Mikkelsen, Vincent P.; And Others

    1989-01-01

    Investigates the effect of microcomputer typing tutorials on students' typing speed. Finds that the typing tutorial was effective regardless of the environment (supervised or unsupervised), grade level, gender, or prior typing experience. (RS)

  11. Unsupervised, Efficient and Semantic Expertise Retrieval

    NARCIS (Netherlands)

    Van Gysel, C.; de Rijke, M.; Worring, M.

    2016-01-01

    We introduce an unsupervised discriminative model for the task of retrieving experts in online document collections. We exclusively employ textual evidence and avoid explicit feature engineering by learning distributed word representations in an unsupervised way. We compare our model to state-of-the

  12. Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring

    DEFF Research Database (Denmark)

    Kallenberg, Michiel Gijsbertus J.; Petersen, Peter Kersten; Nielsen, Mads

    2016-01-01

    Mammographic risk scoring has commonly been automated by extracting a set of handcrafted features from mammograms, and relating the responses directly or indirectly to breast cancer risk. We present a method that learns a feature hierarchy from unlabeled data. When the learned features are used...... as the input to a simple classifier, two different tasks can be addressed: i) breast density segmentation, and ii) scoring of mammographic texture. The proposed model learns features at multiple scales. To control the models capacity a novel sparsity regularizer is introduced that incorporates both lifetime...... and population sparsity. We evaluated our method on three different clinical datasets. Our state-of-the-art results show that the learned breast density scores have a very strong positive relationship with manual ones, and that the learned texture scores are predictive of breast cancer. The model is easy...

  13. Data integration modeling applied to drill hole planning through semi-supervised learning: A case study from the Dalli Cu-Au porphyry deposit in the central Iran

    Science.gov (United States)

    Fatehi, Moslem; Asadi, Hooshang H.

    2017-04-01

    In this study, the application of a transductive support vector machine (TSVM), an innovative semi-supervised learning algorithm, has been proposed for mapping the potential drill targets at a detailed exploration stage. The semi-supervised learning method is a hybrid of supervised and unsupervised learning approach that simultaneously uses both training and non-training data to design a classifier. By using the TSVM algorithm, exploration layers at the Dalli porphyry Cu-Au deposit in the central Iran were integrated to locate the boundary of the Cu-Au mineralization for further drilling. By applying this algorithm on the non-training (unlabeled) and limited training (labeled) Dalli exploration data, the study area was classified in two domains of Cu-Au ore and waste. Then, the results were validated by the earlier block models created, using the available borehole and trench data. In addition to TSVM, the support vector machine (SVM) algorithm was also implemented on the study area for comparison. Thirty percent of the labeled exploration data was used to evaluate the performance of these two algorithms. The results revealed 87 percent correct recognition accuracy for the TSVM algorithm and 82 percent for the SVM algorithm. The deepest inclined borehole, recently drilled in the western part of the Dalli deposit, indicated that the boundary of Cu-Au mineralization, as identified by the TSVM algorithm, was only 15 m off from the actual boundary intersected by this borehole. According to the results of the TSVM algorithm, six new boreholes were suggested for further drilling at the Dalli deposit. This study showed that the TSVM algorithm could be a useful tool for enhancing the mineralization zones and consequently, ensuring a more accurate drill hole planning.

  14. Semi-supervised eigenvectors for large-scale locally-biased learning

    DEFF Research Database (Denmark)

    Hansen, Toke Jansen; Mahoney, Michael W.

    2014-01-01

    -based machine learning and data analysis tools. At root, the reason is that eigenvectors are inherently global quantities, thus limiting the applicability of eigenvector-based methods in situations where one is interested in very local properties of the data. In this paper, we address this issue by providing......In many applications, one has side information, e.g., labels that are provided in a semi-supervised manner, about a specific target region of a large data set, and one wants to perform machine learning and data analysis tasks nearby that prespecified target region. For example, one might...... a methodology to construct semi-supervised eigenvectors of a graph Laplacian, and we illustrate how these locally-biased eigenvectors can be used to perform locally-biased machine learning. These semi-supervised eigenvectors capture successively-orthogonalized directions of maximum variance, conditioned...

  15. Customers Behavior Modeling by Semi-Supervised Learning in Customer Relationship Management

    CERN Document Server

    Emtiyaz, Siavash; 10.4156/AISS.vol3.issue9.31

    2012-01-01

    Leveraging the power of increasing amounts of data to analyze customer base for attracting and retaining the most valuable customers is a major problem facing companies in this information age. Data mining technologies extract hidden information and knowledge from large data stored in databases or data warehouses, thereby supporting the corporate decision making process. CRM uses data mining (one of the elements of CRM) techniques to interact with customers. This study investigates the use of a technique, semi-supervised learning, for the management and analysis of customer-related data warehouse and information. The idea of semi-supervised learning is to learn not only from the labeled training data, but to exploit also the structural information in additionally available unlabeled data. The proposed semi-supervised method is a model by means of a feed-forward neural network trained by a back propagation algorithm (multi-layer perceptron) in order to predict the category of an unknown customer (potential cus...

  16. The efficacy of early initiated, supervised, progressive resistance training compared to unsupervised, home-based exercise after unicompartmental knee arthroplasty: a single-blinded randomized controlled trial.

    Science.gov (United States)

    Jørgensen, Peter B; Bogh, Søren B; Kierkegaard, Signe; Sørensen, Henrik; Odgaard, Anders; Søballe, Kjeld; Mechlenburg, Inger

    2017-01-01

    To examine if supervised progressive resistance training was superior to home-based exercise in rehabilitation after unicompartmental knee arthroplasty. Single blinded, randomized clinical trial. Surgery, progressive resistance training and testing was carried out at Aarhus University Hospital and home-based exercise was carried out in the home of the patient. Fifty five patients were randomized to either progressive resistance training or home-based exercise. Patients were randomized to either progressive resistance training (home based exercise five days/week and progressive resistance training two days/week) or control group (home based exercise seven days/week). Preoperative assessment, 10-week (primary endpoint) and one-year follow-up were performed for leg extension power, spatiotemporal gait parameters and knee injury and osteoarthritis outcome score (KOOS). Forty patients (73%) completed 1-year follow-up. Patients in the progressive resistance training group participated in average 11 of 16 training sessions. Leg extension power increased from baseline to 10-week follow-up in progressive resistance training group (progressive resistance training: 0.28 W/kg, P= 0.01, control group: 0.01 W/kg, P=0.93) with no between-group difference. Walking speed and KOOS scores increased from baseline to 10-week follow-up in both groups with no between-group difference (six minutes walk test P=0.63, KOOS P>0.29). Progressive resistance training two days/week combined with home based exercise five days/week was not superior to home based exercise seven days/week in improving leg extension power of the operated leg.

  17. An unsupervised learning algorithm: application to the discrimination of seismic events and quarry blasts in the vicinity of Istanbul

    Directory of Open Access Journals (Sweden)

    H. S. Kuyuk

    2011-01-01

    Full Text Available The results of the application of an unsupervised learning (neural network approach comprising a Self Organizing Map (SOM, to distinguish micro-earthquakes from quarry blasts in the vicinity of Istanbul, Turkey, are presented and discussed. The SOM is constructed as a neural classifier and complementary reliability estimator to distinguish seismic events, and was employed for varying map sizes. Input parameters consisting of frequency and time domain data (complexity, spectral ratio, S/P wave amplitude peak ratio and origin time of events extracted from the vertical components of digital seismograms were estimated as discriminants for 179 (1.8 < Md < 3.0 local events. The results show that complexity and amplitude peak ratio parameters of the observed velocity seismogram may suffice for a reliable discrimination, while origin time and spectral ratio were found to be fuzzy and misleading classifiers for this problem. The SOM discussed here achieved a discrimination reliability that could be employed routinely in observatory practice; however, about 6% of all events were classified as ambiguous cases. This approach was developed independently for this particular classification, but it could be applied to different earthquake regions.

  18. Determining effects of non-synonymous SNPs on protein-protein interactions using supervised and semi-supervised learning.

    Directory of Open Access Journals (Sweden)

    Nan Zhao

    2014-05-01

    Full Text Available Single nucleotide polymorphisms (SNPs are among the most common types of genetic variation in complex genetic disorders. A growing number of studies link the functional role of SNPs with the networks and pathways mediated by the disease-associated genes. For example, many non-synonymous missense SNPs (nsSNPs have been found near or inside the protein-protein interaction (PPI interfaces. Determining whether such nsSNP will disrupt or preserve a PPI is a challenging task to address, both experimentally and computationally. Here, we present this task as three related classification problems, and develop a new computational method, called the SNP-IN tool (non-synonymous SNP INteraction effect predictor. Our method predicts the effects of nsSNPs on PPIs, given the interaction's structure. It leverages supervised and semi-supervised feature-based classifiers, including our new Random Forest self-learning protocol. The classifiers are trained based on a dataset of comprehensive mutagenesis studies for 151 PPI complexes, with experimentally determined binding affinities of the mutant and wild-type interactions. Three classification problems were considered: (1 a 2-class problem (strengthening/weakening PPI mutations, (2 another 2-class problem (mutations that disrupt/preserve a PPI, and (3 a 3-class classification (detrimental/neutral/beneficial mutation effects. In total, 11 different supervised and semi-supervised classifiers were trained and assessed resulting in a promising performance, with the weighted f-measure ranging from 0.87 for Problem 1 to 0.70 for the most challenging Problem 3. By integrating prediction results of the 2-class classifiers into the 3-class classifier, we further improved its performance for Problem 3. To demonstrate the utility of SNP-IN tool, it was applied to study the nsSNP-induced rewiring of two disease-centered networks. The accurate and balanced performance of SNP-IN tool makes it readily available to study the

  19. Semi-supervised learning and domain adaptation in natural language processing

    CERN Document Server

    Søgaard, Anders

    2013-01-01

    This book introduces basic supervised learning algorithms applicable to natural language processing (NLP) and shows how the performance of these algorithms can often be improved by exploiting the marginal distribution of large amounts of unlabeled data. One reason for that is data sparsity, i.e., the limited amounts of data we have available in NLP. However, in most real-world NLP applications our labeled data is also heavily biased. This book introduces extensions of supervised learning algorithms to cope with data sparsity and different kinds of sampling bias.This book is intended to be both

  20. Supervised Learning of Logical Operations in Layered Spiking Neural Networks with Spike Train Encoding

    CERN Document Server

    Grüning, André

    2011-01-01

    Few algorithms for supervised training of spiking neural networks exist that can deal with patterns of multiple spikes, and their computational properties are largely unexplored. We demonstrate in a set of simulations that the ReSuMe learning algorithm can be successfully applied to layered neural networks. Input and output patterns are encoded as spike trains of multiple precisely timed spikes, and the network learns to transform the input trains into target output trains. This is done by combining the ReSuMe learning algorithm with multiplicative scaling of the connections of downstream neurons. We show in particular that layered networks with one hidden layer can learn the basic logical operations, including Exclusive-Or, while networks without hidden layer cannot, mirroring an analogous result for layered networks of rate neurons. While supervised learning in spiking neural networks is not yet fit for technical purposes, exploring computational properties of spiking neural networks advances our understand...

  1. Out-of-Sample Generalizations for Supervised Manifold Learning for Classification

    Science.gov (United States)

    Vural, Elif; Guillemot, Christine

    2016-03-01

    Supervised manifold learning methods for data classification map data samples residing in a high-dimensional ambient space to a lower-dimensional domain in a structure-preserving way, while enhancing the separation between different classes in the learned embedding. Most nonlinear supervised manifold learning methods compute the embedding of the manifolds only at the initially available training points, while the generalization of the embedding to novel points, known as the out-of-sample extension problem in manifold learning, becomes especially important in classification applications. In this work, we propose a semi-supervised method for building an interpolation function that provides an out-of-sample extension for general supervised manifold learning algorithms studied in the context of classification. The proposed algorithm computes a radial basis function (RBF) interpolator that minimizes an objective function consisting of the total embedding error of unlabeled test samples, defined as their distance to the embeddings of the manifolds of their own class, as well as a regularization term that controls the smoothness of the interpolation function in a direction-dependent way. The class labels of test data and the interpolation function parameters are estimated jointly with a progressive procedure. Experimental results on face and object images demonstrate the potential of the proposed out-of-sample extension algorithm for the classification of manifold-modeled data sets.

  2. Unsupervised learning of reflexive and action-based affordances to model adaptive navigational behavior

    Directory of Open Access Journals (Sweden)

    Daniel Weiller

    2010-05-01

    Full Text Available Here we introduce a cognitive model capable to model a variety of behavioral domains and apply it to a navigational task. We used place cells as sensory representation, such that the cells’ place fields divided the environment into discrete states. The robot learns knowledge of the environment by memorizing the sensory outcome of its motor actions. This is composed of a central process, learning the probability of state-to-state transitions by motor actions and a distal processing routine, learning the extent to which these state-to-state transitions are caused by sensory-driven reflex behavior (obstacle avoidance. Navigational decision making integrates central and distal learned environmental knowledge to select an action that leads to a goal state. Differentiating distal and central processing increases the behavioral accuracy of the selected actions and the ability of behavioral adaptation to a changed environment. We propose that the system can canonically be expanded to model other behaviors, using alternative definitions of states and actions. The emphasis of this paper is to test this general cognitive model on a robot in a real world environment

  3. Multiresolutional schemata for unsupervised learning of autonomous robots for 3D space operation

    Science.gov (United States)

    Lacaze, Alberto; Meystel, Michael; Meystel, Alex

    1994-01-01

    This paper describes a novel approach to the development of a learning control system for autonomous space robot (ASR) which presents the ASR as a 'baby' -- that is, a system with no a priori knowledge of the world in which it operates, but with behavior acquisition techniques that allows it to build this knowledge from the experiences of actions within a particular environment (we will call it an Astro-baby). The learning techniques are rooted in the recursive algorithm for inductive generation of nested schemata molded from processes of early cognitive development in humans. The algorithm extracts data from the environment and by means of correlation and abduction, it creates schemata that are used for control. This system is robust enough to deal with a constantly changing environment because such changes provoke the creation of new schemata by generalizing from experiences, while still maintaining minimal computational complexity, thanks to the system's multiresolutional nature.

  4. Real time unsupervised learning of visual stimuli in neuromorphic VLSI systems

    Science.gov (United States)

    Giulioni, Massimiliano; Corradi, Federico; Dante, Vittorio; Del Giudice, Paolo

    2015-10-01

    Neuromorphic chips embody computational principles operating in the nervous system, into microelectronic devices. In this domain it is important to identify computational primitives that theory and experiments suggest as generic and reusable cognitive elements. One such element is provided by attractor dynamics in recurrent networks. Point attractors are equilibrium states of the dynamics (up to fluctuations), determined by the synaptic structure of the network; a ‘basin’ of attraction comprises all initial states leading to a given attractor upon relaxation, hence making attractor dynamics suitable to implement robust associative memory. The initial network state is dictated by the stimulus, and relaxation to the attractor state implements the retrieval of the corresponding memorized prototypical pattern. In a previous work we demonstrated that a neuromorphic recurrent network of spiking neurons and suitably chosen, fixed synapses supports attractor dynamics. Here we focus on learning: activating on-chip synaptic plasticity and using a theory-driven strategy for choosing network parameters, we show that autonomous learning, following repeated presentation of simple visual stimuli, shapes a synaptic connectivity supporting stimulus-selective attractors. Associative memory develops on chip as the result of the coupled stimulus-driven neural activity and ensuing synaptic dynamics, with no artificial separation between learning and retrieval phases.

  5. Identification and tracking of vertebrae in ultrasound using deep networks with unsupervised feature learning

    Science.gov (United States)

    Hetherington, Jorden; Pesteie, Mehran; Lessoway, Victoria A.; Abolmaesumi, Purang; Rohling, Robert N.

    2017-03-01

    Percutaneous needle insertion procedures on the spine often require proper identification of the vertebral level in order to effectively deliver anesthetics and analgesic agents to achieve adequate block. For example, in obstetric epidurals, the target is at the L3-L4 intervertebral space. The current clinical method involves "blind" identification of the vertebral level through manual palpation of the spine, which has only 30% accuracy. This implies the need for better anatomical identification prior to needle insertion. A system is proposed to identify the vertebrae, assigning them to their respective levels, and track them in a standard sequence of ultrasound images, when imaged in the paramedian plane. Machine learning techniques are developed to identify discriminative features of the laminae. In particular, a deep network is trained to automatically learn the anatomical features of the lamina peaks, and classify image patches, for pixel-level classification. The chosen network utilizes multiple connected auto-encoders to learn the anatomy. Pre-processing with ultrasound bone enhancement techniques is done to aid the pixel-level classification performance. Once the lamina are identified, vertebrae are assigned levels and tracked in sequential frames. Experimental results were evaluated against an expert sonographer. Based on data acquired from 15 subjects, vertebrae identification with sensitivity of 95% and precision of 95% was achieved within each frame. Between pairs of subsequently analyzed frames, matches of predicted vertebral level labels were correct in 94% of cases, when compared to matches of manually selected labels

  6. Supervised learning with decision tree-based methods in computational and systems biology.

    Science.gov (United States)

    Geurts, Pierre; Irrthum, Alexandre; Wehenkel, Louis

    2009-12-01

    At the intersection between artificial intelligence and statistics, supervised learning allows algorithms to automatically build predictive models from just observations of a system. During the last twenty years, supervised learning has been a tool of choice to analyze the always increasing and complexifying data generated in the context of molecular biology, with successful applications in genome annotation, function prediction, or biomarker discovery. Among supervised learning methods, decision tree-based methods stand out as non parametric methods that have the unique feature of combining interpretability, efficiency, and, when used in ensembles of trees, excellent accuracy. The goal of this paper is to provide an accessible and comprehensive introduction to this class of methods. The first part of the review is devoted to an intuitive but complete description of decision tree-based methods and a discussion of their strengths and limitations with respect to other supervised learning methods. The second part of the review provides a survey of their applications in the context of computational and systems biology.

  7. Towards designing an email classification system using multi-view based semi-supervised learning

    NARCIS (Netherlands)

    Li, Wenjuan; Meng, Weizhi; Tan, Zhiyuan; Xiang, Yang

    2014-01-01

    The goal of email classification is to classify user emails into spam and legitimate ones. Many supervised learning algorithms have been invented in this domain to accomplish the task, and these algorithms require a large number of labeled training data. However, data labeling is a labor intensive t

  8. Re/Learning Student Teaching Supervision: A Co/Autoethnographic Self-Study

    Science.gov (United States)

    Butler, Brandon M.; Diacopoulos, Mark M.

    2016-01-01

    This article documents the critical friendship of an experienced teacher educator and a doctoral student through our joint exploration of student teaching supervision. By adopting a co/autoethnographic approach, we learned from biographical and contemporaneous critical incidents that informed short- and long-term practices. In particular, we…

  9. Undergraduate Internship Supervision in Psychology Departments: Use of Experiential Learning Best Practices

    Science.gov (United States)

    Bailey, Sarah F.; Barber, Larissa K.; Nelson, Videl L.

    2017-01-01

    This study examined trends in how psychology internships are supervised compared to current experiential learning best practices in the literature. We sent a brief online survey to relevant contact persons for colleges/universities with psychology departments throughout the United States (n = 149 responded). Overall, the majority of institutions…

  10. Multiclass semi-supervised learning for animal behavior recognition from accelerometer data

    NARCIS (Netherlands)

    Tanha, J.; van Someren, M.; de Bakker, M.; Bouten, W.; Shamoun-Baranes, J.; Afsarmanesh, H.

    2012-01-01

    In this paper we present a new Multiclass semi-supervised learning algorithm that uses a base classifier in combination with a similarity function applied to all data to find a classifier that maximizes the margin and consistency over all data. A novel multiclass loss function is presented and used

  11. Social media research: The application of supervised machine learning in organizational communication research

    NARCIS (Netherlands)

    van Zoonen, W.; van der Meer, T.G.L.A.

    2016-01-01

    Despite the online availability of data, analysis of this information in academic research is arduous. This article explores the application of supervised machine learning (SML) to overcome challenges associated with online data analysis. In SML classifiers are used to categorize and code binary dat

  12. Unsupervised Linear Discriminant Analysis

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    An algorithm for unsupervised linear discriminant analysis was presented. Optimal unsupervised discriminant vectors are obtained through maximizing covariance of all samples and minimizing covariance of local k-nearest neighbor samples. The experimental results show our algorithm is effective.

  13. Supervised learning for neural manifold using spatiotemporal brain activity

    Science.gov (United States)

    Kuo, Po-Chih; Chen, Yong-Sheng; Chen, Li-Fen

    2015-12-01

    Objective. Determining the means by which perceived stimuli are compactly represented in the human brain is a difficult task. This study aimed to develop techniques for the construction of the neural manifold as a representation of visual stimuli. Approach. We propose a supervised locally linear embedding method to construct the embedded manifold from brain activity, taking into account similarities between corresponding stimuli. In our experiments, photographic portraits were used as visual stimuli and brain activity was calculated from magnetoencephalographic data using a source localization method. Main results. The results of 10 × 10-fold cross-validation revealed a strong correlation between manifolds of brain activity and the orientation of faces in the presented images, suggesting that high-level information related to image content can be revealed in the brain responses represented in the manifold. Significance. Our experiments demonstrate that the proposed method is applicable to investigation into the inherent patterns of brain activity.

  14. 一种用于半监督学习的核优化设计%A Kernel Optimization Design for Semi-supervised Learning

    Institute of Scientific and Technical Information of China (English)

    崔鹏

    2013-01-01

    Semi-supervised learning aims to obtain good performance and learning ability under lacking of some information on training examples.We proposed a semi-supervised learning framework based on optimizing kernel,which project data into high dimensional feature space and equal to linear classification.In kernel design,we applied spectral feature decomposition to unsupervised kernel design,and found optimal kernel by minimizing learning bound.With experimental results,we demonstrated our theory by comparison of different kernel approaches.%半监督学习研究主要关注当训练数据的部分信息缺失的情况下,如何获得具有良好性能和推广能力的学习机器。本文我们提出了一种基于核优化的半监督学习框架,将数据嵌入到高维特征空间,从而与线性分类器等价。在核的设计上,采用了基于谱分解的无监督核设计,提出了学习边界,通过最小化边界来获得最优核表示。通过实验,对不同的核方法进行了比较,证明了我们结论的正确性。

  15. Unsupervised Discovery of El Nino Using Causal Feature Learning on Microlevel Climate Data

    CERN Document Server

    Chalupka, Krzysztof; Perona, Pietro; Eberhardt, Frederick

    2016-01-01

    We show that the climate phenomena of El Nino and La Nina arise naturally as states of macro-variables when our recent causal feature learning framework (Chalupka 2015, Chalupka 2016) is applied to micro-level measures of zonal wind (ZW) and sea surface temperatures (SST) taken over the equatorial band of the Pacific Ocean. The method identifies these unusual climate states on the basis of the relation between ZW and SST patterns without any input about past occurrences of El Nino or La Nina. The simpler alternatives of (i) clustering the SST fields while disregarding their relationship with ZW patterns, or (ii) clustering the joint ZW-SST patterns, do not discover El Nino. We discuss the degree to which our method supports a causal interpretation and use a low-dimensional toy example to explain its success over other clustering approaches. Finally, we propose a new robust and scalable alternative to our original algorithm (Chalupka 2016), which circumvents the need for high-dimensional density learning.

  16. Cost-conscious comparison of supervised learning algorithms over multiple data sets

    OpenAIRE

    Ulaş, Aydın; Yıldız, Olcay Taner; Alpaydın, Ahmet İbrahim Ethem

    2012-01-01

    In the literature, there exist statistical tests to compare supervised learning algorithms on multiple data sets in terms of accuracy but they do not always generate an ordering. We propose Multi(2)Test, a generalization of our previous work, for ordering multiple learning algorithms on multiple data sets from "best" to "worst" where our goodness measure is composed of a prior cost term additional to generalization error. Our simulations show that Multi2Test generates orderings using pairwise...

  17. Supervised hub-detection for brain connectivity

    Science.gov (United States)

    Kasenburg, Niklas; Liptrot, Matthew; Reislev, Nina Linde; Garde, Ellen; Nielsen, Mads; Feragen, Aasa

    2016-03-01

    A structural brain network consists of physical connections between brain regions. Brain network analysis aims to find features associated with a parameter of interest through supervised prediction models such as regression. Unsupervised preprocessing steps like clustering are often applied, but can smooth discriminative signals in the population, degrading predictive performance. We present a novel hub-detection optimized for supervised learning that both clusters network nodes based on population level variation in connectivity and also takes the learning problem into account. The found hubs are a low-dimensional representation of the network and are chosen based on predictive performance as features for a linear regression. We apply our method to the problem of finding age-related changes in structural connectivity. We compare our supervised hub-detection (SHD) to an unsupervised hub-detection and a linear regression using the original network connections as features. The results show that the SHD is able to retain regression performance, while still finding hubs that represent the underlying variation in the population. Although here we applied the SHD to brain networks, it can be applied to any network regression problem. Further development of the presented algorithm will be the extension to other predictive models such as classification or non-linear regression.

  18. Don't 'have a clue'? Unsupervised co-learning of downward-entailing operators

    CERN Document Server

    Danescu-Niculescu-Mizil, Cristian

    2010-01-01

    Researchers in textual entailment have begun to consider inferences involving 'downward-entailing operators', an interesting and important class of lexical items that change the way inferences are made. Recent work proposed a method for learning English downward-entailing operators that requires access to a high-quality collection of 'negative polarity items' (NPIs). However, English is one of the very few languages for which such a list exists. We propose the first approach that can be applied to the many languages for which there is no pre-existing high-precision database of NPIs. As a case study, we apply our method to Romanian and show that our method yields good results. Also, we perform a cross-linguistic analysis that suggests interesting connections to some findings in linguistic typology.

  19. Developing a practice of supervision in university as a collective learning process

    DEFF Research Database (Denmark)

    Lund, Birthe; Jensen, Annie Aarup

    2009-01-01

    of the framework surrounding the supervision process, both as regards the students and the teachers; to de-privatize the problems encountered by the individual teacher during the supervision; to ensure that students would be able to graduate within the timeframe of the education (the institutional economic...... of creating a transformation in the sense that it may change from being a top-down project (instigated by the Faculty) and develop into being a bottom-up project. It may hold the potential for developing collective learning processes assuming that good structures and frameworks can be created, as well...

  20. Unsupervised feature learning for human activity recognition%无监督特征学习的人体活动识别

    Institute of Scientific and Technical Information of China (English)

    史殿习; 李勇谋; 丁博

    2015-01-01

    针对人的局限性可能会导致在提取特征中丢失重要信息,从而影响最终的识别效果问题,提出无监督特征学习技术的惯性传感器特征提取方法。其核心思想是使用无监督特征学习方法学习多个特征映射,再将所有特征映射拼接起来形成最终的特征计算方法。其优点是不会造成重要信息的损失,而且可以显著减少所使用的无监督特征学习模型的规模。为了验证所提出的特征提取方法在活动识别中的有效性,运用一个公开的活动识别数据集,使用三种常用无监督模型进行特征提取,并使用支持向量机进行活动识别。实验结果表明,特征提取方法取得了良好的效果,与其他方法相比具有一定的优势。%To solve the problems that human limitations may cause the loss of important information,thus affecting the classification results,a feature extraction method based on unsupervised feature learning techniques was proposed.Unsupervised feature learning method to learn multiple feature maps was used and concatenated together.This method can avoid the loss of important information,and also can significantly reduce the scale of unsupervised feature learning model used.To evaluate the proposed method,experiments on a public human activity recognition dataset were performed,using three commonly used unsupervised feature learning models,and finally using support vector machines to classify activities. The results show that the proposed feature extraction method achieves good results,and has certain advantages compared with other methods.

  1. Identification of characteristic ELM evolution patterns with Alfven-scale measurements and unsupervised machine learning analysis

    Science.gov (United States)

    Smith, David R.; Fonck, R. J.; McKee, G. R.; Diallo, A.; Kaye, S. M.; Leblanc, B. P.; Sabbagh, S. A.

    2016-10-01

    Edge localized mode (ELM) saturation mechanisms, filament dynamics, and multi-mode interactions require nonlinear models, and validation of nonlinear ELM models requires fast, localized measurements on Alfven timescales. Recently, we investigated characteristic ELM evolution patterns with Alfven-scale measurements from the NSTX/NSTX-U beam emission spectroscopy (BES) system. We applied clustering algorithms from the machine learning domain to ELM time-series data. The algorithms identified two or three groups of ELM events with distinct evolution patterns. In addition, we found that the identified ELM groups correspond to distinct parameter regimes for plasma current, shape, magnetic balance, and density pedestal profile. The observed characteristic evolution patterns and corresponding parameter regimes suggest genuine variation in the underlying physical mechanisms that influence the evolution of ELM events and motivate nonlinear MHD simulations. Here, we review the previous results for characteristic ELM evolution patterns and parameter regimes, and we report on a new effort to explore the identified ELM groups with 2D BES measurements and nonlinear MHD simulations. Supported by U.S. Department of Energy Award Numbers DE-SC0001288 and DE-AC02-09CH11466.

  2. Efficient supervised learning in networks with binary synapses

    CERN Document Server

    Baldassi, Carlo; Brunel, Nicolas; Zecchina, Riccardo

    2007-01-01

    Recent experimental studies indicate that synaptic changes induced by neuronal activity are discrete jumps between a small number of stable states. Learning in systems with discrete synapses is known to be a computationally hard problem. Here, we study a neurobiologically plausible on-line learning algorithm that derives from Belief Propagation algorithms. We show that it performs remarkably well in a model neuron with binary synapses, and a finite number of `hidden' states per synapse, that has to learn a random classification task. Such system is able to learn a number of associations close to the theoretical limit, in time which is sublinear in system size. This is to our knowledge the first on-line algorithm that is able to achieve efficiently a finite number of patterns learned per binary synapse. Furthermore, we show that performance is optimal for a finite number of hidden states which becomes very small for sparse coding. The algorithm is similar to the standard `perceptron' learning algorithm, with a...

  3. Supervised orthogonal discriminant subspace projects learning for face recognition.

    Science.gov (United States)

    Chen, Yu; Xu, Xiao-Hong

    2014-02-01

    In this paper, a new linear dimension reduction method called supervised orthogonal discriminant subspace projection (SODSP) is proposed, which addresses high-dimensionality of data and the small sample size problem. More specifically, given a set of data points in the ambient space, a novel weight matrix that describes the relationship between the data points is first built. And in order to model the manifold structure, the class information is incorporated into the weight matrix. Based on the novel weight matrix, the local scatter matrix as well as non-local scatter matrix is defined such that the neighborhood structure can be preserved. In order to enhance the recognition ability, we impose an orthogonal constraint into a graph-based maximum margin analysis, seeking to find a projection that maximizes the difference, rather than the ratio between the non-local scatter and the local scatter. In this way, SODSP naturally avoids the singularity problem. Further, we develop an efficient and stable algorithm for implementing SODSP, especially, on high-dimensional data set. Moreover, the theoretical analysis shows that LPP is a special instance of SODSP by imposing some constraints. Experiments on the ORL, Yale, Extended Yale face database B and FERET face database are performed to test and evaluate the proposed algorithm. The results demonstrate the effectiveness of SODSP.

  4. Developing a practice of supervision in university as a collective learning process

    DEFF Research Database (Denmark)

    Lund, Birthe; Jensen, Annie Aarup

    2009-01-01

    of the framework surrounding the supervision process, both as regards the students and the teachers; to de-privatize the problems encountered by the individual teacher during the supervision; to ensure that students would be able to graduate within the timeframe of the education (the institutional economic......The point of departure of the paper is a university pedagogical course established with the purpose of strengthening the university teachers’ competence regarding the supervision of students working on their master’s thesis. The purpose of the course is furthermore to ensure the improvement...... of creating a transformation in the sense that it may change from being a top-down project (instigated by the Faculty) and develop into being a bottom-up project. It may hold the potential for developing collective learning processes assuming that good structures and frameworks can be created, as well...

  5. Gene classification using parameter-free semi-supervised manifold learning.

    Science.gov (United States)

    Huang, Hong; Feng, Hailiang

    2012-01-01

    A new manifold learning method, called parameter-free semi-supervised local Fisher discriminant analysis (pSELF), is proposed to map the gene expression data into a low-dimensional space for tumor classification. Motivated by the fact that semi-supervised and parameter-free are two desirable and promising characteristics for dimension reduction, a new difference-based optimization objective function with unlabeled samples has been designed. The proposed method preserves the global structure of unlabeled samples in addition to separating labeled samples in different classes from each other. The semi-supervised method has an analytic form of the globally optimal solution, which can be computed efficiently by eigen decomposition. Experimental results on synthetic data and SRBCT, DLBCL, and Brain Tumor gene expression data sets demonstrate the effectiveness of the proposed method.

  6. Unsupervised Language Acquisition

    Science.gov (United States)

    de Marcken, Carl

    1996-11-01

    This thesis presents a computational theory of unsupervised language acquisition, precisely defining procedures for learning language from ordinary spoken or written utterances, with no explicit help from a teacher. The theory is based heavily on concepts borrowed from machine learning and statistical estimation. In particular, learning takes place by fitting a stochastic, generative model of language to the evidence. Much of the thesis is devoted to explaining conditions that must hold for this general learning strategy to arrive at linguistically desirable grammars. The thesis introduces a variety of technical innovations, among them a common representation for evidence and grammars, and a learning strategy that separates the ``content'' of linguistic parameters from their representation. Algorithms based on it suffer from few of the search problems that have plagued other computational approaches to language acquisition. The theory has been tested on problems of learning vocabularies and grammars from unsegmented text and continuous speech, and mappings between sound and representations of meaning. It performs extremely well on various objective criteria, acquiring knowledge that causes it to assign almost exactly the same structure to utterances as humans do. This work has application to data compression, language modeling, speech recognition, machine translation, information retrieval, and other tasks that rely on either structural or stochastic descriptions of language.

  7. Facilitating the Learning Process in Design-Based Learning Practices: An Investigation of Teachers' Actions in Supervising Students

    Science.gov (United States)

    Gómez Puente, S. M.; van Eijck, M.; Jochems, W.

    2013-01-01

    Background: In research on design-based learning (DBL), inadequate attention is paid to the role the teacher plays in supervising students in gathering and applying knowledge to design artifacts, systems, and innovative solutions in higher education. Purpose: In this study, we examine whether teacher actions we previously identified in the DBL…

  8. Emotional Literacy Support Assistants' Views on Supervision Provided by Educational Psychologists: What EPs Can Learn from Group Supervision

    Science.gov (United States)

    Osborne, Cara; Burton, Sheila

    2014-01-01

    The Educational Psychology Service in this study has responsibility for providing group supervision to Emotional Literacy Support Assistants (ELSAs) working in schools. To date, little research has examined this type of inter-professional supervision arrangement. The current study used a questionnaire to examine ELSAs' views on the supervision…

  9. Unsupervised statistical clustering of environmental shotgun sequences

    Directory of Open Access Journals (Sweden)

    Bhatnagar Srijak

    2009-10-01

    Full Text Available Abstract Background The development of effective environmental shotgun sequence binning methods remains an ongoing challenge in algorithmic analysis of metagenomic data. While previous methods have focused primarily on supervised learning involving extrinsic data, a first-principles statistical model combined with a self-training fitting method has not yet been developed. Results We derive an unsupervised, maximum-likelihood formalism for clustering short sequences by their taxonomic origin on the basis of their k-mer distributions. The formalism is implemented using a Markov Chain Monte Carlo approach in a k-mer feature space. We introduce a space transformation that reduces the dimensionality of the feature space and a genomic fragment divergence measure that strongly correlates with the method's performance. Pairwise analysis of over 1000 completely sequenced genomes reveals that the vast majority of genomes have sufficient genomic fragment divergence to be amenable for binning using the present formalism. Using a high-performance implementation, the binner is able to classify fragments as short as 400 nt with accuracy over 90% in simulations of low-complexity communities of 2 to 10 species, given sufficient genomic fragment divergence. The method is available as an open source package called LikelyBin. Conclusion An unsupervised binning method based on statistical signatures of short environmental sequences is a viable stand-alone binning method for low complexity samples. For medium and high complexity samples, we discuss the possibility of combining the current method with other methods as part of an iterative process to enhance the resolving power of sorting reads into taxonomic and/or functional bins.

  10. Online semi-supervised learning: algorithm and application in metagenomics

    NARCIS (Netherlands)

    S. Imangaliyev; B. Keijser; W. Crielaard; E. Tsivtsivadze

    2013-01-01

    As the amount of metagenomic data grows rapidly, online statistical learning algorithms are poised to play key role in metagenome analysis tasks. Frequently, data are only partially labeled, namely dataset contains partial information about the problem of interest. This work presents an algorithm an

  11. Online Semi-Supervised Learning: Algorithm and Application in Metagenomics

    NARCIS (Netherlands)

    Imangaliyev, S.; Keijser, B.J.F.; Crielaard, W.; Tsivtsivadze, E.

    2013-01-01

    As the amount of metagenomic data grows rapidly, online statistical learning algorithms are poised to play key rolein metagenome analysis tasks. Frequently, data are only partially labeled, namely dataset contains partial information about the problem of interest. This work presents an algorithm and

  12. Generating a Spanish Affective Dictionary with Supervised Learning Techniques

    Science.gov (United States)

    Bermudez-Gonzalez, Daniel; Miranda-Jiménez, Sabino; García-Moreno, Raúl-Ulises; Calderón-Nepamuceno, Dora

    2016-01-01

    Nowadays, machine learning techniques are being used in several Natural Language Processing (NLP) tasks such as Opinion Mining (OM). OM is used to analyse and determine the affective orientation of texts. Usually, OM approaches use affective dictionaries in order to conduct sentiment analysis. These lexicons are labeled manually with affective…

  13. Quantum-Enhanced Machine Learning.

    Science.gov (United States)

    Dunjko, Vedran; Taylor, Jacob M; Briegel, Hans J

    2016-09-23

    The emerging field of quantum machine learning has the potential to substantially aid in the problems and scope of artificial intelligence. This is only enhanced by recent successes in the field of classical machine learning. In this work we propose an approach for the systematic treatment of machine learning, from the perspective of quantum information. Our approach is general and covers all three main branches of machine learning: supervised, unsupervised, and reinforcement learning. While quantum improvements in supervised and unsupervised learning have been reported, reinforcement learning has received much less attention. Within our approach, we tackle the problem of quantum enhancements in reinforcement learning as well, and propose a systematic scheme for providing improvements. As an example, we show that quadratic improvements in learning efficiency, and exponential improvements in performance over limited time periods, can be obtained for a broad class of learning problems.

  14. Quantum-Enhanced Machine Learning

    Science.gov (United States)

    Dunjko, Vedran; Taylor, Jacob M.; Briegel, Hans J.

    2016-09-01

    The emerging field of quantum machine learning has the potential to substantially aid in the problems and scope of artificial intelligence. This is only enhanced by recent successes in the field of classical machine learning. In this work we propose an approach for the systematic treatment of machine learning, from the perspective of quantum information. Our approach is general and covers all three main branches of machine learning: supervised, unsupervised, and reinforcement learning. While quantum improvements in supervised and unsupervised learning have been reported, reinforcement learning has received much less attention. Within our approach, we tackle the problem of quantum enhancements in reinforcement learning as well, and propose a systematic scheme for providing improvements. As an example, we show that quadratic improvements in learning efficiency, and exponential improvements in performance over limited time periods, can be obtained for a broad class of learning problems.

  15. Extended apprenticeship learning in doctoral training and supervision - moving beyond 'cookbook recipes'

    DEFF Research Database (Denmark)

    Tanggaard, Lene; Wegener, Charlotte

    An apprenticeship perspective on learning in academia sheds light on the potential for mutual learning and production, and also reveals the diverse range of learning resources beyond the formal novice-–expert relationship. Although apprenticeship is a well-known concept in educational research......, in this case apprenticeship offers an innovative perspective on future practice and research in academia allowing more students access to high high-quality research training and giving supervisors a chance to combine their own research with their supervision obligations....

  16. Recent advances on techniques and theories of feedforward networks with supervised learning

    Science.gov (United States)

    Xu, Lei; Klasa, Stan

    1992-07-01

    The rediscovery and popularization of the back propagation training technique for multilayer perceptrons as well as the invention of the Boltzmann Machine learning algorithm has given a new boost to the study of supervised learning networks. In recent years, besides the widely spread applications and the various further improvements of the classical back propagation technique, many new supervised learning models, techniques as well as theories, have also been proposed in a vast number of publications. This paper tries to give a rather systematical review on the recent advances on supervised learning techniques and theories for static feedforward networks. We summarize a great number of developments into four aspects: (1) Various improvements and variants made on the classical back propagation techniques for multilayer (static) perceptron nets, for speeding up training, avoiding local minima, increasing the generalization ability, as well as for many other interesting purposes. (2) A number of other learning methods for training multilayer (static) perceptron, such as derivative estimation by perturbation, direct weight update by perturbation, genetic algorithms, recursive least square estimate and extended Kalman filter, linear programming, the policy of fixing one layer while updating another, constructing networks by converting decision tree classifiers, and others. (3) Various other feedforward models which are also able to implement function approximation, probability density estimation and classification, including various models of basis function expansion (e.g., radial basis functions, restricted coulomb energy, multivariate adaptive regression splines, trigonometric and polynomial bases, projection pursuit, basis function tree, and may others), and several other supervised learning models. (4) Models with complex structures, e.g., modular architecture, hierarchy architecture, and others. (5) A number of theoretical issues involving the universal

  17. Assessing Miniaturized Sensor Performance using Supervised Learning, with Application to Drug and Explosive Detection

    DEFF Research Database (Denmark)

    Alstrøm, Tommy Sonne

    of sensors, as the sensors are designed to provide robust and reliable measurements. That means, the sensors are designed to have repeated measurement clusters. Sensor fusion is presented for the sensor based on chemoselective compounds. An array of color changing compounds are handled and in unity they make......This Ph.D. thesis titled “Assessing Miniaturized Sensor Performance using Supervised Learning, with Application to Drug and Explosive Detection” is a part of the strategic research project “Miniaturized sensors for explosives detection in air” funded by the Danish Agency for Science and Technology...... before the sensor responses can be applied to supervised learning algorithms. The technologies used for sensing consist of Calorimetry, Cantilevers, Chemoselective compounds, Quartz Crystal Microbalance and Surface Enhanced Raman Scattering. Each of the sensors have their own strength and weaknesses...

  18. Supervised Machine Learning Methods Applied to Predict Ligand- Binding Affinity.

    Science.gov (United States)

    Heck, Gabriela S; Pintro, Val O; Pereira, Richard R; de Ávila, Mauricio B; Levin, Nayara M B; de Azevedo, Walter F

    2017-01-01

    Calculation of ligand-binding affinity is an open problem in computational medicinal chemistry. The ability to computationally predict affinities has a beneficial impact in the early stages of drug development, since it allows a mathematical model to assess protein-ligand interactions. Due to the availability of structural and binding information, machine learning methods have been applied to generate scoring functions with good predictive power. Our goal here is to review recent developments in the application of machine learning methods to predict ligand-binding affinity. We focus our review on the application of computational methods to predict binding affinity for protein targets. In addition, we also describe the major available databases for experimental binding constants and protein structures. Furthermore, we explain the most successful methods to evaluate the predictive power of scoring functions. Association of structural information with ligand-binding affinity makes it possible to generate scoring functions targeted to a specific biological system. Through regression analysis, this data can be used as a base to generate mathematical models to predict ligandbinding affinities, such as inhibition constant, dissociation constant and binding energy. Experimental biophysical techniques were able to determine the structures of over 120,000 macromolecules. Considering also the evolution of binding affinity information, we may say that we have a promising scenario for development of scoring functions, making use of machine learning techniques. Recent developments in this area indicate that building scoring functions targeted to the biological systems of interest shows superior predictive performance, when compared with other approaches. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  19. Weakly supervised learning of a classifier for unusual event detection.

    Science.gov (United States)

    Jäger, Mark; Knoll, Christian; Hamprecht, Fred A

    2008-09-01

    In this paper, we present an automatic classification framework combining appearance based features and hidden Markov models (HMM) to detect unusual events in image sequences. One characteristic of the classification task is that anomalies are rare. This reflects the situation in the quality control of industrial processes, where error events are scarce by nature. As an additional restriction, class labels are only available for the complete image sequence, since frame-wise manual scanning of the recorded sequences for anomalies is too expensive and should, therefore, be avoided. The proposed framework reduces the feature space dimension of the image sequences by employing subspace methods and encodes characteristic temporal dynamics using continuous hidden Markov models (CHMMs). The applied learning procedure is as follows. 1) A generative model for the regular sequences is trained (one-class learning). 2) The regular sequence model (RSM) is used to locate potentially unusual segments within error sequences by means of a change detection algorithm (outlier detection). 3) Unusual segments are used to expand the RSM to an error sequence model (ESM). The complexity of the ESM is controlled by means of the Bayesian Information Criterion (BIC). The likelihood ratio of the data given the ESM and the RSM is used for the classification decision. This ratio is close to one for sequences without error events and increases for sequences containing error events. Experimental results are presented for image sequences recorded from industrial laser welding processes. We demonstrate that the learning procedure can significantly reduce the user interaction and that sequences with error events can be found with a small false positive rate. It has also been shown that a modeling of the temporal dynamics is necessary to reach these low error rates.

  20. Integrating learning assessment and supervision in a competency framework for clinical workplace education.

    Science.gov (United States)

    Embo, M; Driessen, E; Valcke, M; van der Vleuten, C P M

    2015-02-01

    Although competency-based education is well established in health care education, research shows that the competencies do not always match the reality of clinical workplaces. Therefore, there is a need to design feasible and evidence-based competency frameworks that fit the workplace reality. This theoretical paper outlines a competency-based framework, designed to facilitate learning, assessment and supervision in clinical workplace education. Integration is the cornerstone of this holistic competency framework.

  1. Hierarchical Wireless Multimedia Sensor Networks for Collaborative Hybrid Semi-Supervised Classifier Learning

    Directory of Open Access Journals (Sweden)

    Liang Ding

    2007-11-01

    Full Text Available Wireless multimedia sensor networks (WMSN have recently emerged as one ofthe most important technologies, driven by the powerful multimedia signal acquisition andprocessing abilities. Target classification is an important research issue addressed in WMSN,which has strict requirement in robustness, quickness and accuracy. This paper proposes acollaborative semi-supervised classifier learning algorithm to achieve durative onlinelearning for support vector machine (SVM based robust target classification. The proposedalgorithm incrementally carries out the semi-supervised classifier learning process inhierarchical WMSN, with the collaboration of multiple sensor nodes in a hybrid computingparadigm. For decreasing the energy consumption and improving the performance, somemetrics are introduced to evaluate the effectiveness of the samples in specific sensor nodes,and a sensor node selection strategy is also proposed to reduce the impact of inevitablemissing detection and false detection. With the ant optimization routing, the learningprocess is implemented with the selected sensor nodes, which can decrease the energyconsumption. Experimental results demonstrate that the collaborative hybrid semi-supervised classifier learning algorithm can effectively implement target classification inhierarchical WMSN. It has outstanding performance in terms of energy efficiency and timecost, which verifies the effectiveness of the sensor nodes selection and ant optimizationrouting.

  2. Clinical learning environment, supervision and nurse teacher evaluation scale: psychometric evaluation of the Swedish version.

    Science.gov (United States)

    Johansson, Unn-Britt; Kaila, Päivi; Ahlner-Elmqvist, Marianne; Leksell, Janeth; Isoaho, Hannu; Saarikoski, Mikko

    2010-09-01

    This article is a report of the development and psychometric testing of the Swedish version of the Clinical Learning Environment, Supervision and Nurse Teacher evaluation scale. To achieve quality assurance, collaboration between the healthcare and nursing systems is a pre-requisite. Therefore, it is important to develop a tool that can measure the quality of clinical education. The Clinical Learning Environment, Supervision and Nurse Teacher evaluation scale is a previously validated instrument, currently used in several universities across Europe. The instrument has been suggested for use as part of quality assessment and evaluation of nursing education. The scale was translated into Swedish from the English version. Data were collected between March 2008 and May 2009 among nursing students from three university colleges, with 324 students completing the questionnaire. Exploratory factor analysis was performed on the 34-item scale to determine construct validity and Cronbach's alpha was used to measure the internal consistency. The five sub-dimensions identified in the original scale were replicated in the exploratory factor analysis. The five factors had explanation percentages of 60.2%, which is deemed sufficient. Cronbach's alpha coefficient for the total scale was 0.95, and varied between 0.96 and 0.75 within the five sub-dimensions. The Swedish version of Clinical Learning Environment, Supervision and Nurse Teacher evaluation scale has satisfactory psychometric properties and could be a useful quality instrument in nursing education. However, further investigation is required to develop and evaluate the questionnaire.

  3. Fall detection using supervised machine learning algorithms: A comparative study

    KAUST Repository

    Zerrouki, Nabil

    2017-01-05

    Fall incidents are considered as the leading cause of disability and even mortality among older adults. To address this problem, fall detection and prevention fields receive a lot of intention over the past years and attracted many researcher efforts. We present in the current study an overall performance comparison between fall detection systems using the most popular machine learning approaches which are: Naïve Bayes, K nearest neighbor, neural network, and support vector machine. The analysis of the classification power associated to these most widely utilized algorithms is conducted on two fall detection databases namely FDD and URFD. Since the performance of the classification algorithm is inherently dependent on the features, we extracted and used the same features for all classifiers. The classification evaluation is conducted using different state of the art statistical measures such as the overall accuracy, the F-measure coefficient, and the area under ROC curve (AUC) value.

  4. Entropy based unsupervised Feature Selection in digital mammogram image using rough set theory.

    Science.gov (United States)

    Velayutham, C; Thangavel, K

    2012-01-01

    Feature Selection (FS) is a process, which attempts to select features, which are more informative. In the supervised FS methods various feature subsets are evaluated using an evaluation function or metric to select only those features, which are related to the decision classes of the data under consideration. However, for many data mining applications, decision class labels are often unknown or incomplete, thus indicating the significance of unsupervised FS. However, in unsupervised learning, decision class labels are not provided. The problem is that not all features are important. Some of the features may be redundant, and others may be irrelevant and noisy. In this paper, a novel unsupervised FS in mammogram image, using rough set-based entropy measures, is proposed. A typical mammogram image processing system generally consists of mammogram image acquisition, pre-processing of image, segmentation, features extracted from the segmented mammogram image. The proposed method is used to select features from data set, the method is compared with the existing rough set-based supervised FS methods and classification performance of both methods are recorded and demonstrates the efficiency of the method.

  5. Fast Affinity Propagation Clustering based on Machine Learning

    OpenAIRE

    Shailendra Kumar Shrivastava; J. L. Rana; DR.R.C.JAIN

    2013-01-01

    Affinity propagation (AP) was recently introduced as an un-supervised learning algorithm for exemplar based clustering. In this paper a novel Fast Affinity Propagation clustering Approach based on Machine Learning (FAPML) has been proposed. FAPML tries to put data points into clusters based on the history of the data points belonging to clusters in early stages. In FAPML we introduce affinity learning constant and dispersion constant which supervise the clustering process. FAPML also enforces...

  6. Content-Based High-Resolution Remote Sensing Image Retrieval via Unsupervised Feature Learning and Collaborative Affinity Metric Fusion

    Directory of Open Access Journals (Sweden)

    Yansheng Li

    2016-08-01

    Full Text Available With the urgent demand for automatic management of large numbers of high-resolution remote sensing images, content-based high-resolution remote sensing image retrieval (CB-HRRS-IR has attracted much research interest. Accordingly, this paper proposes a novel high-resolution remote sensing image retrieval approach via multiple feature representation and collaborative affinity metric fusion (IRMFRCAMF. In IRMFRCAMF, we design four unsupervised convolutional neural networks with different layers to generate four types of unsupervised features from the fine level to the coarse level. In addition to these four types of unsupervised features, we also implement four traditional feature descriptors, including local binary pattern (LBP, gray level co-occurrence (GLCM, maximal response 8 (MR8, and scale-invariant feature transform (SIFT. In order to fully incorporate the complementary information among multiple features of one image and the mutual information across auxiliary images in the image dataset, this paper advocates collaborative affinity metric fusion to measure the similarity between images. The performance evaluation of high-resolution remote sensing image retrieval is implemented on two public datasets, the UC Merced (UCM dataset and the Wuhan University (WH dataset. Large numbers of experiments show that our proposed IRMFRCAMF can significantly outperform the state-of-the-art approaches.

  7. Clinical learning environment and supervision of international nursing students: A cross-sectional study.

    Science.gov (United States)

    Mikkonen, Kristina; Elo, Satu; Miettunen, Jouko; Saarikoski, Mikko; Kääriäinen, Maria

    2017-05-01

    Previously, it has been shown that the clinical learning environment causes challenges for international nursing students, but there is a lack of empirical evidence relating to the background factors explaining and influencing the outcomes. To describe international and national students' perceptions of their clinical learning environment and supervision, and explain the related background factors. An explorative cross-sectional design was used in a study conducted in eight universities of applied sciences in Finland during September 2015-May 2016. All nursing students studying English language degree programs were invited to answer a self-administered questionnaire based on both the clinical learning environment, supervision and nurse teacher scale and Cultural and Linguistic Diversity scale with additional background questions. Participants (n=329) included international (n=231) and Finnish (n=98) nursing students. Binary logistic regression was used to identify background factors relating to the clinical learning environment and supervision. International students at a beginner level in Finnish perceived the pedagogical atmosphere as worse than native speakers. In comparison to native speakers, these international students generally needed greater support from the nurse teacher at their university. Students at an intermediate level in Finnish reported two times fewer negative encounters in cultural diversity at their clinical placement than the beginners. To facilitate a successful learning experience, international nursing students require a sufficient level of competence in the native language when conducting clinical placements. Educational interventions in language education are required to test causal effects on students' success in the clinical learning environment. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. Scikit-learn: Machine Learning in Python

    OpenAIRE

    Pedregosa, Fabian; Varoquaux, Gaël; Gramfort, Alexandre; Michel, Vincent; Thirion, Bertrand; Grisel, Olivier; Blondel, Mathieu; Prettenhofer, Peter; Weiss, Ron; Dubourg, Vincent; Vanderplas, Jake; Passos, Alexandre; Cournapeau, David; Brucher, Matthieu; Perrot, Matthieu

    2011-01-01

    International audience; Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic ...

  9. Automated labeling of cancer textures in larynx histopathology slides using quasi-supervised learning.

    Science.gov (United States)

    Onder, Devrim; Sarioglu, Sulen; Karacali, Bilge

    2014-12-01

    To evaluate the performance of a quasi-supervised statistical learning algorithm, operating on datasets having normal and neoplastic tissues, to identify larynx squamous cell carcinomas. Furthermore, cancer texture separability measures against normal tissues are to be developed and compared either for colorectal or larynx tissues. Light microscopic digital images from histopathological sections were obtained from laryngectomy materials including squamous cell carcinoma and nonneoplastic regions. The texture features were calculated by using co-occurrence matrices and local histograms. The texture features were input to the quasi-supervised learning algorithm. Larynx regions containing squamous cell carcinomas were accurately identified, having false and true positive rates up to 21% and 87%, respectively. Larynx squamous cell carcinoma versus normal tissue texture separability measures were higher than colorectal adenocarcinoma versus normal textures for the colorectal database. Furthermore, the resultant labeling performances for all larynx datasets are higher than or equal to that of colorectal datasets. The results in larynx datasets, in comparison with the former colorectal study, suggested that quasi-supervised texture classification is to be a helpful method in histopathological image classification and analysis.

  10. Musical Instrument Classification Based on Nonlinear Recurrence Analysis and Supervised Learning

    Directory of Open Access Journals (Sweden)

    R.Rui

    2013-04-01

    Full Text Available In this paper, the phase space reconstruction of time series produced by different instruments is discussed based on the nonlinear dynamic theory. The dense ratio, a novel quantitative recurrence parameter, is proposed to describe the difference of wind instruments, stringed instruments and keyboard instruments in the phase space by analyzing the recursive property of every instrument. Furthermore, a novel supervised learning algorithm for automatic classification of individual musical instrument signals is addressed deriving from the idea of supervised non-negative matrix factorization (NMF algorithm. In our approach, the orthogonal basis matrix could be obtained without updating the matrix iteratively, which NMF is unable to do. The experimental results indicate that the accuracy of the proposed method is improved by 3% comparing with the conventional features in the individual instrument classification.

  11. Semi-supervised Learning for Classification of Polarimetric SAR Images Based on SVM-Wishart

    Directory of Open Access Journals (Sweden)

    Hua Wen-qiang

    2015-02-01

    Full Text Available In this study, we propose a new semi-supervised classification method for Polarimetric SAR (PolSAR images, aiming at handling the issue that the number of train set is small. First, considering the scattering characters of PolSAR data, this method extracts multiple scattering features using target decomposition approach. Then, a semi-supervised learning model is established based on a co-training framework and Support Vector Machine (SVM. Both labeled and unlabeled data are utilized in this model to obtain high classification accuracy. Third, a recovery scheme based on the Wishart classifier is proposed to improve the classification performance. From the experiments conducted in this study, it is evident that the proposed method performs more effectively compared with other traditional methods when the number of train set is small.

  12. Wireless capsule endoscopy video classification using an unsupervised learning approach%无监督学习的无线胶囊内诊镜视频分类方法

    Institute of Scientific and Technical Information of China (English)

    冯文刚; 高隽; Bill P.Buckles; 吴克伟

    2011-01-01

    Since Wireless Capsule Endoscopy ( WCE) is a novel technology for recording the videos of the digestive tract of a patient,the problem of segmenting the WCE videos of the digestive tract into sub-images corresponding to the mouth,stomach, small intestine and large intestine regions is not well addressed in the literature. A few papers addressing this problem use a supervised learning approach that presumes availability of a large database of correctly labeled training samples. Considering the difficulties in procuring sizable WCE training data sets needed for achieving high classification accuracy,we introduce an unsupervised learning approach that employs Scale invariant feature transform (SIFT) with color information for extraction of local features and uses probabilistic latent semantic analysis (pLSA) model for data semantic analysis. Our results indicate that this method compares well in classification accuracy with the state-of-the-art supervised classification approach to WCE image classification.%近年来在对病人的消化道系统检查中,无线胶囊内诊镜(WCE)是一种最新技术,可以让医生直接观察到病人的病灶所在,但是对于消化道系统中的口腔、胃、小肠和大肠的WCE视频分类却是难点所在.相关研究中均采用通过人工标记的训练库的有监督学习方法.为了在WCE训练数据中获得高识别率,提出一种无监督学习方法,它利用融合颜色信息的尺度不变特征转换( SIFT)获取局部特征,再利用概率隐语义分析模型(pLSA)数据训练中进行语义内容分析.实验结果表明,在WCE图像分类中本方法与当前最新的监督分类方法一样可以获得高准确率.

  13. Exploiting Attribute Correlations: A Novel Trace Lasso-Based Weakly Supervised Dictionary Learning Method.

    Science.gov (United States)

    Wu, Lin; Wang, Yang; Pan, Shirui

    2016-10-04

    It is now well established that sparse representation models are working effectively for many visual recognition tasks, and have pushed forward the success of dictionary learning therein. Recent studies over dictionary learning focus on learning discriminative atoms instead of purely reconstructive ones. However, the existence of intraclass diversities (i.e., data objects within the same category but exhibit large visual dissimilarities), and interclass similarities (i.e., data objects from distinct classes but share much visual similarities), makes it challenging to learn effective recognition models. To this end, a large number of labeled data objects are required to learn models which can effectively characterize these subtle differences. However, labeled data objects are always limited to access, committing it difficult to learn a monolithic dictionary that can be discriminative enough. To address the above limitations, in this paper, we propose a weakly-supervised dictionary learning method to automatically learn a discriminative dictionary by fully exploiting visual attribute correlations rather than label priors. In particular, the intrinsic attribute correlations are deployed as a critical cue to guide the process of object categorization, and then a set of subdictionaries are jointly learned with respect to each category. The resulting dictionary is highly discriminative and leads to intraclass diversity aware sparse representations. Extensive experiments on image classification and object recognition are conducted to show the effectiveness of our approach.

  14. New supervised learning theory applied to cerebellar modeling for suppression of variability of saccade end points.

    Science.gov (United States)

    Fujita, Masahiko

    2013-06-01

    A new supervised learning theory is proposed for a hierarchical neural network with a single hidden layer of threshold units, which can approximate any continuous transformation, and applied to a cerebellar function to suppress the end-point variability of saccades. In motor systems, feedback control can reduce noise effects if the noise is added in a pathway from a motor center to a peripheral effector; however, it cannot reduce noise effects if the noise is generated in the motor center itself: a new control scheme is necessary for such noise. The cerebellar cortex is well known as a supervised learning system, and a novel theory of cerebellar cortical function developed in this study can explain the capability of the cerebellum to feedforwardly reduce noise effects, such as end-point variability of saccades. This theory assumes that a Golgi-granule cell system can encode the strength of a mossy fiber input as the state of neuronal activity of parallel fibers. By combining these parallel fiber signals with appropriate connection weights to produce a Purkinje cell output, an arbitrary continuous input-output relationship can be obtained. By incorporating such flexible computation and learning ability in a process of saccadic gain adaptation, a new control scheme in which the cerebellar cortex feedforwardly suppresses the end-point variability when it detects a variation in saccadic commands can be devised. Computer simulation confirmed the efficiency of such learning and showed a reduction in the variability of saccadic end points, similar to results obtained from experimental data.

  15. Enhanced low-rank representation via sparse manifold adaption for semi-supervised learning.

    Science.gov (United States)

    Peng, Yong; Lu, Bao-Liang; Wang, Suhang

    2015-05-01

    Constructing an informative and discriminative graph plays an important role in various pattern recognition tasks such as clustering and classification. Among the existing graph-based learning models, low-rank representation (LRR) is a very competitive one, which has been extensively employed in spectral clustering and semi-supervised learning (SSL). In SSL, the graph is composed of both labeled and unlabeled samples, where the edge weights are calculated based on the LRR coefficients. However, most of existing LRR related approaches fail to consider the geometrical structure of data, which has been shown beneficial for discriminative tasks. In this paper, we propose an enhanced LRR via sparse manifold adaption, termed manifold low-rank representation (MLRR), to learn low-rank data representation. MLRR can explicitly take the data local manifold structure into consideration, which can be identified by the geometric sparsity idea; specifically, the local tangent space of each data point was sought by solving a sparse representation objective. Therefore, the graph to depict the relationship of data points can be built once the manifold information is obtained. We incorporate a regularizer into LRR to make the learned coefficients preserve the geometric constraints revealed in the data space. As a result, MLRR combines both the global information emphasized by low-rank property and the local information emphasized by the identified manifold structure. Extensive experimental results on semi-supervised classification tasks demonstrate that MLRR is an excellent method in comparison with several state-of-the-art graph construction approaches.

  16. Machine Learning for Neuroimaging with Scikit-Learn

    Directory of Open Access Journals (Sweden)

    Alexandre eAbraham

    2014-02-01

    Full Text Available Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g. multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g. resting state functional MRI or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.

  17. Machine learning for neuroimaging with scikit-learn.

    Science.gov (United States)

    Abraham, Alexandre; Pedregosa, Fabian; Eickenberg, Michael; Gervais, Philippe; Mueller, Andreas; Kossaifi, Jean; Gramfort, Alexandre; Thirion, Bertrand; Varoquaux, Gaël

    2014-01-01

    Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g., resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.

  18. DL-ReSuMe: A Delay Learning-Based Remote Supervised Method for Spiking Neurons.

    Science.gov (United States)

    Taherkhani, Aboozar; Belatreche, Ammar; Li, Yuhua; Maguire, Liam P

    2015-12-01

    Recent research has shown the potential capability of spiking neural networks (SNNs) to model complex information processing in the brain. There is biological evidence to prove the use of the precise timing of spikes for information coding. However, the exact learning mechanism in which the neuron is trained to fire at precise times remains an open problem. The majority of the existing learning methods for SNNs are based on weight adjustment. However, there is also biological evidence that the synaptic delay is not constant. In this paper, a learning method for spiking neurons, called delay learning remote supervised method (DL-ReSuMe), is proposed to merge the delay shift approach and ReSuMe-based weight adjustment to enhance the learning performance. DL-ReSuMe uses more biologically plausible properties, such as delay learning, and needs less weight adjustment than ReSuMe. Simulation results have shown that the proposed DL-ReSuMe approach achieves learning accuracy and learning speed improvements compared with ReSuMe.

  19. Virtual Calibration of Cosmic Ray Sensor: Using Supervised Ensemble Machine Learning

    Directory of Open Access Journals (Sweden)

    Ritaban Dutta

    2013-09-01

    Full Text Available In this paper an ensemble of supervised machine learning methods has been investigated to virtually and dynamically calibrate the cosmic ray sensors measuring area wise bulk soil moisture. Main focus of this study was to find an alternative to the currently available field calibration method; based on expensive and time consuming soil sample collection methodology. Data from the Australian Water Availability Project (AWAP database was used as independent soil moisture ground truth and results were compared against the conventionally estimated soil moisture using a Hydroinnova CRS-1000 cosmic ray probe deployed in Tullochgorum, Australia. Prediction performance of a complementary ensemble of four supervised estimators, namely Sugano type Adaptive Neuro-Fuzzy Inference System (S-ANFIS, Cascade Forward Neural Network (CFNN, Elman Neural Network (ENN and Learning Vector Quantization Neural Network (LVQN was evaluated using training and testing paradigms. An AWAP trained ensemble of four estimators was able to predict bulk soil moisture directly from cosmic ray neutron counts with 94.4% as best accuracy. The ensemble approach outperformed the individual performances from these networks. This result proved that an ensemble machine learning based paradigm could be a valuable alternative data driven calibration method for cosmic ray sensors against the current expensive and hydrological assumption based field calibration method.

  20. Test-retest reliability of the Clinical Learning Environment, Supervision and Nurse Teacher (CLES + T) scale.

    Science.gov (United States)

    Gustafsson, Margareta; Blomberg, Karin; Holmefur, Marie

    2015-07-01

    The Clinical Learning Environment, Supervision and Nurse Teacher (CLES + T) scale evaluates the student nurses' perception of the learning environment and supervision within the clinical placement. It has never been tested in a replication study. The aim of the present study was to evaluate the test-retest reliability of the CLES + T scale. The CLES + T scale was administered twice to a group of 42 student nurses, with a one-week interval. Test-retest reliability was determined by calculations of Intraclass Correlation Coefficients (ICCs) and weighted Kappa coefficients. Standard Error of Measurements (SEM) and Smallest Detectable Difference (SDD) determined the precision of individual scores. Bland-Altman plots were created for analyses of systematic differences between the test occasions. The results of the study showed that the stability over time was good to excellent (ICC 0.88-0.96) in the sub-dimensions "Supervisory relationship", "Pedagogical atmosphere on the ward" and "Role of the nurse teacher". Measurements of "Premises of nursing on the ward" and "Leadership style of the manager" had lower but still acceptable stability (ICC 0.70-0.75). No systematic differences occurred between the test occasions. This study supports the usefulness of the CLES + T scale as a reliable measure of the student nurses' perception of the learning environment within the clinical placement at a hospital.

  1. Identification of Village Building via Google Earth Images and Supervised Machine Learning Methods

    Directory of Open Access Journals (Sweden)

    Zhiling Guo

    2016-03-01

    Full Text Available In this study, a method based on supervised machine learning is proposed to identify village buildings from open high-resolution remote sensing images. We select Google Earth (GE RGB images to perform the classification in order to examine its suitability for village mapping, and investigate the feasibility of using machine learning methods to provide automatic classification in such fields. By analyzing the characteristics of GE images, we design different features on the basis of two kinds of supervised machine learning methods for classification: adaptive boosting (AdaBoost and convolutional neural networks (CNN. To recognize village buildings via their color and texture information, the RGB color features and a large number of Haar-like features in a local window are utilized in the AdaBoost method; with multilayer trained networks based on gradient descent algorithms and back propagation, CNN perform the identification by mining deeper information from buildings and their neighborhood. Experimental results from the testing area at Savannakhet province in Laos show that our proposed AdaBoost method achieves an overall accuracy of 96.22% and the CNN method is also competitive with an overall accuracy of 96.30%.

  2. Towards harmonized seismic analysis across Europe using supervised machine learning approaches

    Science.gov (United States)

    Zaccarelli, Riccardo; Bindi, Dino; Cotton, Fabrice; Strollo, Angelo

    2017-04-01

    In the framework of the Thematic Core Services for Seismology of EPOS-IP (European Plate Observing System-Implementation Phase), a service for disseminating a regionalized logic-tree of ground motions models for Europe is under development. While for the Mediterranean area the large availability of strong motion data qualified and disseminated through the Engineering Strong Motion database (ESM-EPOS), supports the development of both selection criteria and ground motion models, for the low-to-moderate seismic regions of continental Europe the development of ad-hoc models using weak motion recordings of moderate earthquakes is unavoidable. Aim of this work is to present a platform for creating application-oriented earthquake databases by retrieving information from EIDA (European Integrated Data Archive) and applying supervised learning models for earthquake records selection and processing suitable for any specific application of interest. Supervised learning models, i.e. the task of inferring a function from labelled training data, have been extensively used in several fields such as spam detection, speech and image recognition and in general pattern recognition. Their suitability to detect anomalies and perform a semi- to fully- automated filtering on large waveform data set easing the effort of (or replacing) human expertise is therefore straightforward. Being supervised learning algorithms capable of learning from a relatively small training set to predict and categorize unseen data, its advantage when processing large amount of data is crucial. Moreover, their intrinsic ability to make data driven predictions makes them suitable (and preferable) in those cases where explicit algorithms for detection might be unfeasible or too heuristic. In this study, we consider relatively simple statistical classifiers (e.g., Naive Bayes, Logistic Regression, Random Forest, SVMs) where label are assigned to waveform data based on "recognized classes" needed for our use case

  3. Unsupervised learning toward brain imaging data analysis: cigarette craving and resistance related neuronal activations from functional magnetic resonance imaging data analysis

    Science.gov (United States)

    Kim, Dong-Youl; Lee, Jong-Hwan

    2014-05-01

    A data-driven unsupervised learning such as an independent component analysis was gainfully applied to bloodoxygenation- level-dependent (BOLD) functional magnetic resonance imaging (fMRI) data compared to a model-based general linear model (GLM). This is due to an ability of this unsupervised learning method to extract a meaningful neuronal activity from BOLD signal that is a mixture of confounding non-neuronal artifacts such as head motions and physiological artifacts as well as neuronal signals. In this study, we support this claim by identifying neuronal underpinnings of cigarette craving and cigarette resistance. The fMRI data were acquired from heavy cigarette smokers (n = 14) while they alternatively watched images with and without cigarette smoking. During acquisition of two fMRI runs, they were asked to crave when they watched cigarette smoking images or to resist the urge to smoke. Data driven approaches of group independent component analysis (GICA) method based on temporal concatenation (TC) and TCGICA with an extension of iterative dual-regression (TC-GICA-iDR) were applied to the data. From the results, cigarette craving and cigarette resistance related neuronal activations were identified in the visual area and superior frontal areas, respectively with a greater statistical significance from the TC-GICA-iDR method than the TC-GICA method. On the other hand, the neuronal activity levels in many of these regions were not statistically different from the GLM method between the cigarette craving and cigarette resistance due to potentially aberrant BOLD signals.

  4. Learning phacoemulsification. Results of different teaching methods.

    Directory of Open Access Journals (Sweden)

    Hennig Albrecht

    2004-01-01

    Full Text Available We report the learning curves of three eye surgeons converting from sutureless extracapsular cataract extraction to phacoemulsification using different teaching methods. Posterior capsule rupture (PCR as a per-operative complication and visual outcome of the first 100 operations were analysed. The PCR rate was 4% and 15% in supervised and unsupervised surgery respectively. Likewise, an uncorrected visual acuity of > or = 6/18 on the first postoperative day was seen in 62 (62% of patients and in 22 (22% in supervised and unsupervised surgery respectively.

  5. Feature-Based Localization in Sonar-Equipped Autonomous Mobile Robots Through Hough Transform and Unsupervised Learning Network,

    Science.gov (United States)

    1998-06-01

    research has focused on the development of autonomous mobile robots - robots that can move about without human supervision. This brings with it several...around it? Various methods of localization in mobile robots have been explored. Most of these methods, however, assume some a priori knowledge of the

  6. Multi-Modal Curriculum Learning for Semi-Supervised Image Classification.

    Science.gov (United States)

    Gong, Chen; Tao, Dacheng; Maybank, Stephen J; Liu, Wei; Kang, Guoliang; Yang, Jie

    2016-07-01

    Semi-supervised image classification aims to classify a large quantity of unlabeled images by typically harnessing scarce labeled images. Existing semi-supervised methods often suffer from inadequate classification accuracy when encountering difficult yet critical images, such as outliers, because they treat all unlabeled images equally and conduct classifications in an imperfectly ordered sequence. In this paper, we employ the curriculum learning methodology by investigating the difficulty of classifying every unlabeled image. The reliability and the discriminability of these unlabeled images are particularly investigated for evaluating their difficulty. As a result, an optimized image sequence is generated during the iterative propagations, and the unlabeled images are logically classified from simple to difficult. Furthermore, since images are usually characterized by multiple visual feature descriptors, we associate each kind of features with a teacher, and design a multi-modal curriculum learning (MMCL) strategy to integrate the information from different feature modalities. In each propagation, each teacher analyzes the difficulties of the currently unlabeled images from its own modality viewpoint. A consensus is subsequently reached among all the teachers, determining the currently simplest images (i.e., a curriculum), which are to be reliably classified by the multi-modal learner. This well-organized propagation process leveraging multiple teachers and one learner enables our MMCL to outperform five state-of-the-art methods on eight popular image data sets.

  7. A supervised machine learning estimator for the non-linear matter power spectrum - SEMPS

    CERN Document Server

    Mohammed, Irshad

    2015-01-01

    In this article, we argue that models based on machine learning (ML) can be very effective in estimating the non-linear matter power spectrum ($P(k)$). We employ the prediction ability of the supervised ML algorithms to build an estimator for the $P(k)$. The estimator is trained on a set of cosmological models, and redshifts for which the $P(k)$ is known, and it learns to predict $P(k)$ for any other set. We review three ML algorithms -- Random Forest, Gradient Boosting Machines, and K-Nearest Neighbours -- and investigate their prime parameters to optimize the prediction accuracy of the estimator. We also compute an optimal size of the training set, which is realistic enough, and still yields high accuracy. We find that, employing the optimal values of the internal parameters, a set of $50-100$ cosmological models is enough to train the estimator that can predict the $P(k)$ for a wide range of cosmological models, and redshifts. Using this configuration, we build a blackbox -- Supervised Estimator for Matter...

  8. AcceleRater: a web application for supervised learning of behavioral modes from acceleration measurements.

    Science.gov (United States)

    Resheff, Yehezkel S; Rotics, Shay; Harel, Roi; Spiegel, Orr; Nathan, Ran

    2014-01-01

    The study of animal movement is experiencing rapid progress in recent years, forcefully driven by technological advancement. Biologgers with Acceleration (ACC) recordings are becoming increasingly popular in the fields of animal behavior and movement ecology, for estimating energy expenditure and identifying behavior, with prospects for other potential uses as well. Supervised learning of behavioral modes from acceleration data has shown promising results in many species, and for a diverse range of behaviors. However, broad implementation of this technique in movement ecology research has been limited due to technical difficulties and complicated analysis, deterring many practitioners from applying this approach. This highlights the need to develop a broadly applicable tool for classifying behavior from acceleration data. Here we present a free-access python-based web application called AcceleRater, for rapidly training, visualizing and using models for supervised learning of behavioral modes from ACC measurements. We introduce AcceleRater, and illustrate its successful application for classifying vulture behavioral modes from acceleration data obtained from free-ranging vultures. The seven models offered in the AcceleRater application achieved overall accuracy of between 77.68% (Decision Tree) and 84.84% (Artificial Neural Network), with a mean overall accuracy of 81.51% and standard deviation of 3.95%. Notably, variation in performance was larger between behavioral modes than between models. AcceleRater provides the means to identify animal behavior, offering a user-friendly tool for ACC-based behavioral annotation, which will be dynamically upgraded and maintained.

  9. Semi-supervised eigenvectors for large-scale locally-biased learning

    DEFF Research Database (Denmark)

    Hansen, Toke Jansen; Mahoney, Michael W.

    2014-01-01

    In many applications, one has side information, e.g., labels that are provided in a semi-supervised manner, about a specific target region of a large data set, and one wants to perform machine learning and data analysis tasks nearby that prespecified target region. For example, one might...... machine learning and data analysis tools. At root, the reason is that eigenvectors are inherently global quantities, thus limiting the applicability of eigenvector-based methods in situations where one is interested in very local properties of the data. In this paper, we address this issue by providing...... be interested in the clustering structure of a data graph near a prespecified seed set of nodes, or one might be interested in finding partitions in an image that are near a prespecified ground truth set of pixels. Locally-biased problems of this sort are particularly challenging for popular eigenvector-based...

  10. Semi-supervised eigenvectors for large-scale locally-biased learning

    DEFF Research Database (Denmark)

    Hansen, Toke Jansen; Mahoney, Michael W.

    2014-01-01

    -based machine learning and data analysis tools. At root, the reason is that eigenvectors are inherently global quantities, thus limiting the applicability of eigenvector-based methods in situations where one is interested in very local properties of the data. In this paper, we address this issue by providing......In many applications, one has side information, e.g., labels that are provided in a semi-supervised manner, about a specific target region of a large data set, and one wants to perform machine learning and data analysis tasks nearby that prespecified target region. For example, one might...... be interested in the clustering structure of a data graph near a prespecified seed set of nodes, or one might be interested in finding partitions in an image that are near a prespecified ground truth set of pixels. Locally-biased problems of this sort are particularly challenging for popular eigenvector...

  11. Semi-supervised analysis of human brain tumours from partially labeled MRS information, using manifold learning models.

    Science.gov (United States)

    Cruz-Barbosa, Raúl; Vellido, Alfredo

    2011-02-01

    Medical diagnosis can often be understood as a classification problem. In oncology, this typically involves differentiating between tumour types and grades, or some type of discrete outcome prediction. From the viewpoint of computer-based medical decision support, this classification requires the availability of accurate diagnoses of past cases as training target examples. The availability of such labeled databases is scarce in most areas of oncology, and especially so in neuro-oncology. In such context, semi-supervised learning oriented towards classification can be a sensible data modeling choice. In this study, semi-supervised variants of Generative Topographic Mapping, a model of the manifold learning family, are applied to two neuro-oncology problems: the diagnostic discrimination between different brain tumour pathologies, and the prediction of outcomes for a specific type of aggressive brain tumours. Their performance compared favorably with those of the alternative Laplacian Eigenmaps and Semi-Supervised SVM for Manifold Learning models in most of the experiments.

  12. Prototype-based models in machine learning

    NARCIS (Netherlands)

    Biehl, Michael; Hammer, Barbara; Villmann, Thomas

    2016-01-01

    An overview is given of prototype-based models in machine learning. In this framework, observations, i.e., data, are stored in terms of typical representatives. Together with a suitable measure of similarity, the systems can be employed in the context of unsupervised and supervised analysis of poten

  13. Prototype-based models in machine learning

    NARCIS (Netherlands)

    Biehl, Michael; Hammer, Barbara; Villmann, Thomas

    2016-01-01

    An overview is given of prototype-based models in machine learning. In this framework, observations, i.e., data, are stored in terms of typical representatives. Together with a suitable measure of similarity, the systems can be employed in the context of unsupervised and supervised analysis of

  14. Hearing in a shoe-box : binaural source position and wall absorption estimation using virtually supervised learning

    OpenAIRE

    Kataria, Saurabh; Gaultier, Clément; Deleforge, Antoine

    2016-01-01

    This paper introduces a new framework for supervised sound source localization referred to as virtually-supervised learning. An acoustic shoe-box room simulator is used to generate a large number of binaural single-source audio scenes. These scenes are used to build a dataset of spatial binaural features annotated with acoustic properties such as the 3D source position and the walls' absorption coefficients. A probabilis-tic high-to low-dimensional regression framework is used to learn a mapp...

  15. mlpy: Machine Learning Python

    CERN Document Server

    Albanese, Davide; Merler, Stefano; Riccadonna, Samantha; Jurman, Giuseppe; Furlanello, Cesare

    2012-01-01

    mlpy is a Python Open Source Machine Learning library built on top of NumPy/SciPy and the GNU Scientific Libraries. mlpy provides a wide range of state-of-the-art machine learning methods for supervised and unsupervised problems and it is aimed at finding a reasonable compromise among modularity, maintainability, reproducibility, usability and efficiency. mlpy is multiplatform, it works with Python 2 and 3 and it is distributed under GPL3 at the website http://mlpy.fbk.eu.

  16. mlpy: Machine Learning Python

    OpenAIRE

    Albanese, Davide; Visintainer, Roberto; Merler, Stefano; Riccadonna, Samantha; Jurman, Giuseppe; Furlanello, Cesare

    2012-01-01

    mlpy is a Python Open Source Machine Learning library built on top of NumPy/SciPy and the GNU Scientific Libraries. mlpy provides a wide range of state-of-the-art machine learning methods for supervised and unsupervised problems and it is aimed at finding a reasonable compromise among modularity, maintainability, reproducibility, usability and efficiency. mlpy is multiplatform, it works with Python 2 and 3 and it is distributed under GPL3 at the website http://mlpy.fbk.eu.

  17. A new semi-supervised classification strategy combining active learning and spectral unmixing of hyperspectral data

    Science.gov (United States)

    Sun, Yanli; Zhang, Xia; Plaza, Antonio; Li, Jun; Dópido, Inmaculada; Liu, Yi

    2016-10-01

    Hyperspectral remote sensing allows for the detailed analysis of the surface of the Earth by providing high-dimensional images with hundreds of spectral bands. Hyperspectral image classification plays a significant role in hyperspectral image analysis and has been a very active research area in the last few years. In the context of hyperspectral image classification, supervised techniques (which have achieved wide acceptance) must address a difficult task due to the unbalance between the high dimensionality of the data and the limited availability of labeled training samples in real analysis scenarios. While the collection of labeled samples is generally difficult, expensive, and time-consuming, unlabeled samples can be generated in a much easier way. Semi-supervised learning offers an effective solution that can take advantage of both unlabeled and a small amount of labeled samples. Spectral unmixing is another widely used technique in hyperspectral image analysis, developed to retrieve pure spectral components and determine their abundance fractions in mixed pixels. In this work, we propose a method to perform semi-supervised hyperspectral image classification by combining the information retrieved with spectral unmixing and classification. Two kinds of samples that are highly mixed in nature are automatically selected, aiming at finding the most informative unlabeled samples. One kind is given by the samples minimizing the distance between the first two most probable classes by calculating the difference between the two highest abundances. Another kind is given by the samples minimizing the distance between the most probable class and the least probable class, obtained by calculating the difference between the highest and lowest abundances. The effectiveness of the proposed method is evaluated using a real hyperspectral data set collected by the airborne visible infrared imaging spectrometer (AVIRIS) over the Indian Pines region in Northwestern Indiana. In the

  18. Literature mining of protein-residue associations with graph rules learned through distant supervision

    Directory of Open Access Journals (Sweden)

    Ravikumar KE

    2012-10-01

    Full Text Available Abstract Background We propose a method for automatic extraction of protein-specific residue mentions from the biomedical literature. The method searches text for mentions of amino acids at specific sequence positions and attempts to correctly associate each mention with a protein also named in the text. The methods presented in this work will enable improved protein functional site extraction from articles, ultimately supporting protein function prediction. Our method made use of linguistic patterns for identifying the amino acid residue mentions in text. Further, we applied an automated graph-based method to learn syntactic patterns corresponding to protein-residue pairs mentioned in the text. We finally present an approach to automated construction of relevant training and test data using the distant supervision model. Results The performance of the method was assessed by extracting protein-residue relations from a new automatically generated test set of sentences containing high confidence examples found using distant supervision. It achieved a F-measure of 0.84 on automatically created silver corpus and 0.79 on a manually annotated gold data set for this task, outperforming previous methods. Conclusions The primary contributions of this work are to (1 demonstrate the effectiveness of distant supervision for automatic creation of training data for protein-residue relation extraction, substantially reducing the effort and time involved in manual annotation of a data set and (2 show that the graph-based relation extraction approach we used generalizes well to the problem of protein-residue association extraction. This work paves the way towards effective extraction of protein functional residues from the literature.

  19. Semi-supervised Graph Embedding Approach to Dynamic Link Prediction

    CERN Document Server

    Hisano, Ryohei

    2016-01-01

    We propose a simple discrete time semi-supervised graph embedding approach to link prediction in dynamic networks. The learned embedding reflects information from both the temporal and cross-sectional network structures, which is performed by defining the loss function as a weighted sum of the supervised loss from past dynamics and the unsupervised loss of predicting the neighborhood context in the current network. Our model is also capable of learning different embeddings for both formation and dissolution dynamics. These key aspects contributes to the predictive performance of our model and we provide experiments with three real--world dynamic networks showing that our method is comparable to state of the art methods in link formation prediction and outperforms state of the art baseline methods in link dissolution prediction.

  20. Supervised learning classification models for prediction of plant virus encoded RNA silencing suppressors.

    Directory of Open Access Journals (Sweden)

    Zeenia Jagga

    Full Text Available Viral encoded RNA silencing suppressor proteins interfere with the host RNA silencing machinery, facilitating viral infection by evading host immunity. In plant hosts, the viral proteins have several basic science implications and biotechnology applications. However in silico identification of these proteins is limited by their high sequence diversity. In this study we developed supervised learning based classification models for plant viral RNA silencing suppressor proteins in plant viruses. We developed four classifiers based on supervised learning algorithms: J48, Random Forest, LibSVM and Naïve Bayes algorithms, with enriched model learning by correlation based feature selection. Structural and physicochemical features calculated for experimentally verified primary protein sequences were used to train the classifiers. The training features include amino acid composition; auto correlation coefficients; composition, transition, and distribution of various physicochemical properties; and pseudo amino acid composition. Performance analysis of predictive models based on 10 fold cross-validation and independent data testing revealed that the Random Forest based model was the best and achieved 86.11% overall accuracy and 86.22% balanced accuracy with a remarkably high area under the Receivers Operating Characteristic curve of 0.95 to predict viral RNA silencing suppressor proteins. The prediction models for plant viral RNA silencing suppressors can potentially aid identification of novel viral RNA silencing suppressors, which will provide valuable insights into the mechanism of RNA silencing and could be further explored as potential targets for designing novel antiviral therapeutics. Also, the key subset of identified optimal features may help in determining compositional patterns in the viral proteins which are important determinants for RNA silencing suppressor activities. The best prediction model developed in the study is available as a

  1. Unsupervised classification of remote multispectral sensing data

    Science.gov (United States)

    Su, M. Y.

    1972-01-01

    The new unsupervised classification technique for classifying multispectral remote sensing data which can be either from the multispectral scanner or digitized color-separation aerial photographs consists of two parts: (a) a sequential statistical clustering which is a one-pass sequential variance analysis and (b) a generalized K-means clustering. In this composite clustering technique, the output of (a) is a set of initial clusters which are input to (b) for further improvement by an iterative scheme. Applications of the technique using an IBM-7094 computer on multispectral data sets over Purdue's Flight Line C-1 and the Yellowstone National Park test site have been accomplished. Comparisons between the classification maps by the unsupervised technique and the supervised maximum liklihood technique indicate that the classification accuracies are in agreement.

  2. Supervised neural network modeling: an empirical investigation into learning from imbalanced data with labeling errors.

    Science.gov (United States)

    Khoshgoftaar, Taghi M; Van Hulse, Jason; Napolitano, Amri

    2010-05-01

    Neural network algorithms such as multilayer perceptrons (MLPs) and radial basis function networks (RBFNets) have been used to construct learners which exhibit strong predictive performance. Two data related issues that can have a detrimental impact on supervised learning initiatives are class imbalance and labeling errors (or class noise). Imbalanced data can make it more difficult for the neural network learning algorithms to distinguish between examples of the various classes, and class noise can lead to the formulation of incorrect hypotheses. Both class imbalance and labeling errors are pervasive problems encountered in a wide variety of application domains. Many studies have been performed to investigate these problems in isolation, but few have focused on their combined effects. This study presents a comprehensive empirical investigation using neural network algorithms to learn from imbalanced data with labeling errors. In particular, the first component of our study investigates the impact of class noise and class imbalance on two common neural network learning algorithms, while the second component considers the ability of data sampling (which is commonly used to address the issue of class imbalance) to improve their performances. Our results, for which over two million models were trained and evaluated, show that conclusions drawn using the more commonly studied C4.5 classifier may not apply when using neural networks.

  3. SimNest: Social Media Nested Epidemic Simulation via Online Semi-supervised Deep Learning.

    Science.gov (United States)

    Zhao, Liang; Chen, Jiangzhuo; Chen, Feng; Wang, Wei; Lu, Chang-Tien; Ramakrishnan, Naren

    2015-11-01

    Infectious disease epidemics such as influenza and Ebola pose a serious threat to global public health. It is crucial to characterize the disease and the evolution of the ongoing epidemic efficiently and accurately. Computational epidemiology can model the disease progress and underlying contact network, but suffers from the lack of real-time and fine-grained surveillance data. Social media, on the other hand, provides timely and detailed disease surveillance, but is insensible to the underlying contact network and disease model. This paper proposes a novel semi-supervised deep learning framework that integrates the strengths of computational epidemiology and social media mining techniques. Specifically, this framework learns the social media users' health states and intervention actions in real time, which are regularized by the underlying disease model and contact network. Conversely, the learned knowledge from social media can be fed into computational epidemic model to improve the efficiency and accuracy of disease diffusion modeling. We propose an online optimization algorithm to substantialize the above interactive learning process iteratively to achieve a consistent stage of the integration. The extensive experimental results demonstrated that our approach can effectively characterize the spatio-temporal disease diffusion, outperforming competing methods by a substantial margin on multiple metrics.

  4. Semi-supervised learning of causal relations in biomedical scientific discourse

    Science.gov (United States)

    2014-01-01

    Background The increasing number of daily published articles in the biomedical domain has become too large for humans to handle on their own. As a result, bio-text mining technologies have been developed to improve their workload by automatically analysing the text and extracting important knowledge. Specific bio-entities, bio-events between these and facts can now be recognised with sufficient accuracy and are widely used by biomedical researchers. However, understanding how the extracted facts are connected in text is an extremely difficult task, which cannot be easily tackled by machinery. Results In this article, we describe our method to recognise causal triggers and their arguments in biomedical scientific discourse. We introduce new features and show that a self-learning approach improves the performance obtained by supervised machine learners to 83.47% for causal triggers. Furthermore, the spans of causal arguments can be recognised to a slightly higher level that by using supervised or rule-based methods that have been employed before. Conclusion Exploiting the large amount of unlabelled data that is already available can help improve the performance of recognising causal discourse relations in the biomedical domain. This improvement will further benefit the development of multiple tasks, such as hypothesis generation for experimental laboratories, contradiction detection, and the creation of causal networks. PMID:25559746

  5. A Novel Semi-Supervised Electronic Nose Learning Technique: M-Training

    Directory of Open Access Journals (Sweden)

    Pengfei Jia

    2016-03-01

    Full Text Available When an electronic nose (E-nose is used to distinguish different kinds of gases, the label information of the target gas could be lost due to some fault of the operators or some other reason, although this is not expected. Another fact is that the cost of getting the labeled samples is usually higher than for unlabeled ones. In most cases, the classification accuracy of an E-nose trained using labeled samples is higher than that of the E-nose trained by unlabeled ones, so gases without label information should not be used to train an E-nose, however, this wastes resources and can even delay the progress of research. In this work a novel multi-class semi-supervised learning technique called M-training is proposed to train E-noses with both labeled and unlabeled samples. We employ M-training to train the E-nose which is used to distinguish three indoor pollutant gases (benzene, toluene and formaldehyde. Data processing results prove that the classification accuracy of E-nose trained by semi-supervised techniques (tri-training and M-training is higher than that of an E-nose trained only with labeled samples, and the performance of M-training is better than that of tri-training because more base classifiers can be employed by M-training.

  6. Using distant supervised learning to identify protein subcellular localizations from full-text scientific articles.

    Science.gov (United States)

    Zheng, Wu; Blake, Catherine

    2015-10-01

    Databases of curated biomedical knowledge, such as the protein-locations reflected in the UniProtKB database, provide an accurate and useful resource to researchers and decision makers. Our goal is to augment the manual efforts currently used to curate knowledge bases with automated approaches that leverage the increased availability of full-text scientific articles. This paper describes experiments that use distant supervised learning to identify protein subcellular localizations, which are important to understand protein function and to identify candidate drug targets. Experiments consider Swiss-Prot, the manually annotated subset of the UniProtKB protein knowledge base, and 43,000 full-text articles from the Journal of Biological Chemistry that contain just under 11.5 million sentences. The system achieves 0.81 precision and 0.49 recall at sentence level and an accuracy of 57% on held-out instances in a test set. Moreover, the approach identifies 8210 instances that are not in the UniProtKB knowledge base. Manual inspection of the 50 most likely relations showed that 41 (82%) were valid. These results have immediate benefit to researchers interested in protein function, and suggest that distant supervision should be explored to complement other manual data curation efforts.

  7. Non-Supervised Learning for Spread Spectrum Signal Pseudo-Noise Sequence Acquisition

    Institute of Scientific and Technical Information of China (English)

    Hao Cheng; Na Yu,; Tai-Jun Wang

    2015-01-01

    Abstract¾An idea of estimating the direct sequence spread spectrum (DSSS) signal pseudo-noise (PN) sequence is presented. Without the apriority knowledge about the DSSS signal in the non-cooperation condition, we propose a self-organizing feature map (SOFM) neural network algorithm to detect and identify the PN sequence. A non-supervised learning algorithm is proposed according the Kohonen rule in SOFM. The blind algorithm can also estimate the PN sequence in a low signal-to-noise (SNR) and computer simulation demonstrates that the algorithm is effective. Compared with the traditional correlation algorithm based on slip-correlation, the proposed algorithm’s bit error rate (BER) and complexity are lower.

  8. Classification of Autism Spectrum Disorder Using Supervised Learning of Brain Connectivity Measures Extracted from Synchrostates

    CERN Document Server

    Jamal, Wasifa; Oprescu, Ioana-Anastasia; Maharatna, Koushik; Apicella, Fabio; Sicca, Federico

    2014-01-01

    Objective. The paper investigates the presence of autism using the functional brain connectivity measures derived from electro-encephalogram (EEG) of children during face perception tasks. Approach. Phase synchronized patterns from 128-channel EEG signals are obtained for typical children and children with autism spectrum disorder (ASD). The phase synchronized states or synchrostates temporally switch amongst themselves as an underlying process for the completion of a particular cognitive task. We used 12 subjects in each group (ASD and typical) for analyzing their EEG while processing fearful, happy and neutral faces. The minimal and maximally occurring synchrostates for each subject are chosen for extraction of brain connectivity features, which are used for classification between these two groups of subjects. Among different supervised learning techniques, we here explored the discriminant analysis and support vector machine both with polynomial kernels for the classification task. Main results. The leave ...

  9. Exhaustive and Efficient Constraint Propagation: A Semi-Supervised Learning Perspective and Its Applications

    CERN Document Server

    Lu, Zhiwu; Peng, Yuxin

    2011-01-01

    This paper presents a novel pairwise constraint propagation approach by decomposing the challenging constraint propagation problem into a set of independent semi-supervised learning subproblems which can be solved in quadratic time using label propagation based on k-nearest neighbor graphs. Considering that this time cost is proportional to the number of all possible pairwise constraints, our approach actually provides an efficient solution for exhaustively propagating pairwise constraints throughout the entire dataset. The resulting exhaustive set of propagated pairwise constraints are further used to adjust the similarity matrix for constrained spectral clustering. Other than the traditional constraint propagation on single-source data, our approach is also extended to more challenging constraint propagation on multi-source data where each pairwise constraint is defined over a pair of data points from different sources. This multi-source constraint propagation has an important application to cross-modal mul...

  10. Anxiety, supervision and a space for thinking: some narcissistic perils for clinical psychologists in learning psychotherapy.

    Science.gov (United States)

    Mollon, P

    1989-06-01

    The process of learning psychotherapy involves narcissistic dangers--there may be injuries to self-esteem and self-image, especially when working with certain kinds of disturbed and hostile patients. Some patients will unconsciously recreate, in the transference, representations of early damaging experiences with parents, but now reversed with the therapist as the victim. It is vital for the trainee to be helped to understand these powerful interactional pressures. There are aspects of the professional culture and ideals of clinical psychologists (and possibly of some psychiatrists and social workers as well) which may make them particularly vulnerable in work with the hostile patient. It is argued that the function of supervision is not to teach a technique directly, but to create a 'space for thinking'--a kind of thinking which is more akin to maternal reverie, as described by Bion, than problem solving.

  11. Unsupervised grammar induction of clinical report sublanguage

    Directory of Open Access Journals (Sweden)

    Kate Rohit J

    2012-10-01

    Full Text Available Abstract Background Clinical reports are written using a subset of natural language while employing many domain-specific terms; such a language is also known as a sublanguage for a scientific or a technical domain. Different genres of clinical reports use different sublaguages, and in addition, different medical facilities use different medical language conventions. This makes supervised training of a parser for clinical sentences very difficult as it would require expensive annotation effort to adapt to every type of clinical text. Methods In this paper, we present an unsupervised method which automatically induces a grammar and a parser for the sublanguage of a given genre of clinical reports from a corpus with no annotations. In order to capture sentence structures specific to clinical domains, the grammar is induced in terms of semantic classes of clinical terms in addition to part-of-speech tags. Our method induces grammar by minimizing the combined encoding cost of the grammar and the corresponding sentence derivations. The probabilities for the productions of the induced grammar are then learned from the unannotated corpus using an instance of the expectation-maximization algorithm. Results Our experiments show that the induced grammar is able to parse novel sentences. Using a dataset of discharge summary sentences with no annotations, our method obtains 60.5% F-measure for parse-bracketing on sentences of maximum length 10. By varying a parameter, the method can induce a range of grammars, from very specific to very general, and obtains the best performance in between the two extremes.

  12. A two-stage unsupervised learning algorithm reproduces multisensory enhancement in a neural network model of the corticotectal system.

    Science.gov (United States)

    Anastasio, Thomas J; Patton, Paul E

    2003-07-30

    Multisensory enhancement (MSE) is the augmentation of the response to sensory stimulation of one modality by stimulation of a different modality. It has been described for multisensory neurons in the deep superior colliculus (DSC) of mammals, which function to detect, and direct orienting movements toward, the sources of stimulation (targets). MSE would seem to improve the ability of DSC neurons to detect targets, but many mammalian DSC neurons are unimodal. MSE requires descending input to DSC from certain regions of parietal cortex. Paradoxically, the descending projections necessary for MSE originate from unimodal cortical neurons. MSE, and the puzzling findings associated with it, can be simulated using a model of the corticotectal system. In the model, a network of DSC units receives primary sensory input that can be augmented by modulatory cortical input. Connection weights from primary and modulatory inputs are trained in stages one (Hebb) and two (Hebb-anti-Hebb), respectively, of an unsupervised two-stage algorithm. Two-stage training causes DSC units to extract information concerning simulated targets from their inputs. It also causes the DSC to develop a mixture of unimodal and multisensory units. The percentage of DSC multisensory units is determined by the proportion of cross-modal targets and by primary input ambiguity. Multisensory DSC units develop MSE, which depends on unimodal modulatory connections. Removal of the modulatory influence greatly reduces MSE but has little effect on DSC unit responses to stimuli of a single modality. The correspondence between model and data suggests that two-stage training captures important features of self-organization in the real corticotectal system.

  13. An unsupervised machine learning method for delineating stratum corneum in reflectance confocal microscopy stacks of human skin in vivo

    Science.gov (United States)

    Bozkurt, Alican; Kose, Kivanc; Fox, Christi A.; Dy, Jennifer; Brooks, Dana H.; Rajadhyaksha, Milind

    2016-02-01

    Study of the stratum corneum (SC) in human skin is important for research in barrier structure and function, drug delivery, and water permeability of skin. The optical sectioning and high resolution of reflectance confocal microscopy (RCM) allows visual examination of SC non-invasively. Here, we present an unsupervised segmentation algorithm that can automatically delineate thickness of the SC in RCM images of human skin in-vivo. We mimic clinicians visual process by applying complex wavelet transform over non-overlapping local regions of size 16 x 16 μm called tiles, and analyze the textural changes in between consecutive tiles in axial (depth) direction. We use dual-tree complex wavelet transform to represent textural structures in each tile. This transform is almost shift-invariant, and directionally selective, which makes it highly efficient in texture representation. Using DT-CWT, we decompose each tile into 6 directional sub-bands with orientations in +/-15, 45, and 75 degrees and a low-pass band, which is the decimated version of the input. We apply 3 scales of decomposition by recursively transforming the low-pass bands and obtain 18 bands of different directionality at different scales. We then calculate mean and variance of each band resulting in a feature vector of 36 entries. Feature vectors obtained for each stack of tiles in axial direction are then clustered using spectral clustering in order to detect the textural changes in depth direction. Testing on a set of 15 RCM stacks produced a mean error of 5.45+/-1.32 μm, compared to the "ground truth" segmentation provided by a clinical expert reader.

  14. Response monitoring using quantitative ultrasound methods and supervised dictionary learning in locally advanced breast cancer

    Science.gov (United States)

    Gangeh, Mehrdad J.; Fung, Brandon; Tadayyon, Hadi; Tran, William T.; Czarnota, Gregory J.

    2016-03-01

    A non-invasive computer-aided-theragnosis (CAT) system was developed for the early assessment of responses to neoadjuvant chemotherapy in patients with locally advanced breast cancer. The CAT system was based on quantitative ultrasound spectroscopy methods comprising several modules including feature extraction, a metric to measure the dissimilarity between "pre-" and "mid-treatment" scans, and a supervised learning algorithm for the classification of patients to responders/non-responders. One major requirement for the successful design of a high-performance CAT system is to accurately measure the changes in parametric maps before treatment onset and during the course of treatment. To this end, a unified framework based on Hilbert-Schmidt independence criterion (HSIC) was used for the design of feature extraction from parametric maps and the dissimilarity measure between the "pre-" and "mid-treatment" scans. For the feature extraction, HSIC was used to design a supervised dictionary learning (SDL) method by maximizing the dependency between the scans taken from "pre-" and "mid-treatment" with "dummy labels" given to the scans. For the dissimilarity measure, an HSIC-based metric was employed to effectively measure the changes in parametric maps as an indication of treatment effectiveness. The HSIC-based feature extraction and dissimilarity measure used a kernel function to nonlinearly transform input vectors into a higher dimensional feature space and computed the population means in the new space, where enhanced group separability was ideally obtained. The results of the classification using the developed CAT system indicated an improvement of performance compared to a CAT system with basic features using histogram of intensity.

  15. A semi-supervised learning framework for biomedical event extraction based on hidden topics.

    Science.gov (United States)

    Zhou, Deyu; Zhong, Dayou

    2015-05-01

    Scientists have devoted decades of efforts to understanding the interaction between proteins or RNA production. The information might empower the current knowledge on drug reactions or the development of certain diseases. Nevertheless, due to the lack of explicit structure, literature in life science, one of the most important sources of this information, prevents computer-based systems from accessing. Therefore, biomedical event extraction, automatically acquiring knowledge of molecular events in research articles, has attracted community-wide efforts recently. Most approaches are based on statistical models, requiring large-scale annotated corpora to precisely estimate models' parameters. However, it is usually difficult to obtain in practice. Therefore, employing un-annotated data based on semi-supervised learning for biomedical event extraction is a feasible solution and attracts more interests. In this paper, a semi-supervised learning framework based on hidden topics for biomedical event extraction is presented. In this framework, sentences in the un-annotated corpus are elaborately and automatically assigned with event annotations based on their distances to these sentences in the annotated corpus. More specifically, not only the structures of the sentences, but also the hidden topics embedded in the sentences are used for describing the distance. The sentences and newly assigned event annotations, together with the annotated corpus, are employed for training. Experiments were conducted on the multi-level event extraction corpus, a golden standard corpus. Experimental results show that more than 2.2% improvement on F-score on biomedical event extraction is achieved by the proposed framework when compared to the state-of-the-art approach. The results suggest that by incorporating un-annotated data, the proposed framework indeed improves the performance of the state-of-the-art event extraction system and the similarity between sentences might be precisely

  16. Entry-Level Technical Skills That Teachers Expected Students to Learn through Supervised Agricultural Experiences (SAEs): A Modified Delphi Study

    Science.gov (United States)

    Ramsey, Jon W.; Edwards, M. Craig

    2012-01-01

    Supervised experiences are designed to provide opportunities for the hands-on learning of skills and practices that lead to successful personal growth and future employment in an agricultural career (Talbert, Vaughn, Croom, & Lee, 2007). In the Annual Report for Agricultural Education (2005-2006), it was stated that 91% of the respondents…

  17. Just How Much Can School Pupils Learn from School Gardening? A Study of Two Supervised Agricultural Experience Approaches in Uganda

    Science.gov (United States)

    Okiror, John James; Matsiko, Biryabaho Frank; Oonyu, Joseph

    2011-01-01

    School systems in Africa are short of skills that link well with rural communities, yet arguments to vocationalize curricula remain mixed and school agriculture lacks the supervised practical component. This study, conducted in eight primary (elementary) schools in Uganda, sought to compare the learning achievement of pupils taught using…

  18. Teaching the computer to code frames in news: comparing two supervised machine learning approaches to frame analysis

    NARCIS (Netherlands)

    Burscher, B.; Odijk, D.; Vliegenthart, R.; de Rijke, M.; de Vreese, C.H.

    2014-01-01

    We explore the application of supervised machine learning (SML) to frame coding. By automating the coding of frames in news, SML facilitates the incorporation of large-scale content analysis into framing research, even if financial resources are scarce. This furthers a more integrated investigation

  19. Teaching the computer to code frames in news: comparing two supervised machine learning approaches to frame analysis

    NARCIS (Netherlands)

    Burscher, B.; Odijk, D.; Vliegenthart, R.; de Rijke, M.; de Vreese, C.H.

    2014-01-01

    We explore the application of supervised machine learning (SML) to frame coding. By automating the coding of frames in news, SML facilitates the incorporation of large-scale content analysis into framing research, even if financial resources are scarce. This furthers a more integrated investigation

  20. Entry-Level Technical Skills That Teachers Expected Students to Learn through Supervised Agricultural Experiences (SAEs): A Modified Delphi Study

    Science.gov (United States)

    Ramsey, Jon W.; Edwards, M. Craig

    2012-01-01

    Supervised experiences are designed to provide opportunities for the hands-on learning of skills and practices that lead to successful personal growth and future employment in an agricultural career (Talbert, Vaughn, Croom, & Lee, 2007). In the Annual Report for Agricultural Education (2005-2006), it was stated that 91% of the respondents (i.e.,…

  1. Collective Academic Supervision: A Model for Participation and Learning in Higher Education

    Science.gov (United States)

    Nordentoft, Helle Merete; Thomsen, Rie; Wichmann-Hansen, Gitte

    2013-01-01

    Supervision of graduate students is a core activity in higher education. Previous research on graduate supervision focuses on individual and relational aspects of the supervisory relationship rather than collective, pedagogical and methodological aspects of the supervision process. In presenting a collective model we have developed for academic…

  2. Visual Recognition by Learning From Web Data via Weakly Supervised Domain Generalization.

    Science.gov (United States)

    Niu, Li; Li, Wen; Xu, Dong; Cai, Jianfei

    2016-06-01

    In this paper, a weakly supervised domain generalization (WSDG) method is proposed for real-world visual recognition tasks, in which we train classifiers by using Web data (\\eg, Web images and Web videos) with noisy labels. In particular, two challenging problems need to be solved when learning robust classifiers, in which the first issue is to cope with the label noise of training Web data from the source domain, while the second issue is to enhance the generalization capability of learned classifiers to an arbitrary target domain. In order to handle the first problem, the training samples within each category are partitioned into clusters, where we use one bag to denote each cluster and instances to denote the samples in each cluster. Then, we identify a proportion of good training samples in each bag and train robust classifiers by using the good training samples, which leads to a multi-instance learning (MIL) problem. In order to handle the second problem, we assume that the training samples possibly form a set of hidden domains, with each hidden domain associated with a distinctive data distribution. Then, for each category and each hidden latent domain, we propose to learn one classifier by extending our MIL formulation, which leads to our WSDG approach. In the testing stage, our approach can obtain better generalization capability by effectively integrating multiple classifiers from different latent domains in each category. Moreover, our WSDG approach is further extended to utilize additional textual descriptions associated with Web data as privileged information (PI), although testing data do not have such PI. Extensive experiments on three benchmark data sets indicate that our newly proposed methods are effective for real-world visual recognition tasks by learning from Web data.

  3. Whither Supervision?

    Directory of Open Access Journals (Sweden)

    Duncan Waite

    2006-11-01

    Full Text Available This paper inquires if the school supervision is in decadence. Dr. Waite responds that the answer will depend on which perspective you look at it. Dr. Waite suggests taking in consideration three elements that are related: the field itself, the expert in the field (the professor, the theorist, the student and the administrator, and the context. When these three elements are revised, it emphasizes that there is not a consensus about the field of supervision, but there are coincidences related to its importance and that it is related to the improvement of the practice of the students in the school for their benefit. Dr. Waite suggests that the practice on this field is not always in harmony with what the theorists affirm. When referring to the supervisor or the skilled person, the author indicates that his or her perspective depends on his or her epistemological believes or in the way he or she conceives the learning; that is why supervision can be understood in different ways. About the context, Waite suggests that there have to be taken in consideration the social or external forces that influent the people and the society, because through them the education is affected. Dr. Waite concludes that the way to understand the supervision depends on the performer’s perspective. He responds to the initial question saying that the supervision authorities, the knowledge on this field, the performers, and its practice, are maybe spread but not extinct because the supervision will always be part of the great enterprise that we called education.

  4. The Mathematics of Divergence Based Online Learning in Vector Quantization

    NARCIS (Netherlands)

    Villmann, Thomas; Haase, Sven; Schleif, Frank-Michael; Hammer, Barbara; Biehl, Michael

    2010-01-01

    We propose the utilization of divergences in gradient descent learning of supervised and unsupervised vector quantization as an alternative for the squared Euclidean distance. The approach is based on the determination of the Fréchet-derivatives for the divergences, wich can be immediately plugged i

  5. Unsupervised segmentation with dynamical units.

    Science.gov (United States)

    Rao, A Ravishankar; Cecchi, Guillermo A; Peck, Charles C; Kozloski, James R

    2008-01-01

    In this paper, we present a novel network to separate mixtures of inputs that have been previously learned. A significant capability of the network is that it segments the components of each input object that most contribute to its classification. The network consists of amplitude-phase units that can synchronize their dynamics, so that separation is determined by the amplitude of units in an output layer, and segmentation by phase similarity between input and output layer units. Learning is unsupervised and based on a Hebbian update, and the architecture is very simple. Moreover, efficient segmentation can be achieved even when there is considerable superposition of the inputs. The network dynamics are derived from an objective function that rewards sparse coding in the generalized amplitude-phase variables. We argue that this objective function can provide a possible formal interpretation of the binding problem and that the implementation of the network architecture and dynamics is biologically plausible.

  6. Unsupervised learning of generative and discriminative weights encoding elementary image components in a predictive coding model of cortical function.

    Science.gov (United States)

    Spratling, M W

    2012-01-01

    A method is presented for learning the reciprocal feedforward and feedback connections required by the predictive coding model of cortical function. When this method is used, feedforward and feedback connections are learned simultaneously and independently in a biologically plausible manner. The performance of the proposed algorithm is evaluated by applying it to learning the elementary components of artificial and natural images. For artificial images, the bars problem is employed, and the proposed algorithm is shown to produce state-of-the-art performance on this task. For natural images, components resembling Gabor functions are learned in the first processing stage, and neurons responsive to corners are learned in the second processing stage. The properties of these learned representations are in good agreement with neurophysiological data from V1 and V2. The proposed algorithm demonstrates for the first time that a single computational theory can explain the formation of cortical RFs and also the response properties of cortical neurons once those RFs have been learned.

  7. Unsupervised Language Acquisition: Theory and Practice

    OpenAIRE

    Clark, Alexander

    2002-01-01

    In this thesis I present various algorithms for the unsupervised machine learning of aspects of natural languages using a variety of statistical models. The scientific object of the work is to examine the validity of the so-called Argument from the Poverty of the Stimulus advanced in favour of the proposition that humans have language-specific innate knowledge. I start by examining an a priori argument based on Gold's theorem, that purports to prove that natural languages cannot be learned, a...

  8. Unsupervised Language Acquisition: Theory and Practice

    OpenAIRE

    Clark, Alexander

    2002-01-01

    In this thesis I present various algorithms for the unsupervised machine learning of aspects of natural languages using a variety of statistical models. The scientific object of the work is to examine the validity of the so-called Argument from the Poverty of the Stimulus advanced in favour of the proposition that humans have language-specific innate knowledge. I start by examining an a priori argument based on Gold's theorem, that purports to prove that natural languages cannot be learned, a...

  9. Automated Quality Assessment of Structural Magnetic Resonance Brain Images Based on a Supervised Machine Learning Algorithm

    Directory of Open Access Journals (Sweden)

    Ricardo Andres Pizarro

    2016-12-01

    Full Text Available High-resolution three-dimensional magnetic resonance imaging (3D-MRI is being increasingly used to delineate morphological changes underlying neuropsychiatric disorders. Unfortunately, artifacts frequently compromise the utility of 3D-MRI yielding irreproducible results, from both type I and type II errors. It is therefore critical to screen 3D-MRIs for artifacts before use. Currently, quality assessment involves slice-wise visual inspection of 3D-MRI volumes, a procedure that is both subjective and time consuming. Automating the quality rating of 3D-MRI could improve the efficiency and reproducibility of the procedure. The present study is one of the first efforts to apply a support vector machine (SVM algorithm in the quality assessment of structural brain images, using global and region of interest (ROI automated image quality features developed in-house. SVM is a supervised machine-learning algorithm that can predict the category of test datasets based on the knowledge acquired from a learning dataset. The performance (accuracy of the automated SVM approach was assessed, by comparing the SVM-predicted quality labels to investigator-determined quality labels. The accuracy for classifying 1457 3D-MRI volumes from our database using the SVM approach is around 80%. These results are promising and illustrate the possibility of using SVM as an automated quality assessment tool for 3D-MRI.

  10. Automated detection of microaneurysms using scale-adapted blob analysis and semi-supervised learning.

    Science.gov (United States)

    Adal, Kedir M; Sidibé, Désiré; Ali, Sharib; Chaum, Edward; Karnowski, Thomas P; Mériaudeau, Fabrice

    2014-04-01

    Despite several attempts, automated detection of microaneurysm (MA) from digital fundus images still remains to be an open issue. This is due to the subtle nature of MAs against the surrounding tissues. In this paper, the microaneurysm detection problem is modeled as finding interest regions or blobs from an image and an automatic local-scale selection technique is presented. Several scale-adapted region descriptors are introduced to characterize these blob regions. A semi-supervised based learning approach, which requires few manually annotated learning examples, is also proposed to train a classifier which can detect true MAs. The developed system is built using only few manually labeled and a large number of unlabeled retinal color fundus images. The performance of the overall system is evaluated on Retinopathy Online Challenge (ROC) competition database. A competition performance measure (CPM) of 0.364 shows the competitiveness of the proposed system against state-of-the art techniques as well as the applicability of the proposed features to analyze fundus images.

  11. The effects of supervised learning on event-related potential correlates of music-syntactic processing.

    Science.gov (United States)

    Guo, Shuang; Koelsch, Stefan

    2015-11-11

    Humans process music even without conscious effort according to implicit knowledge about syntactic regularities. Whether such automatic and implicit processing is modulated by veridical knowledge has remained unknown in previous neurophysiological studies. This study investigates this issue by testing whether the acquisition of veridical knowledge of a music-syntactic irregularity (acquired through supervised learning) modulates early, partly automatic, music-syntactic processes (as reflected in the early right anterior negativity, ERAN), and/or late controlled processes (as reflected in the late positive component, LPC). Excerpts of piano sonatas with syntactically regular and less regular chords were presented repeatedly (10 times) to non-musicians and amateur musicians. Participants were informed by a cue as to whether the following excerpt contained a regular or less regular chord. Results showed that the repeated exposure to several presentations of regular and less regular excerpts did not influence the ERAN elicited by less regular chords. By contrast, amplitudes of the LPC (as well as of the P3a evoked by less regular chords) decreased systematically across learning trials. These results reveal that late controlled, but not early (partly automatic), neural mechanisms of music-syntactic processing are modulated by repeated exposure to a musical piece. This article is part of a Special Issue entitled SI: Prediction and Attention. Copyright © 2015 Elsevier B.V. All rights reserved.

  12. Unsupervised Spectral Mesh Segmentation Driven by Heterogeneous Graphs.

    Science.gov (United States)

    Theologou, Panagiotis; Pratikakis, Ioannis; Theoharis, Theoharis

    2017-02-01

    A fully automatic mesh segmentation scheme using heterogeneous graphs is presented. We introduce a spectral framework where local geometry affinities are coupled with surface patch affinities. A heterogeneous graph is constructed combining two distinct graphs: a weighted graph based on adjacency of patches of an initial over-segmentation, and the weighted dual mesh graph. The partitioning relies on processing each eigenvector of the heterogeneous graph Laplacian individually, taking into account the nodal set and nodal domain theory. Experiments on standard datasets show that the proposed unsupervised approach outperforms the state-of-the-art unsupervised methodologies and is comparable to the best supervised approaches.

  13. A framework to facilitate self-directed learning, assessment and supervision in midwifery practice: a qualitative study of supervisors' perceptions.

    Science.gov (United States)

    Embo, M; Driessen, E; Valcke, M; van der Vleuten, C P M

    2014-08-01

    Self-directed learning is an educational concept that has received increasing attention. The recent workplace literature, however, reports problems with the facilitation of self-directed learning in clinical practice. We developed the Midwifery Assessment and Feedback Instrument (MAFI) as a framework to facilitate self-directed learning. In the present study, we sought clinical supervisors' perceptions of the usefulness of MAFI. Interviews with fifteen clinical supervisors were audio taped, transcribed verbatim and analysed thematically using Atlas-Ti software for qualitative data analysis. Four themes emerged from the analysis. (1) The competency-based educational structure promotes the setting of realistic learning outcomes and a focus on competency development, (2) instructing students to write reflections facilitates student-centred supervision, (3) creating a feedback culture is necessary to achieve continuity in supervision and (4) integrating feedback and assessment might facilitate competency development under the condition that evidence is discussed during assessment meetings. Supervisors stressed the need for direct observation, and instruction how to facilitate a self-directed learning process. The MAFI appears to be a useful framework to promote self-directed learning in clinical practice. The effect can be advanced by creating a feedback and assessment culture where learners and supervisors share the responsibility for developing self-directed learning. Copyright © 2014 Elsevier Ltd. All rights reserved.

  14. Classification of autism spectrum disorder using supervised learning of brain connectivity measures extracted from synchrostates

    Science.gov (United States)

    Jamal, Wasifa; Das, Saptarshi; Oprescu, Ioana-Anastasia; Maharatna, Koushik; Apicella, Fabio; Sicca, Federico

    2014-08-01

    Objective. The paper investigates the presence of autism using the functional brain connectivity measures derived from electro-encephalogram (EEG) of children during face perception tasks. Approach. Phase synchronized patterns from 128-channel EEG signals are obtained for typical children and children with autism spectrum disorder (ASD). The phase synchronized states or synchrostates temporally switch amongst themselves as an underlying process for the completion of a particular cognitive task. We used 12 subjects in each group (ASD and typical) for analyzing their EEG while processing fearful, happy and neutral faces. The minimal and maximally occurring synchrostates for each subject are chosen for extraction of brain connectivity features, which are used for classification between these two groups of subjects. Among different supervised learning techniques, we here explored the discriminant analysis and support vector machine both with polynomial kernels for the classification task. Main results. The leave one out cross-validation of the classification algorithm gives 94.7% accuracy as the best performance with corresponding sensitivity and specificity values as 85.7% and 100% respectively. Significance. The proposed method gives high classification accuracies and outperforms other contemporary research results. The effectiveness of the proposed method for classification of autistic and typical children suggests the possibility of using it on a larger population to validate it for clinical practice.

  15. A Model for Detecting Tor Encrypted Traffic using Supervised Machine Learning

    Directory of Open Access Journals (Sweden)

    Alaeddin Almubayed

    2015-06-01

    Full Text Available Tor is the low-latency anonymity tool and one of the prevalent used open source anonymity tools for anonymizing TCP traffic on the Internet used by around 500,000 people every day. Tor protects user's privacy against surveillance and censorship by making it extremely difficult for an observer to correlate visited websites in the Internet with the real physical-world identity. Tor accomplished that by ensuring adequate protection of Tor traffic against traffic analysis and feature extraction techniques. Further, Tor ensures anti-website fingerprinting by implementing different defences like TLS encryption, padding, and packet relaying. However, in this paper, an analysis has been performed against Tor from a local observer in order to bypass Tor protections; the method consists of a feature extraction from a local network dataset. Analysis shows that it's still possible for a local observer to fingerprint top monitored sites on Alexa and Tor traffic can be classified amongst other HTTPS traffic in the network despite the use of Tor's protections. In the experiment, several supervised machine-learning algorithms have been employed. The attack assumes a local observer sitting on a local network fingerprinting top 100 sites on Alexa; results gave an improvement amongst previous results by achieving an accuracy of 99.64% and 0.01% false positive.

  16. An Adaptive Privacy Protection Method for Smart Home Environments Using Supervised Learning

    Directory of Open Access Journals (Sweden)

    Jingsha He

    2017-03-01

    Full Text Available In recent years, smart home technologies have started to be widely used, bringing a great deal of convenience to people’s daily lives. At the same time, privacy issues have become particularly prominent. Traditional encryption methods can no longer meet the needs of privacy protection in smart home applications, since attacks can be launched even without the need for access to the cipher. Rather, attacks can be successfully realized through analyzing the frequency of radio signals, as well as the timestamp series, so that the daily activities of the residents in the smart home can be learnt. Such types of attacks can achieve a very high success rate, making them a great threat to users’ privacy. In this paper, we propose an adaptive method based on sample data analysis and supervised learning (SDASL, to hide the patterns of daily routines of residents that would adapt to dynamically changing network loads. Compared to some existing solutions, our proposed method exhibits advantages such as low energy consumption, low latency, strong adaptability, and effective privacy protection.

  17. How to measure metallicity from five-band photometry with supervised machine learning algorithms

    CERN Document Server

    Acquaviva, Viviana

    2015-01-01

    We demonstrate that it is possible to measure metallicity from the SDSS five-band photometry to better than 0.1 dex using supervised machine learning algorithms. Using spectroscopic estimates of metallicity as ground truth, we build, optimize and train several estimators to predict metallicity. We use the observed photometry, as well as derived quantities such as stellar mass and photometric redshift, as features, and we build two sample data sets at median redshifts of 0.103 and 0.218 and median r-band magnitude of 17.5 and 18.3 respectively. We find that ensemble methods, such as Random Forests of Trees and Extremely Randomized Trees, and Support Vector Machines all perform comparably well and can measure metallicity with a Root Mean Square Error (RMSE) of 0.081 and 0.090 for the two data sets when all objects are included. The fraction of outliers (objects for which the difference between true and predicted metallicity is larger than 0.2 dex) is only 2.2 and 3.9% respectively, and the RMSE decreases to 0.0...

  18. Supervised Learning Detection of Sixty Non-transiting Hot Jupiter Candidates

    Science.gov (United States)

    Millholland, Sarah; Laughlin, Gregory

    2017-09-01

    The optical full-phase photometric variations of a short-period planet provide a unique view of the planet’s atmospheric composition and dynamics. The number of planets with optical phase curve detections, however, is currently too small to study them as an aggregate population, motivating an extension of the search to non-transiting planets. Here we present an algorithm for the detection of non-transiting short-period giant planets in the Kepler field. The procedure uses the phase curves themselves as evidence for the planets’ existence. We employ a supervised learning algorithm to recognize the salient time-dependent properties of synthetic phase curves; we then search for detections of signals that match these properties. After demonstrating the algorithm’s capabilities, we classify 142,630 FGK Kepler stars without confirmed planets or Kepler Objects of Interest, and for each one, we assign a probability of a phase curve of a non-transiting planet being present. We identify 60 high-probability non-transiting hot Jupiter candidates. We also derive constraints on the candidates’ albedos and offsets of the phase curve maxima. These targets are strong candidates for follow-up radial velocity confirmation and characterization. Once confirmed, the atmospheric information content in the phase curves may be studied in yet greater detail.

  19. Distributed multisensory integration in a recurrent network model through supervised learning

    Science.gov (United States)

    Wang, He; Wong, K. Y. Michael

    Sensory integration between different modalities has been extensively studied. It is suggested that the brain integrates signals from different modalities in a Bayesian optimal way. However, how the Bayesian rule is implemented in a neural network remains under debate. In this work we propose a biologically plausible recurrent network model, which can perform Bayesian multisensory integration after trained by supervised learning. Our model is composed of two modules, each for one modality. We assume that each module is a recurrent network, whose activity represents the posterior distribution of each stimulus. The feedforward input on each module is the likelihood of each modality. Two modules are integrated through cross-links, which are feedforward connections from the other modality, and reciprocal connections, which are recurrent connections between different modules. By stochastic gradient descent, we successfully trained the feedforward and recurrent coupling matrices simultaneously, both of which resembles the Mexican-hat. We also find that there are more than one set of coupling matrices that can approximate the Bayesian theorem well. Specifically, reciprocal connections and cross-links will compensate each other if one of them is removed. Even though trained with two inputs, the network's performance with only one input is in good accordance with what is predicted by the Bayesian theorem.

  20. Semi-supervised learning for detecting text-lines in noisy document images

    Science.gov (United States)

    Liu, Zongyi; Zhou, Hanning

    2010-01-01

    Document layout analysis is a key step in document image understanding with wide applications in document digitization and reformatting. Identifying correct layout from noisy scanned images is especially challenging. In this paper, we introduce a semi-supervised learning framework to detect text-lines from noisy document images. Our framework consists of three steps. The first step is the initial segmentation that extracts text-lines and images using simple morphological operations. The second step is a grouping-based layout analysis that identifies text-lines, image zones, column separator and vertical border noise. It is able to efficiently remove the vertical border noises from multi-column pages. The third step is an online classifier that is trained with the high confidence line detection results from Step Two, and filters out noise from low confidence lines. The classifier effectively removes speckle noises embedded inside the content zones. We compare the performance of our algorithm to the state-of-the-art work in the field on the UW-III database. We choose the results reported by the Image Understanding Pattern Recognition Research (IUPR) and Scansoft Omnipage SDK 15.5. We evaluate the performances at both the page frame level and the text-line level. The result shows that our system has much lower false-alarm rate, while maintains similar content detection rate. In addition, we also show that our online training model generalizes better than algorithms depending on offline training.

  1. Restricted Boltzmann machines based oversampling and semi-supervised learning for false positive reduction in breast CAD.

    Science.gov (United States)

    Cao, Peng; Liu, Xiaoli; Bao, Hang; Yang, Jinzhu; Zhao, Dazhe

    2015-01-01

    The false-positive reduction (FPR) is a crucial step in the computer aided detection system for the breast. The issues of imbalanced data distribution and the limitation of labeled samples complicate the classification procedure. To overcome these challenges, we propose oversampling and semi-supervised learning methods based on the restricted Boltzmann machines (RBMs) to solve the classification of imbalanced data with a few labeled samples. To evaluate the proposed method, we conducted a comprehensive performance study and compared its results with the commonly used techniques. Experiments on benchmark dataset of DDSM demonstrate the effectiveness of the RBMs based oversampling and semi-supervised learning method in terms of geometric mean (G-mean) for false positive reduction in Breast CAD.

  2. Supporting and Supervising Teachers Working With Adults Learning English. CAELA Network Brief

    Science.gov (United States)

    Young, Sarah

    2009-01-01

    This brief provides an overview of the knowledge and skills that administrators need in order to support and supervise teachers of adult English language learners. It begins with a review of resources and literature related to teacher supervision in general and to adult ESL education. It continues with information on the background and…

  3. Understanding Trust as an Essential Element of Trainee Supervision and Learning in the Workplace

    Science.gov (United States)

    Hauer, Karen E.; ten Cate, Olle; Boscardin, Christy; Irby, David M.; Iobst, William; O'Sullivan, Patricia S.

    2014-01-01

    Clinical supervision requires that supervisors make decisions about how much independence to allow their trainees for patient care tasks. The simultaneous goals of ensuring quality patient care and affording trainees appropriate and progressively greater responsibility require that the supervising physician trusts the trainee. Trust allows the…

  4. Enhancing the Doctoral Journey: The Role of Group Supervision in Supporting Collaborative Learning and Creativity

    Science.gov (United States)

    Fenge, Lee-Ann

    2012-01-01

    This article explores the role of group supervision within doctoral education, offering an exploration of the experience of group supervision processes through a small-scale study evaluating both student and staff experience across three cohorts of one professional doctorate programme. There has been very little research to date exploring…

  5. Is Direct Supervision in Clinical Education for Athletic Training Students Always Necessary to Enhance Student Learning?

    Science.gov (United States)

    Scriber, Kent; Trowbridge, Cindy

    2009-01-01

    Objective: To present an alternative model of supervision within clinical education experiences. Background: Several years ago direct supervision was defined more clearly in the accreditation standards for athletic training education programs (ATEPs). Currently, athletic training students may not gain any clinical experience without their clinical…

  6. Clinical group supervision in yoga therapy: model effects, and lessons learned.

    Science.gov (United States)

    Forbes, Bo; Volpe Horii, Cassandra; Earls, Bethany; Mashek, Stephanie; Akhtar, Fiona

    2012-01-01

    Clinical supervision is an integral component of therapist training and professional development because of its capacity for fostering knowledge, self-awareness, and clinical acumen. Individual supervision is part of many yoga therapy training programs and is referenced in the IAYT Standards as "mentoring." Group supervision is not typically used in the training of yoga therapists. We propose that group supervision effectively supports the growth and development of yoga therapists-in-training. We present a model of group supervision for yoga therapist trainees developed by the New England School of Integrative Yoga Therapeutics™ (The NESIYT Model) that includes the background, structure, format, and development of our inaugural 18-month supervision group. Pre-and post-supervision surveys and analyzed case notes, which captured key didactic and process themes, are discussed. Clinical issues, such as boundaries, performance anxiety, sense of self efficacy, the therapeutic alliance, transference and counter transference, pacing of yoga therapy sessions, evaluation of client progress, and adjunct therapist interaction are reviewed. The timing and sequence of didactic and process themes and benefits for yoga therapist trainees' professional development, are discussed. The NESIYT group supervision model is offered as an effective blueprint for yoga therapy training programs.

  7. Knowledge Work Supervision: Transforming School Systems into High Performing Learning Organizations.

    Science.gov (United States)

    Duffy, Francis M.

    1997-01-01

    This article describes a new supervision model conceived to help a school system redesign its anatomy (structures), physiology (flow of information and webs of relationships), and psychology (beliefs and values). The new paradigm (Knowledge Work Supervision) was constructed by reviewing the practices of several interrelated areas: sociotechnical…

  8. Classification and Diagnostic Output Prediction of Cancer Using Gene Expression Profiling and Supervised Machine Learning Algorithms

    DEFF Research Database (Denmark)

    Yoo, C.; Gernaey, Krist

    2008-01-01

    In this paper, a new supervised clustering and classification method is proposed. First, the application of discriminant partial least squares (DPLS) for the selection of a minimum number of key genes is applied on a gene expression microarray data set. Second, supervised hierarchical clustering ...

  9. An unsupervised text mining method for relation extraction from biomedical literature.

    Science.gov (United States)

    Quan, Changqin; Wang, Meng; Ren, Fuji

    2014-01-01

    The wealth of interaction information provided in biomedical articles motivated the implementation of text mining approaches to automatically extract biomedical relations. This paper presents an unsupervised method based on pattern clustering and sentence parsing to deal with biomedical relation extraction. Pattern clustering algorithm is based on Polynomial Kernel method, which identifies interaction words from unlabeled data; these interaction words are then used in relation extraction between entity pairs. Dependency parsing and phrase structure parsing are combined for relation extraction. Based on the semi-supervised KNN algorithm, we extend the proposed unsupervised approach to a semi-supervised approach by combining pattern clustering, dependency parsing and phrase structure parsing rules. We evaluated the approaches on two different tasks: (1) Protein-protein interactions extraction, and (2) Gene-suicide association extraction. The evaluation of task (1) on the benchmark dataset (AImed corpus) showed that our proposed unsupervised approach outperformed three supervised methods. The three supervised methods are rule based, SVM based, and Kernel based separately. The proposed semi-supervised approach is superior to the existing semi-supervised methods. The evaluation on gene-suicide association extraction on a smaller dataset from Genetic Association Database and a larger dataset from publicly available PubMed showed that the proposed unsupervised and semi-supervised methods achieved much higher F-scores than co-occurrence based method.

  10. An unsupervised text mining method for relation extraction from biomedical literature.

    Directory of Open Access Journals (Sweden)

    Changqin Quan

    Full Text Available The wealth of interaction information provided in biomedical articles motivated the implementation of text mining approaches to automatically extract biomedical relations. This paper presents an unsupervised method based on pattern clustering and sentence parsing to deal with biomedical relation extraction. Pattern clustering algorithm is based on Polynomial Kernel method, which identifies interaction words from unlabeled data; these interaction words are then used in relation extraction between entity pairs. Dependency parsing and phrase structure parsing are combined for relation extraction. Based on the semi-supervised KNN algorithm, we extend the proposed unsupervised approach to a semi-supervised approach by combining pattern clustering, dependency parsing and phrase structure parsing rules. We evaluated the approaches on two different tasks: (1 Protein-protein interactions extraction, and (2 Gene-suicide association extraction. The evaluation of task (1 on the benchmark dataset (AImed corpus showed that our proposed unsupervised approach outperformed three supervised methods. The three supervised methods are rule based, SVM based, and Kernel based separately. The proposed semi-supervised approach is superior to the existing semi-supervised methods. The evaluation on gene-suicide association extraction on a smaller dataset from Genetic Association Database and a larger dataset from publicly available PubMed showed that the proposed unsupervised and semi-supervised methods achieved much higher F-scores than co-occurrence based method.

  11. Robust SIFT Image Matching Algorithm Based on Unsupervised Learning%基于无监督学习的SIFT鲁棒图像匹配算法

    Institute of Scientific and Technical Information of China (English)

    袭著有

    2014-01-01

    Due to the invariance of scale,rota-tion,illumination,SIFT (scale invariant feature transform)descriptor is commonly used in image matching.However,on the one hand,in practical applications the isolated point and the noise point may cause mismatching points.On the other hand, SIFT feature points record the relationship of dif-ferent scale between the feature point and around it,so easily caused the described similar between the different image feature point and it can be matched each other after extracting their feature points.In order to solve the problem,this paper proposed a feature point matching method based on SIFT algorithm,use unsupervised learning meth-ods to classify the matching points and eliminate the abnormal points,achieving the goal of the sec-ond accurate feature matching.%SIFT特征匹配算法的匹配能力强,但特征点中孤立点和噪声点等会导致部分特征点误匹配;不同图像间特征点的有关描述相近,也会造成两幅不同结构的图像,在提取出各自的 SIFT 特征点后相互匹配。为此,提出一种改进 SIFT 的图像特征匹配算法。该算法是在 SIFT 特征匹配的基础上,利用无监督学习方法对匹配异常点进行剔除,实现特征点的二次精确匹配。

  12. Material classification and automatic content enrichment of images using supervised learning and knowledge bases

    Science.gov (United States)

    Mallepudi, Sri Abhishikth; Calix, Ricardo A.; Knapp, Gerald M.

    2011-02-01

    In recent years there has been a rapid increase in the size of video and image databases. Effective searching and retrieving of images from these databases is a significant current research area. In particular, there is a growing interest in query capabilities based on semantic image features such as objects, locations, and materials, known as content-based image retrieval. This study investigated mechanisms for identifying materials present in an image. These capabilities provide additional information impacting conditional probabilities about images (e.g. objects made of steel are more likely to be buildings). These capabilities are useful in Building Information Modeling (BIM) and in automatic enrichment of images. I2T methodologies are a way to enrich an image by generating text descriptions based on image analysis. In this work, a learning model is trained to detect certain materials in images. To train the model, an image dataset was constructed containing single material images of bricks, cloth, grass, sand, stones, and wood. For generalization purposes, an additional set of 50 images containing multiple materials (some not used in training) was constructed. Two different supervised learning classification models were investigated: a single multi-class SVM classifier, and multiple binary SVM classifiers (one per material). Image features included Gabor filter parameters for texture, and color histogram data for RGB components. All classification accuracy scores using the SVM-based method were above 85%. The second model helped in gathering more information from the images since it assigned multiple classes to the images. A framework for the I2T methodology is presented.

  13. Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning.

    Science.gov (United States)

    Cole-Lewis, Heather; Varghese, Arun; Sanders, Amy; Schwarz, Mary; Pugatch, Jillian; Augustson, Erik

    2015-08-25

    Electronic cigarettes (e-cigarettes) continue to be a growing topic among social media users, especially on Twitter. The ability to analyze conversations about e-cigarettes in real-time can provide important insight into trends in the public's knowledge, attitudes, and beliefs surrounding e-cigarettes, and subsequently guide public health interventions. Our aim was to establish a supervised machine learning algorithm to build predictive classification models that assess Twitter data for a range of factors related to e-cigarettes. Manual content analysis was conducted for 17,098 tweets. These tweets were coded for five categories: e-cigarette relevance, sentiment, user description, genre, and theme. Machine learning classification models were then built for each of these five categories, and word groupings (n-grams) were used to define the feature space for each classifier. Predictive performance scores for classification models indicated that the models correctly labeled the tweets with the appropriate variables between 68.40% and 99.34% of the time, and the percentage of maximum possible improvement over a random baseline that was achieved by the classification models ranged from 41.59% to 80.62%. Classifiers with the highest performance scores that also achieved the highest percentage of the maximum possible improvement over a random baseline were Policy/Government (performance: 0.94; % improvement: 80.62%), Relevance (performance: 0.94; % improvement: 75.26%), Ad or Promotion (performance: 0.89; % improvement: 72.69%), and Marketing (performance: 0.91; % improvement: 72.56%). The most appropriate word-grouping unit (n-gram) was 1 for the majority of classifiers. Performance continued to marginally increase with the size of the training dataset of manually annotated data, but eventually leveled off. Even at low dataset sizes of 4000 observations, performance characteristics were fairly sound. Social media outlets like Twitter can uncover real-time snapshots of

  14. Attend in groups: a weakly-supervised deep learning framework for learning from web data

    OpenAIRE

    Zhuang, Bohan; Liu, Lingqiao; Li, Yao; Shen, Chunhua; Reid, Ian

    2016-01-01

    Large-scale datasets have driven the rapid development of deep neural networks for visual recognition. However, annotating a massive dataset is expensive and time-consuming. Web images and their labels are, in comparison, much easier to obtain, but direct training on such automatically harvested images can lead to unsatisfactory performance, because the noisy labels of Web images adversely affect the learned recognition models. To address this drawback we propose an end-to-end weakly-supervis...

  15. Evaluation of Four Supervised Learning Methods for Benthic Habitat Mapping Using Backscatter from Multi-Beam Sonar

    Directory of Open Access Journals (Sweden)

    Jacquomo Monk

    2012-11-01

    Full Text Available An understanding of the distribution and extent of marine habitats is essential for the implementation of ecosystem-based management strategies. Historically this had been difficult in marine environments until the advancement of acoustic sensors. This study demonstrates the applicability of supervised learning techniques for benthic habitat characterization using angular backscatter response data. With the advancement of multibeam echo-sounder (MBES technology, full coverage datasets of physical structure over vast regions of the seafloor are now achievable. Supervised learning methods typically applied to terrestrial remote sensing provide a cost-effective approach for habitat characterization in marine systems. However the comparison of the relative performance of different classifiers using acoustic data is limited. Characterization of acoustic backscatter data from MBES using four different supervised learning methods to generate benthic habitat maps is presented. Maximum Likelihood Classifier (MLC, Quick, Unbiased, Efficient Statistical Tree (QUEST, Random Forest (RF and Support Vector Machine (SVM were evaluated to classify angular backscatter response into habitat classes using training data acquired from underwater video observations. Results for biota classifications indicated that SVM and RF produced the highest accuracies, followed by QUEST and MLC, respectively. The most important backscatter data were from the moderate incidence angles between 30° and 50°. This study presents initial results for understanding how acoustic backscatter from MBES can be optimized for the characterization of marine benthic biological habitats.

  16. Visualization and unsupervised predictive clustering of high-dimensional multimodal neuroimaging data.

    Science.gov (United States)

    Mwangi, Benson; Soares, Jair C; Hasan, Khader M

    2014-10-30

    Neuroimaging machine learning studies have largely utilized supervised algorithms - meaning they require both neuroimaging scan data and corresponding target variables (e.g. healthy vs. diseased) to be successfully 'trained' for a prediction task. Noticeably, this approach may not be optimal or possible when the global structure of the data is not well known and the researcher does not have an a priori model to fit the data. We set out to investigate the utility of an unsupervised machine learning technique; t-distributed stochastic neighbour embedding (t-SNE) in identifying 'unseen' sample population patterns that may exist in high-dimensional neuroimaging data. Multimodal neuroimaging scans from 92 healthy subjects were pre-processed using atlas-based methods, integrated and input into the t-SNE algorithm. Patterns and clusters discovered by the algorithm were visualized using a 2D scatter plot and further analyzed using the K-means clustering algorithm. t-SNE was evaluated against classical principal component analysis. Remarkably, based on unlabelled multimodal scan data, t-SNE separated study subjects into two very distinct clusters which corresponded to subjects' gender labels (cluster silhouette index value=0.79). The resulting clusters were used to develop an unsupervised minimum distance clustering model which identified 93.5% of subjects' gender. Notably, from a neuropsychiatric perspective this method may allow discovery of data-driven disease phenotypes or sub-types of treatment responders. Copyright © 2014 Elsevier B.V. All rights reserved.

  17. Developing a supervised training algorithm for limited precision feed-forward spiking neural networks

    CERN Document Server

    Stromatias, Evangelos

    2011-01-01

    Spiking neural networks have been referred to as the third generation of artificial neural networks where the information is coded as time of the spikes. There are a number of different spiking neuron models available and they are categorized based on their level of abstraction. In addition, there are two known learning methods, unsupervised and supervised learning. This thesis focuses on supervised learning where a new algorithm is proposed, based on genetic algorithms. The proposed algorithm is able to train both synaptic weights and delays and also allow each neuron to emit multiple spikes thus taking full advantage of the spatial-temporal coding power of the spiking neurons. In addition, limited synaptic precision is applied; only six bits are used to describe and train a synapse, three bits for the weights and three bits for the delays. Two limited precision schemes are investigated. The proposed algorithm is tested on the XOR classification problem where it produces better results for even smaller netwo...

  18. Introduction to machine learning.

    Science.gov (United States)

    Baştanlar, Yalin; Ozuysal, Mustafa

    2014-01-01

    The machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity and processing power of computers. Together with many other disciplines, machine learning methods have been widely employed in bioinformatics. The difficulties and cost of biological analyses have led to the development of sophisticated machine learning approaches for this application area. In this chapter, we first review the fundamental concepts of machine learning such as feature assessment, unsupervised versus supervised learning and types of classification. Then, we point out the main issues of designing machine learning experiments and their performance evaluation. Finally, we introduce some supervised learning methods.

  19. Kollegial supervision

    DEFF Research Database (Denmark)

    Andersen, Ole Dibbern; Petersson, Erling

    Publikationen belyser, hvordan kollegial supervision i en kan organiseres i en uddannelsesinstitution......Publikationen belyser, hvordan kollegial supervision i en kan organiseres i en uddannelsesinstitution...

  20. Estimation and scaling of hydrostratigraphic units: application of unsupervised machine learning and multivariate statistical techniques to hydrogeophysical data

    Science.gov (United States)

    Friedel, Michael J.

    2016-08-01

    Numerical models provide a way to evaluate groundwater systems, but determining the hydrostratigraphic units (HSUs) used in constructing these models remains subjective, nonunique, and uncertain. A three-step machine-learning approach is proposed in which fusion, estimation, and clustering operations are performed on different data sets to arrive at HSUs at different scales. In step one, data fusion is performed by training a self-organizing map (SOM) with sparse borehole hydrogeologic (lithology, hydraulic conductivity, aqueous field parameters, dissolved constituents) and geophysical (gamma, spontaneous potential, and resistivity) measurements. Estimation is handled by iterative least-squares minimization of the SOM quantization and topographical errors. Application of the Davies-Bouldin criteria to k-means clustering of SOM nodes is used to determine the number and location of discontinuous borehole HSUs with low lateral density (based on borehole spacing at 100 s m) and high vertical density (based on cm-scale logging). In step two, a scaling network is trained using the estimated borehole HSUs, airborne electromagnetic measurements, and numerically inverted resistivity profiles. In step three, independent airborne electromagnetic measurements are applied to the scaling network, and the estimation performed to arrive at a set of continuous HSUs with high lateral density (based on sounding locations at meter (m) spacing) and medium vertical density (based on m-layer modeled structure). Performance metrics are used to evaluate each step of the approach. Efficacy of the proposed approach is demonstrated to map local-to-regional scale HSUs using hydrogeophysical data collected at a heterogeneous surficial aquifer in northwestern Nebraska, USA.

  1. Estimation and scaling of hydrostratigraphic units: application of unsupervised machine learning and multivariate statistical techniques to hydrogeophysical data

    Science.gov (United States)

    Friedel, Michael J.

    2016-12-01

    Numerical models provide a way to evaluate groundwater systems, but determining the hydrostratigraphic units (HSUs) used in constructing these models remains subjective, nonunique, and uncertain. A three-step machine-learning approach is proposed in which fusion, estimation, and clustering operations are performed on different data sets to arrive at HSUs at different scales. In step one, data fusion is performed by training a self-organizing map (SOM) with sparse borehole hydrogeologic (lithology, hydraulic conductivity, aqueous field parameters, dissolved constituents) and geophysical (gamma, spontaneous potential, and resistivity) measurements. Estimation is handled by iterative least-squares minimization of the SOM quantization and topographical errors. Application of the Davies-Bouldin criteria to k-means clustering of SOM nodes is used to determine the number and location of discontinuous borehole HSUs with low lateral density (based on borehole spacing at 100 s m) and high vertical density (based on cm-scale logging). In step two, a scaling network is trained using the estimated borehole HSUs, airborne electromagnetic measurements, and numerically inverted resistivity profiles. In step three, independent airborne electromagnetic measurements are applied to the scaling network, and the estimation performed to arrive at a set of continuous HSUs with high lateral density (based on sounding locations at meter (m) spacing) and medium vertical density (based on m-layer modeled structure). Performance metrics are used to evaluate each step of the approach. Efficacy of the proposed approach is demonstrated to map local-to-regional scale HSUs using hydrogeophysical data collected at a heterogeneous surficial aquifer in northwestern Nebraska, USA.

  2. Semi-supervised Machine Learning for Analysis of Hydrogeochemical Data and Models

    Science.gov (United States)

    Vesselinov, Velimir; O'Malley, Daniel; Alexandrov, Boian; Moore, Bryan

    2017-04-01

    Data- and model-based analyses such as uncertainty quantification, sensitivity analysis, and decision support using complex physics models with numerous model parameters and typically require a huge number of model evaluations (on order of 10^6). Furthermore, model simulations of complex physics may require substantial computational time. For example, accounting for simultaneously occurring physical processes such as fluid flow and biogeochemical reactions in heterogeneous porous medium may require several hours of wall-clock computational time. To address these issues, we have developed a novel methodology for semi-supervised machine learning based on Non-negative Matrix Factorization (NMF) coupled with customized k-means clustering. The algorithm allows for automated, robust Blind Source Separation (BSS) of groundwater types (contamination sources) based on model-free analyses of observed hydrogeochemical data. We have also developed reduced order modeling tools, which coupling support vector regression (SVR), genetic algorithms (GA) and artificial and convolutional neural network (ANN/CNN). SVR is applied to predict the model behavior within prior uncertainty ranges associated with the model parameters. ANN and CNN procedures are applied to upscale heterogeneity of the porous medium. In the upscaling process, fine-scale high-resolution models of heterogeneity are applied to inform coarse-resolution models which have improved computational efficiency while capturing the impact of fine-scale effects at the course scale of interest. These techniques are tested independently on a series of synthetic problems. We also present a decision analysis related to contaminant remediation where the developed reduced order models are applied to reproduce groundwater flow and contaminant transport in a synthetic heterogeneous aquifer. The tools are coded in Julia and are a part of the MADS high-performance computational framework (https://github.com/madsjulia/Mads.jl).

  3. SU-E-J-107: Supervised Learning Model of Aligned Collagen for Human Breast Carcinoma Prognosis

    Energy Technology Data Exchange (ETDEWEB)

    Bredfeldt, J; Liu, Y; Conklin, M; Keely, P; Eliceiri, K; Mackie, T [University of Wisconsin, Madison, WI (United States)

    2014-06-01

    Purpose: Our goal is to develop and apply a set of optical and computational tools to enable large-scale investigations of the interaction between collagen and tumor cells. Methods: We have built a novel imaging system for automating the capture of whole-slide second harmonic generation (SHG) images of collagen in registry with bright field (BF) images of hematoxylin and eosin stained tissue. To analyze our images, we have integrated a suite of supervised learning tools that semi-automatically model and score collagen interactions with tumor cells via a variety of metrics, a method we call Electronic Tumor Associated Collagen Signatures (eTACS). This group of tools first segments regions of epithelial cells and collagen fibers from BF and SHG images respectively. We then associate fibers with groups of epithelial cells and finally compute features based on the angle of interaction and density of the collagen surrounding the epithelial cell clusters. These features are then processed with a support vector machine to separate cancer patients into high and low risk groups. Results: We validated our model by showing that eTACS produces classifications that have statistically significant correlation with manual classifications. In addition, our system generated classification scores that accurately predicted breast cancer patient survival in a cohort of 196 patients. Feature rank analysis revealed that TACS positive fibers are more well aligned with each other, generally lower density, and terminate within or near groups of epithelial cells. Conclusion: We are working to apply our model to predict survival in larger cohorts of breast cancer patients with a diversity of breast cancer types, predict response to treatments such as COX2 inhibitors, and to study collagen architecture changes in other cancer types. In the future, our system may be used to provide metastatic potential information to cancer patients to augment existing clinical assays.

  4. Unsupervised universal steganalyzer for high-dimensional steganalytic features

    Science.gov (United States)

    Hou, Xiaodan; Zhang, Tao

    2016-11-01

    The research in developing steganalytic features has been highly successful. These features are extremely powerful when applied to supervised binary classification problems. However, they are incompatible with unsupervised universal steganalysis because the unsupervised method cannot distinguish embedding distortion from varying levels of noises caused by cover variation. This study attempts to alleviate the problem by introducing similarity retrieval of image statistical properties (SRISP), with the specific aim of mitigating the effect of cover variation on the existing steganalytic features. First, cover images with some statistical properties similar to those of a given test image are searched from a retrieval cover database to establish an aided sample set. Then, unsupervised outlier detection is performed on a test set composed of the given test image and its aided sample set to determine the type (cover or stego) of the given test image. Our proposed framework, called SRISP-aided unsupervised outlier detection, requires no training. Thus, it does not suffer from model mismatch mess. Compared with prior unsupervised outlier detectors that do not consider SRISP, the proposed framework not only retains the universality but also exhibits superior performance when applied to high-dimensional steganalytic features.

  5. Unsupervised unstained cell detection by SIFT keypoint clustering and self-labeling algorithm.

    Science.gov (United States)

    Muallal, Firas; Schöll, Simon; Sommerfeldt, Björn; Maier, Andreas; Steidl, Stefan; Buchholz, Rainer; Hornegger, Joachim

    2014-01-01

    We propose a novel unstained cell detection algorithm based on unsupervised learning. The algorithm utilizes the scale invariant feature transform (SIFT), a self-labeling algorithm, and two clustering steps in order to achieve high performance in terms of time and detection accuracy. Unstained cell imaging is dominated by phase contrast and bright field microscopy. Therefore, the algorithm was assessed on images acquired using these two modalities. Five cell lines having in total 37 images and 7250 cells were considered for the evaluation: CHO, L929, Sf21, HeLa, and Bovine cells. The obtained F-measures were between 85.1 and 89.5. Compared to the state-of-the-art, the algorithm achieves very close F-measure to the supervised approaches in much less time.

  6. Unsupervised spatial event detection in targeted domains with applications to civil unrest modeling.

    Directory of Open Access Journals (Sweden)

    Liang Zhao

    Full Text Available Twitter has become a popular data source as a surrogate for monitoring and detecting events. Targeted domains such as crime, election, and social unrest require the creation of algorithms capable of detecting events pertinent to these domains. Due to the unstructured language, short-length messages, dynamics, and heterogeneity typical of Twitter data streams, it is technically difficult and labor-intensive to develop and maintain supervised learning systems. We present a novel unsupervised approach for detecting spatial events in targeted domains and illustrate this approach using one specific domain, viz. civil unrest modeling. Given a targeted domain, we propose a dynamic query expansion algorithm to iteratively expand domain-related terms, and generate a tweet homogeneous graph. An anomaly identification method is utilized to detect spatial events over this graph by jointly maximizing local modularity and spatial scan statistics. Extensive experiments conducted in 10 Latin American countries demonstrate the effectiveness of the proposed approach.

  7. Unsupervised EEG analysis for automated epileptic seizure detection

    Science.gov (United States)

    Birjandtalab, Javad; Pouyan, Maziyar Baran; Nourani, Mehrdad

    2016-07-01

    Epilepsy is a neurological disorder which can, if not controlled, potentially cause unexpected death. It is extremely crucial to have accurate automatic pattern recognition and data mining techniques to detect the onset of seizures and inform care-givers to help the patients. EEG signals are the preferred biosignals for diagnosis of epileptic patients. Most of the existing pattern recognition techniques used in EEG analysis leverage the notion of supervised machine learning algorithms. Since seizure data are heavily under-represented, such techniques are not always practical particularly when the labeled data is not sufficiently available or when disease progression is rapid and the corresponding EEG footprint pattern will not be robust. Furthermore, EEG pattern change is highly individual dependent and requires experienced specialists to annotate the seizure and non-seizure events. In this work, we present an unsupervised technique to discriminate seizures and non-seizures events. We employ power spectral density of EEG signals in different frequency bands that are informative features to accurately cluster seizure and non-seizure events. The experimental results tried so far indicate achieving more than 90% accuracy in clustering seizure and non-seizure events without having any prior knowledge on patient's history.

  8. Adaptive Deep Supervised Autoencoder Based Image Reconstruction for Face Recognition

    Directory of Open Access Journals (Sweden)

    Rongbing Huang

    2016-01-01

    Full Text Available Based on a special type of denoising autoencoder (DAE and image reconstruction, we present a novel supervised deep learning framework for face recognition (FR. Unlike existing deep autoencoder which is unsupervised face recognition method, the proposed method takes class label information from training samples into account in the deep learning procedure and can automatically discover the underlying nonlinear manifold structures. Specifically, we define an Adaptive Deep Supervised Network Template (ADSNT with the supervised autoencoder which is trained to extract characteristic features from corrupted/clean facial images and reconstruct the corresponding similar facial images. The reconstruction is realized by a so-called “bottleneck” neural network that learns to map face images into a low-dimensional vector and reconstruct the respective corresponding face images from the mapping vectors. Having trained the ADSNT, a new face image can then be recognized by comparing its reconstruction image with individual gallery images, respectively. Extensive experiments on three databases including AR, PubFig, and Extended Yale B demonstrate that the proposed method can significantly improve the accuracy of face recognition under enormous illumination, pose change, and a fraction of occlusion.

  9. Learning a Markov Logic network for supervised gene regulatory network inference.

    Science.gov (United States)

    Brouard, Céline; Vrain, Christel; Dubois, Julie; Castel, David; Debily, Marie-Anne; d'Alché-Buc, Florence

    2013-09-12

    Gene regulatory network inference remains a challenging problem in systems biology despite the numerous approaches that have been proposed. When substantial knowledge on a gene regulatory network is already available, supervised network inference is appropriate. Such a method builds a binary classifier able to assign a class (Regulation/No regulation) to an ordered pair of genes. Once learnt, the pairwise classifier can be used to predict new regulations. In this work, we explore the framework of Markov Logic Networks (MLN) that combine features of probabilistic graphical models with the expressivity of first-order logic rules. We propose to learn a Markov Logic network, e.g. a set of weighted rules that conclude on the predicate "regulates", starting from a known gene regulatory network involved in the switch proliferation/differentiation of keratinocyte cells, a set of experimental transcriptomic data and various descriptions of genes all encoded into first-order logic. As training data are unbalanced, we use asymmetric bagging to learn a set of MLNs. The prediction of a new regulation can then be obtained by averaging predictions of individual MLNs. As a side contribution, we propose three in silico tests to assess the performance of any pairwise classifier in various network inference tasks on real datasets. A first test consists of measuring the average performance on balanced edge prediction problem; a second one deals with the ability of the classifier, once enhanced by asymmetric bagging, to update a given network. Finally our main result concerns a third test that measures the ability of the method to predict regulations with a new set of genes. As expected, MLN, when provided with only numerical discretized gene expression data, does not perform as well as a pairwise SVM in terms of AUPR. However, when a more complete description of gene properties is provided by heterogeneous sources, MLN achieves the same performance as a black-box model such as a

  10. Investigating the control of climatic oscillations over global terrestrial evaporation using a simple supervised learning method

    Science.gov (United States)

    Martens, Brecht; Miralles, Diego; Waegeman, Willem; Dorigo, Wouter; Verhoest, Niko

    2017-04-01

    Intra-annual and multi-decadal variations in the Earth's climate are to a large extent driven by periodic oscillations in the coupled state of atmosphere and ocean. These oscillations alter not only the climate in nearby regions, but also have an important impact on the local climate in remote areas, a phenomenon that is often referred to as 'teleconnection'. Because changes in local climate immediately impact terrestrial ecosystems through a series of complex processes and feedbacks, ocean-atmospheric teleconnections are expected to influence land evaporation - i.e. the return flux of water from land to atmosphere. In this presentation, the effects of these intra-annual and multi-decadal climate oscillations on global terrestrial evaporation are analysed. To this end, we use satellite observations of different essential climate variables in combination with a simple supervised learning method, the lasso regression. A total of sixteen Climate Oscillation Indices (COIs) - which are routinely used to diagnose the major ocean-atmospheric oscillations - are selected. Multi-decadal data of terrestrial evaporation are retrieved from the Global Land Evaporation Amsterdam Model (GLEAM, www.gleam.eu). Using the lasso regression, it is shown that more than 30% of the inter-annual variations in terrestrial evaporation can be explained by ocean-atmospheric oscillations. In addition, the impact in different regions across the globe can typically be attributed to a small subset of the sixteen COIs. For instance, the dynamics in terrestrial evaporation over Australia are substantially impacted by both the El Niño Southern Oscillation (here diagnosed using the Southern Oscillation Index, SOI) and the Indian Ocean Dipole Oscillation (here diagnosed using the Indian Dipole Mode Index, DMI). Subsequently, using the same learning method but regressing terrestrial evaporation to its local climatic drivers (air temperature, precipitation, radiation), allows us to discern through which

  11. Unsupervised K-Nearest Neighbor Regression

    CERN Document Server

    Kramer, Oliver

    2011-01-01

    In many scientific disciplines structures in high-dimensional data have to be found, e.g., in stellar spectra, in genome data, or for face recognition tasks. In this work we present a novel approach to non-linear dimensionality reduction. It is based on fitting K-nearest neighbor regression to the unsupervised regression framework for learning of low-dimensional manifolds. Similar to related approaches that are mostly based on kernel methods, unsupervised K-nearest neighbor (UKNN) regression optimizes latent variables w.r.t. the data space reconstruction error employing the K-nearest neighbor heuristic. The problem of optimizing latent neighborhoods is difficult to solve, but the UKNN formulation allows an efficient strategy of iteratively embedding latent points to fixed neighborhood topologies. The approaches will be tested experimentally.

  12. Unsupervised information extraction by text segmentation

    CERN Document Server

    Cortez, Eli

    2013-01-01

    A new unsupervised approach to the problem of Information Extraction by Text Segmentation (IETS) is proposed, implemented and evaluated herein. The authors' approach relies on information available on pre-existing data to learn how to associate segments in the input string with attributes of a given domain relying on a very effective set of content-based features. The effectiveness of the content-based features is also exploited to directly learn from test data structure-based features, with no previous human-driven training, a feature unique to the presented approach. Based on the approach, a

  13. Facebook Blocket with Unsupervised Learning

    OpenAIRE

    Amin, Khizer; Minhas, Mehmood ul haq

    2014-01-01

    The Internet has become a valuable channel for both business-to- consumer and business-to-business e-commerce. It has changed the way for many companies to manage the business. Every day, more and more companies are making their presence on Internet. Web sites are launched for online shopping as web shops or on-line stores are a popular means of goods distribution. The number of items sold through the internet has sprung up significantly in the past few years. Moreover, it has become a choice...

  14. Facebook Blocket with Unsupervised Learning

    OpenAIRE

    Amin, Khizer; Minhas, Mehmood ul haq

    2014-01-01

    The Internet has become a valuable channel for both business-to- consumer and business-to-business e-commerce. It has changed the way for many companies to manage the business. Every day, more and more companies are making their presence on Internet. Web sites are launched for online shopping as web shops or on-line stores are a popular means of goods distribution. The number of items sold through the internet has sprung up significantly in the past few years. Moreover, it has become a choice...

  15. Intrusion Detection using unsupervised learning

    Directory of Open Access Journals (Sweden)

    Kusum bharti

    2010-08-01

    Full Text Available Clustering is the one of the efficient datamining techniques for intrusion detection. In clustering algorithm kmean clustering is widely used for intrusion detection. Because it gives efficient results incase of huge datasets. But sometime kmean clustering fails to give best result because of class dominance problem and no class problem. So for removing these problems we are proposing two new algorithms for cluster to class assignment. According to our experimental results the proposed algorithm are having high precision and recall for low class instances.

  16. Incremental learning by message passing in hierarchical temporal memory.

    Science.gov (United States)

    Rehn, Erik M; Maltoni, Davide

    2014-08-01

    Hierarchical temporal memory (HTM) is a biologically inspired framework that can be used to learn invariant representations of patterns in a wide range of applications. Classical HTM learning is mainly unsupervised, and once training is completed, the network structure is frozen, thus making further training (i.e., incremental learning) quite critical. In this letter, we develop a novel technique for HTM (incremental) supervised learning based on gradient descent error minimization. We prove that error backpropagation can be naturally and elegantly implemented through native HTM message passing based on belief propagation. Our experimental results demonstrate that a two-stage training approach composed of unsupervised pretraining and supervised refinement is very effective (both accurate and efficient). This is in line with recent findings on other deep architectures.

  17. Comparison of supervised machine learning algorithms for waterborne pathogen detection using mobile phone fluorescence microscopy

    Science.gov (United States)

    Ceylan Koydemir, Hatice; Feng, Steve; Liang, Kyle; Nadkarni, Rohan; Benien, Parul; Ozcan, Aydogan

    2017-06-01

    Giardia lamblia is a waterborne parasite that affects millions of people every year worldwide, causing a diarrheal illness known as giardiasis. Timely detection of the presence of the cysts of this parasite in drinking water is important to prevent the spread of the disease, especially in resource-limited settings. Here we provide extended experimental testing and evaluation of the performance and repeatability of a field-portable and cost-effective microscopy platform for automated detection and counting of Giardia cysts in water samples, including tap water, non-potable water, and pond water. This compact platform is based on our previous work, and is composed of a smartphone-based fluorescence microscope, a disposable sample processing cassette, and a custom-developed smartphone application. Our mobile phone microscope has a large field of view of 0.8 cm2 and weighs only 180 g, excluding the phone. A custom-developed smartphone application provides a user-friendly graphical interface, guiding the users to capture a fluorescence image of the sample filter membrane and analyze it automatically at our servers using an image processing algorithm and training data, consisting of >30,000 images of cysts and >100,000 images of other fluorescent particles that are captured, including, e.g. dust. The total time that it takes from sample preparation to automated cyst counting is less than an hour for each 10 ml of water sample that is tested. We compared the sensitivity and the specificity of our platform using multiple supervised classification models, including support vector machines and nearest neighbors, and demonstrated that a bootstrap aggregating (i.e. bagging) approach using raw image file format provides the best performance for automated detection of Giardia cysts. We evaluated the performance of this machine learning enabled pathogen detection device with water samples taken from different sources (e.g. tap water, non-potable water, pond water) and achieved a

  18. Comparison of supervised machine learning algorithms for waterborne pathogen detection using mobile phone fluorescence microscopy

    Directory of Open Access Journals (Sweden)

    Ceylan Koydemir Hatice

    2017-06-01

    Full Text Available Giardia lamblia is a waterborne parasite that affects millions of people every year worldwide, causing a diarrheal illness known as giardiasis. Timely detection of the presence of the cysts of this parasite in drinking water is important to prevent the spread of the disease, especially in resource-limited settings. Here we provide extended experimental testing and evaluation of the performance and repeatability of a field-portable and cost-effective microscopy platform for automated detection and counting of Giardia cysts in water samples, including tap water, non-potable water, and pond water. This compact platform is based on our previous work, and is composed of a smartphone-based fluorescence microscope, a disposable sample processing cassette, and a custom-developed smartphone application. Our mobile phone microscope has a large field of view of ~0.8 cm2 and weighs only ~180 g, excluding the phone. A custom-developed smartphone application provides a user-friendly graphical interface, guiding the users to capture a fluorescence image of the sample filter membrane and analyze it automatically at our servers using an image processing algorithm and training data, consisting of >30,000 images of cysts and >100,000 images of other fluorescent particles that are captured, including, e.g. dust. The total time that it takes from sample preparation to automated cyst counting is less than an hour for each 10 ml of water sample that is tested. We compared the sensitivity and the specificity of our platform using multiple supervised classification models, including support vector machines and nearest neighbors, and demonstrated that a bootstrap aggregating (i.e. bagging approach using raw image file format provides the best performance for automated detection of Giardia cysts. We evaluated the performance of this machine learning enabled pathogen detection device with water samples taken from different sources (e.g. tap water, non-potable water, pond

  19. Comparison of supervised machine learning algorithms for waterborne pathogen detection using mobile phone fluorescence microscopy

    KAUST Repository

    Ceylan Koydemir, Hatice

    2017-06-14

    Giardia lamblia is a waterborne parasite that affects millions of people every year worldwide, causing a diarrheal illness known as giardiasis. Timely detection of the presence of the cysts of this parasite in drinking water is important to prevent the spread of the disease, especially in resource-limited settings. Here we provide extended experimental testing and evaluation of the performance and repeatability of a field-portable and cost-effective microscopy platform for automated detection and counting of Giardia cysts in water samples, including tap water, non-potable water, and pond water. This compact platform is based on our previous work, and is composed of a smartphone-based fluorescence microscope, a disposable sample processing cassette, and a custom-developed smartphone application. Our mobile phone microscope has a large field of view of ~0.8 cm2 and weighs only ~180 g, excluding the phone. A custom-developed smartphone application provides a user-friendly graphical interface, guiding the users to capture a fluorescence image of the sample filter membrane and analyze it automatically at our servers using an image processing algorithm and training data, consisting of >30,000 images of cysts and >100,000 images of other fluorescent particles that are captured, including, e.g. dust. The total time that it takes from sample preparation to automated cyst counting is less than an hour for each 10 ml of water sample that is tested. We compared the sensitivity and the specificity of our platform using multiple supervised classification models, including support vector machines and nearest neighbors, and demonstrated that a bootstrap aggregating (i.e. bagging) approach using raw image file format provides the best performance for automated detection of Giardia cysts. We evaluated the performance of this machine learning enabled pathogen detection device with water samples taken from different sources (e.g. tap water, non-potable water, pond water) and achieved

  20. A Comparison of Supervised Machine Learning Algorithms and Feature Vectors for MS Lesion Segmentation Using Multimodal Structural MRI

    Science.gov (United States)

    Sweeney, Elizabeth M.; Vogelstein, Joshua T.; Cuzzocreo, Jennifer L.; Calabresi, Peter A.; Reich, Daniel S.; Crainiceanu, Ciprian M.; Shinohara, Russell T.

    2014-01-01

    Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance. PMID:24781953

  1. A review of literature on the use of machine learning methods for opinion mining

    Directory of Open Access Journals (Sweden)

    Aytuğ ONAN

    2016-05-01

    Full Text Available Opinion mining is an emerging field which uses methods of natural language processing, text mining and computational linguistics to extract subjective information of opinion holders. Opinion mining can be viewed as a classification problem. Hence, machine learning based methods are widely employed for sentiment classification. Machine learning based methods in opinion mining can be mainly classified as supervised, semi-supervised and unsupervised methods. In this study, main existing literature on the use of machine learning methods for opinion mining has been presented. Besides, the weak and strong characteristics of machine learning methods have been discussed.

  2. Information theoretic learning Renyi's entropy and Kernel perspectives

    CERN Document Server

    Principe, Jose C

    2010-01-01

    This book presents the first cohesive treatment of Information Theoretic Learning (ITL) algorithms to adapt linear or nonlinear learning machines both in supervised or unsupervised paradigms. ITL is a framework where the conventional concepts of second order statistics (covariance, L2 distances, correlation functions) are substituted by scalars and functions with information theoretic underpinnings, respectively entropy, mutual information and correntropy. ITL quantifies the stochastic structure of the data beyond second order statistics for improved performance without using full-blown Bayesi

  3. Conducting Supervised Experiential Learning/Field Experiences for Students' Development and Career Reinforcement.

    Science.gov (United States)

    Leventhal, Jerome I.

    A major problem in the educational system of the United States is that a great number of students and graduates lack a career objective, and, therefore, many workers are unhappy. Offering a variety of supervised field experiences, paid or unpaid, in which students see workers in their occupations will help students identify career choices.…

  4. Don't Leave Teaching to Chance: Learning Objectives for Psychodynamic Psychotherapy Supervision

    Science.gov (United States)

    Rojas, Alicia; Arbuckle, Melissa; Cabaniss, Deborah

    2010-01-01

    Objective: The way in which the competencies for psychodynamic psychotherapy specified by the Psychiatry Residency Review Committee of the Accreditation Council for Graduate Medical Education translate into the day-to-day work of individual supervision remains unstudied and unspecified. The authors hypothesized that despite the existence of…

  5. Pre-trained Convolutional Networks and generative statiscial models: a study in semi-supervised learning

    OpenAIRE

    John Michael Salgado Cebola

    2016-01-01

    Comparative study between the performance of Convolutional Networks using pretrained models and statistical generative models on tasks of image classification in semi-supervised enviroments.Study of multiple ensembles using these techniques and generated data from estimated pdfs.Pretrained Convents, LDA, pLSA, Fisher Vectors, Sparse-coded SPMs, TSVMs being the key models worked upon.

  6. Fieldwork online: a GIS-based electronic learning environment for supervising fieldwork

    NARCIS (Netherlands)

    Alberti, K.; Marra, W.A.; Baarsma, R.J.; Karssenberg, D.J.

    2016-01-01

    Fieldwork comes in many forms: individual research projects in unique places, large groups of students on organized fieldtrips, and everything in between those extremes. Supervising students in often distant places can be a logistical challenge and requires a significant time investment of their

  7. Enabling Connections in Postgraduate Supervision for an Applied eLearning Professional Development Programme

    Science.gov (United States)

    Donnelly, Roisin

    2013-01-01

    This article describes the practice of postgraduate supervision on a blended professional development programme for academics, and discusses how connectivism has been a useful lens to explore a complex form of instruction. By examining the processes by which supervisors and their students on a two-year part-time masters in Applied eLearning…

  8. An Early Historical Examination of the Educational Intent of Supervised Agricultural Experiences (SAEs) and Project-Based Learning in Agricultural Education

    Science.gov (United States)

    Smith, Kasee L.; Rayfield, John

    2016-01-01

    Project-based learning has been a component of agricultural education since its inception. In light of the current call for additional emphasis of the Supervised Agricultural Experience (SAE) component of agricultural education, there is a need to revisit the roots of project-based learning. This early historical research study was conducted to…

  9. Unsupervised adaptation of brain machine interface decoders

    Directory of Open Access Journals (Sweden)

    Tayfun eGürel

    2012-11-01

    Full Text Available The performance of neural decoders can degrade over time due to nonstationarities in the relationship between neuronal activity and behavior. In this case, brain-machine interfaces (BMI require adaptation of their decoders to maintain high performance across time. One way to achieve this is by use of periodical calibration phases, during which the BMI system (or an external human demonstrator instructs the user to perform certain movements or behaviors. This approach has two disadvantages: (i calibration phases interrupt the autonomous operation of the BMI and (ii between two calibration phases the BMI performance might not be stable but continuously decrease. A better alternative would be that the BMI decoder is able to continuously adapt in an unsupervised manner during autonomous BMI operation, i.e. without knowing the movement intentions of the user. In the present article, we present an efficient method for such unsupervised training of BMI systems for continuous movement control. The proposed method utilizes a cost function derived from neuronal recordings, which guides a learning algorithm to evaluate the decoding parameters. We verify the performance of our adaptive method by simulating a BMI user with an optimal feedback control model and its interaction with our adaptive BMI decoder. The simulation results show that the cost function and the algorithm yield fast and precise trajectories towards targets at random orientations on a 2-dimensional computer screen. For initially unknown and nonstationary tuning parameters, our unsupervised method is still able to generate precise trajectories and to keep its performance stable in the long term. The algorithm can optionally work also with neuronal error signals instead or in conjunction with the proposed unsupervised adaptation.

  10. Unsupervised algorithms for intrusion detection and identification in wireless ad hoc sensor networks

    Science.gov (United States)

    Hortos, William S.

    2009-05-01

    In previous work by the author, parameters across network protocol layers were selected as features in supervised algorithms that detect and identify certain intrusion attacks on wireless ad hoc sensor networks (WSNs) carrying multisensor data. The algorithms improved the residual performance of the intrusion prevention measures provided by any dynamic key-management schemes and trust models implemented among network nodes. The approach of this paper does not train algorithms on the signature of known attack traffic, but, instead, the approach is based on unsupervised anomaly detection techniques that learn the signature of normal network traffic. Unsupervised learning does not require the data to be labeled or to be purely of one type, i.e., normal or attack traffic. The approach can be augmented to add any security attributes and quantified trust levels, established during data exchanges among nodes, to the set of cross-layer features from the WSN protocols. A two-stage framework is introduced for the security algorithms to overcome the problems of input size and resource constraints. The first stage is an unsupervised clustering algorithm which reduces the payload of network data packets to a tractable size. The second stage is a traditional anomaly detection algorithm based on a variation of support vector machines (SVMs), whose efficiency is improved by the availability of data in the packet payload. In the first stage, selected algorithms are adapted to WSN platforms to meet system requirements for simple parallel distributed computation, distributed storage and data robustness. A set of mobile software agents, acting like an ant colony in securing the WSN, are distributed at the nodes to implement the algorithms. The agents move among the layers involved in the network response to the intrusions at each active node and trustworthy neighborhood, collecting parametric values and executing assigned decision tasks. This minimizes the need to move large amounts

  11. Clinical supervision in a community setting.

    Science.gov (United States)

    Evans, Carol; Marcroft, Emma

    Clinical supervision is a formal process of professional support, reflection and learning that contributes to individual development. First Community Health and Care is committed to providing clinical supervision to nurses and allied healthcare professionals to support the provision and maintenance of high-quality care. In 2012, we developed new guidelines for nurses and AHPs on supervision, incorporating a clinical supervision framework. This offers a range of options to staff so supervision accommodates variations in work settings and individual learning needs and styles.

  12. Graphs in machine learning: an introduction

    CERN Document Server

    Latouche, Pierre

    2015-01-01

    Graphs are commonly used to characterise interactions between objects of interest. Because they are based on a straightforward formalism, they are used in many scientific fields from computer science to historical sciences. In this paper, we give an introduction to some methods relying on graphs for learning. This includes both unsupervised and supervised methods. Unsupervised learning algorithms usually aim at visualising graphs in latent spaces and/or clustering the nodes. Both focus on extracting knowledge from graph topologies. While most existing techniques are only applicable to static graphs, where edges do not evolve through time, recent developments have shown that they could be extended to deal with evolving networks. In a supervised context, one generally aims at inferring labels or numerical values attached to nodes using both the graph and, when they are available, node characteristics. Balancing the two sources of information can be challenging, especially as they can disagree locally or globall...

  13. SU-F-R-08: Can Normalization of Brain MRI Texture Features Reduce Scanner-Dependent Effects in Unsupervised Machine Learning?

    Energy Technology Data Exchange (ETDEWEB)

    Ogden, K; O’Dwyer, R [SUNY Upstate Medical University, Syracuse, NY (United States); Bradford, T [Syracuse University, Syracuse, NY (United States); Cussen, L [Rochester Institute of Technology, Rochester, NY (United States)

    2016-06-15

    Purpose: To reduce differences in features calculated from MRI brain scans acquired at different field strengths with or without Gadolinium contrast. Methods: Brain scans were processed for 111 epilepsy patients to extract hippocampus and thalamus features. Scans were acquired on 1.5 T scanners with Gadolinium contrast (group A), 1.5T scanners without Gd (group B), and 3.0 T scanners without Gd (group C). A total of 72 features were extracted. Features were extracted from original scans and from scans where the image pixel values were rescaled to the mean of the hippocampi and thalami values. For each data set, cluster analysis was performed on the raw feature set and for feature sets with normalization (conversion to Z scores). Two methods of normalization were used: The first was over all values of a given feature, and the second by normalizing within the patient group membership. The clustering software was configured to produce 3 clusters. Group fractions in each cluster were calculated. Results: For features calculated from both the non-rescaled and rescaled data, cluster membership was identical for both the non-normalized and normalized data sets. Cluster 1 was comprised entirely of Group A data, Cluster 2 contained data from all three groups, and Cluster 3 contained data from only groups 1 and 2. For the categorically normalized data sets there was a more uniform distribution of group data in the three Clusters. A less pronounced effect was seen in the rescaled image data features. Conclusion: Image Rescaling and feature renormalization can have a significant effect on the results of clustering analysis. These effects are also likely to influence the results of supervised machine learning algorithms. It may be possible to partly remove the influence of scanner field strength and the presence of Gadolinium based contrast in feature extraction for radiomics applications.

  14. Development of a Late-Life Dementia Prediction Index with Supervised Machine Learning in the Population-Based CAIDE Study

    Science.gov (United States)

    Pekkala, Timo; Hall, Anette; Lötjönen, Jyrki; Mattila, Jussi; Soininen, Hilkka; Ngandu, Tiia; Laatikainen, Tiina; Kivipelto, Miia; Solomon, Alina

    2016-01-01

    Background and objective: This study aimed to develop a late-life dementia prediction model using a novel validated supervised machine learning method, the Disease State Index (DSI), in the Finnish population-based CAIDE study. Methods: The CAIDE study was based on previous population-based midlife surveys. CAIDE participants were re-examined twice in late-life, and the first late-life re-examination was used as baseline for the present study. The main study population included 709 cognitively normal subjects at first re-examination who returned to the second re-examination up to 10 years later (incident dementia n = 39). An extended population (n = 1009, incident dementia 151) included non-participants/non-survivors (national registers data). DSI was used to develop a dementia index based on first re-examination assessments. Performance in predicting dementia was assessed as area under the ROC curve (AUC). Results: AUCs for DSI were 0.79 and 0.75 for main and extended populations. Included predictors were cognition, vascular factors, age, subjective memory complaints, and APOE genotype. Conclusion: The supervised machine learning method performed well in identifying comprehensive profiles for predicting dementia development up to 10 years later. DSI could thus be useful for identifying individuals who are most at risk and may benefit from dementia prevention interventions. PMID:27802228

  15. Development of a Late-Life Dementia Prediction Index with Supervised Machine Learning in the Population-Based CAIDE Study.

    Science.gov (United States)

    Pekkala, Timo; Hall, Anette; Lötjönen, Jyrki; Mattila, Jussi; Soininen, Hilkka; Ngandu, Tiia; Laatikainen, Tiina; Kivipelto, Miia; Solomon, Alina

    2017-01-01

    This study aimed to develop a late-life dementia prediction model using a novel validated supervised machine learning method, the Disease State Index (DSI), in the Finnish population-based CAIDE study. The CAIDE study was based on previous population-based midlife surveys. CAIDE participants were re-examined twice in late-life, and the first late-life re-examination was used as baseline for the present study. The main study population included 709 cognitively normal subjects at first re-examination who returned to the second re-examination up to 10 years later (incident dementia n = 39). An extended population (n = 1009, incident dementia 151) included non-participants/non-survivors (national registers data). DSI was used to develop a dementia index based on first re-examination assessments. Performance in predicting dementia was assessed as area under the ROC curve (AUC). AUCs for DSI were 0.79 and 0.75 for main and extended populations. Included predictors were cognition, vascular factors, age, subjective memory complaints, and APOE genotype. The supervised machine learning method performed well in identifying comprehensive profiles for predicting dementia development up to 10 years later. DSI could thus be useful for identifying individuals who are most at risk and may benefit from dementia prevention interventions.

  16. Cloud detection in all-sky images via multi-scale neighborhood features and multiple supervised learning techniques

    Science.gov (United States)

    Cheng, Hsu-Yung; Lin, Chih-Lung

    2017-01-01

    Cloud detection is important for providing necessary information such as cloud cover in many applications. Existing cloud detection methods include red-to-blue ratio thresholding and other classification-based techniques. In this paper, we propose to perform cloud detection using supervised learning techniques with multi-resolution features. One of the major contributions of this work is that the features are extracted from local image patches with different sizes to include local structure and multi-resolution information. The cloud models are learned through the training process. We consider classifiers including random forest, support vector machine, and Bayesian classifier. To take advantage of the clues provided by multiple classifiers and various levels of patch sizes, we employ a voting scheme to combine the results to further increase the detection accuracy. In the experiments, we have shown that the proposed method can distinguish cloud and non-cloud pixels more accurately compared with existing works.

  17. Poster abstract: Water level estimation in urban ultrasonic/passive infrared flash flood sensor networks using supervised learning

    KAUST Repository

    Mousa, Mustafa

    2014-04-01

    This article describes a machine learning approach to water level estimation in a dual ultrasonic/passive infrared urban flood sensor system. We first show that an ultrasonic rangefinder alone is unable to accurately measure the level of water on a road due to thermal effects. Using additional passive infrared sensors, we show that ground temperature and local sensor temperature measurements are sufficient to correct the rangefinder readings and improve the flood detection performance. Since floods occur very rarely, we use a supervised learning approach to estimate the correction to the ultrasonic rangefinder caused by temperature fluctuations. Preliminary data shows that water level can be estimated with an absolute error of less than 2 cm. © 2014 IEEE.

  18. Cavity contour segmentation in chest radiographs using supervised learning and dynamic programming

    Energy Technology Data Exchange (ETDEWEB)

    Maduskar, Pragnya, E-mail: pragnya.maduskar@radboudumc.nl; Hogeweg, Laurens; Sánchez, Clara I.; Ginneken, Bram van [Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, 6525 GA (Netherlands); Jong, Pim A. de [Department of Radiology, University Medical Center Utrecht, 3584 CX (Netherlands); Peters-Bax, Liesbeth [Department of Radiology, Radboud University Medical Center, Nijmegen, 6525 GA (Netherlands); Dawson, Rodney [University of Cape Town Lung Institute, Cape Town 7700 (South Africa); Ayles, Helen [Department of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London WC1E 7HT (United Kingdom)

    2014-07-15

    Purpose: Efficacy of tuberculosis (TB) treatment is often monitored using chest radiography. Monitoring size of cavities in pulmonary tuberculosis is important as the size predicts severity of the disease and its persistence under therapy predicts relapse. The authors present a method for automatic cavity segmentation in chest radiographs. Methods: A two stage method is proposed to segment the cavity borders, given a user defined seed point close to the center of the cavity. First, a supervised learning approach is employed to train a pixel classifier using texture and radial features to identify the border pixels of the cavity. A likelihood value of belonging to the cavity border is assigned to each pixel by the classifier. The authors experimented with four different classifiers:k-nearest neighbor (kNN), linear discriminant analysis (LDA), GentleBoost (GB), and random forest (RF). Next, the constructed likelihood map was used as an input cost image in the polar transformed image space for dynamic programming to trace the optimal maximum cost path. This constructed path corresponds to the segmented cavity contour in image space. Results: The method was evaluated on 100 chest radiographs (CXRs) containing 126 cavities. The reference segmentation was manually delineated by an experienced chest radiologist. An independent observer (a chest radiologist) also delineated all cavities to estimate interobserver variability. Jaccard overlap measure Ω was computed between the reference segmentation and the automatic segmentation; and between the reference segmentation and the independent observer's segmentation for all cavities. A median overlap Ω of 0.81 (0.76 ± 0.16), and 0.85 (0.82 ± 0.11) was achieved between the reference segmentation and the automatic segmentation, and between the segmentations by the two radiologists, respectively. The best reported mean contour distance and Hausdorff distance between the reference and the automatic segmentation were

  19. Adaptation and validation of the instrument Clinical Learning Environment and Supervision for medical students in primary health care.

    Science.gov (United States)

    Öhman, Eva; Alinaghizadeh, Hassan; Kaila, Päivi; Hult, Håkan; Nilsson, Gunnar H; Salminen, Helena

    2016-12-01

    Clinical learning takes place in complex socio-cultural environments that are workplaces for the staff and learning places for the students. In the clinical context, the students learn by active participation and in interaction with the rest of the community at the workplace. Clinical learning occurs outside the university, therefore is it important for both the university and the student that the student is given opportunities to evaluate the clinical placements with an instrument that allows evaluation from many perspectives. The instrument Clinical Learning Environment and Supervision (CLES) was originally developed for evaluation of nursing students' clinical learning environment. The aim of this study was to adapt and validate the CLES instrument to measure medical students' perceptions of their learning environment in primary health care. In the adaptation process the face validity was tested by an expert panel of primary care physicians, who were also active clinical supervisors. The adapted CLES instrument with 25 items and six background questions was sent electronically to 1,256 medical students from one university. Answers from 394 students were eligible for inclusion. Exploratory factor analysis based on principal component methods followed by oblique rotation was used to confirm the adequate number of factors in the data. Construct validity was assessed by factor analysis. Confirmatory factor analysis was used to confirm the dimensions of CLES instrument. The construct validity showed a clearly indicated four-factor model. The cumulative variance explanation was 0.65, and the overall Cronbach's alpha was 0.95. All items loaded similarly with the dimensions in the non-adapted CLES except for one item that loaded to another dimension. The CLES instrument in its adapted form had high construct validity and high reliability and internal consistency. CLES, in its adapted form, appears to be a valid instrument to evaluate medical students' perceptions of

  20. Adaptation and validation of the instrument Clinical Learning Environment and Supervision for medical students in primary health care

    Directory of Open Access Journals (Sweden)

    Eva Öhman

    2016-12-01

    Full Text Available Abstract Background Clinical learning takes place in complex socio-cultural environments that are workplaces for the staff and learning places for the students. In the clinical context, the students learn by active participation and in interaction with the rest of the community at the workplace. Clinical learning occurs outside the university, therefore is it important for both the university and the student that the student is given opportunities to evaluate the clinical placements with an instrument that allows evaluation from many perspectives. The instrument Clinical Learning Environment and Supervision (CLES was originally developed for evaluation of nursing students’ clinical learning environment. The aim of this study was to adapt and validate the CLES instrument to measure medical students’ perceptions of their learning environment in primary health care. Methods In the adaptation process the face validity was tested by an expert panel of primary care physicians, who were also active clinical supervisors. The adapted CLES instrument with 25 items and six background questions was sent electronically to 1,256 medical students from one university. Answers from 394 students were eligible for inclusion. Exploratory factor analysis based on principal component methods followed by oblique rotation was used to confirm the adequate number of factors in the data. Construct validity was assessed by factor analysis. Confirmatory factor analysis was used to confirm the dimensions of CLES instrument. Results The construct validity showed a clearly indicated four-factor model. The cumulative variance explanation was 0.65, and the overall Cronbach’s alpha was 0.95. All items loaded similarly with the dimensions in the non-adapted CLES except for one item that loaded to another dimension. The CLES instrument in its adapted form had high construct validity and high reliability and internal consistency. Conclusion CLES, in its adapted form, appears

  1. Unsupervised self-care predicts conduct problems: The moderating roles of hostile aggression and gender.

    Science.gov (United States)

    Atherton, Olivia E; Schofield, Thomas J; Sitka, Angela; Conger, Rand D; Robins, Richard W

    2016-04-01

    Despite widespread speculation about the detrimental effect of unsupervised self-care on adolescent outcomes, little is known about which children are particularly prone to problem behaviors when left at home without adult supervision. The present research used data from a longitudinal study of 674 Mexican-origin children residing in the United States to examine the prospective effect of unsupervised self-care on conduct problems, and the moderating roles of hostile aggression and gender. Results showed that unsupervised self-care was related to increases over time in conduct problems such as lying, stealing, and bullying. However, unsupervised self-care only led to conduct problems for boys and for children with an aggressive temperament. The main and interactive effects held for both mother-reported and observational-rated hostile aggression and after controlling for potential confounds.

  2. A Review of Unsupervised Approaches of Opinion Target Extraction from Unstructured Reviews

    Directory of Open Access Journals (Sweden)

    Khairullah Khan

    2014-03-01

    Full Text Available Opinion targets identification is an important task of the opinion mining problem. Several approaches have been employed for this task, which can be broadly divided into two major categories: supervised and unsupervised. The supervised approaches require training data, which need manual work and are mostly domain dependent. The unsupervised technique is most popularly used due to its two main advantages: domain independent and no need for training data. This study presents a review of the state of the art unsupervised approaches for opinion target identification due to its potential applications in opinion mining from web documents. This study compares the existing approaches that might be helpful in the future research work of opinion mining and features extraction.

  3. Fieldwork online: a GIS-based electronic learning environment for supervising fieldwork

    Science.gov (United States)

    Alberti, Koko; Marra, Wouter; Baarsma, Rein; Karssenberg, Derek

    2016-04-01

    Fieldwork comes in many forms: individual research projects in unique places, large groups of students on organized fieldtrips, and everything in between those extremes. Supervising students in often distant places can be a logistical challenge and requires a significant time investment of their supervisors. We developed an online application for remote supervision of students on fieldwork. In our fieldworkonline webapp, which is accessible through a web browser, students can upload their field data in the form of a spreadsheet with coordinates (in a system of choice) and data-fields. Field data can be any combination of quantitative or qualitative data, and can contain references to photos or other documents uploaded to the app. The student's data is converted to a map with data-points that contain all the data-fields and links to photos and documents associated with that location. Supervisors can review the data of their students and provide feedback on observations, or geo-referenced feedback on the map. Similarly, students can ask geo-referenced questions to their supervisors. Furthermore, supervisors can choose different basemaps or upload their own. Fieldwork online is a useful tool for supervising students at a distant location in the field and is most suitable for first-order feedback on students' observations, can be used to guide students to interesting locations, and allows for short discussions on phenomena observed in the field. We seek user that like to use this system, we are able to provide support and add new features if needed. The website is built and controlled using Flask, an open-source Python Framework. The maps are generated and controlled using MapServer and OpenLayers, and the database is built in PostgreSQL with PostGIS support. Fieldworkonline and all tools used to create it are open-source. Experience fieldworkonline at our demo during this session, or online at fieldworkonline.geo.uu.nl (username: EGU2016, password: Vienna).

  4. Development and psychometric testing of the Clinical Learning Environment, Supervision and Nurse Teacher evaluation scale (CLES+T): the Spanish version.

    Science.gov (United States)

    Vizcaya-Moreno, M Flores; Pérez-Cañaveras, Rosa M; De Juan, Joaquín; Saarikoski, Mikko

    2015-01-01

    The Clinical Learning Environment, Supervision and Nurse Teacher scale is a reliable and valid instrument to evaluate the quality of the clinical learning process in international nursing education contexts. This paper reports the development and psychometric testing of the Spanish version of the Clinical Learning Environment, Supervision and Nurse Teacher scale. Cross-sectional validation study of the scale. 10 public and private hospitals in the Alicante area, and the Faculty of Health Sciences (University of Alicante, Spain). 370 student nurses on clinical placement (January 2011-March 2012). The Clinical Learning Environment, Supervision and Nurse Teacher scale was translated using the modified direct translation method. Statistical analyses were performed using PASW Statistics 18 and AMOS 18.0.0 software. A multivariate analysis was conducted in order to assess construct validity. Cronbach's alpha coefficient was used to evaluate instrument reliability. An exploratory factorial analysis identified the five dimensions from the original version, and explained 66.4% of the variance. Confirmatory factor analysis supported the factor structure of the Spanish version of the instrument. Cronbach's alpha coefficient for the scale was .95, ranging from .80 to .97 for the subscales. This version of the Clinical Learning Environment, Supervision and Nurse Teacher scale instrument showed acceptable psychometric properties for use as an assessment scale in Spanish-speaking countries. Copyright © 2014 Elsevier Ltd. All rights reserved.

  5. Learning in the Absence of Direct Supervision: Person-Dependent Scaffolding

    Science.gov (United States)

    Palesy, Debra

    2017-01-01

    Contemporary accounts of learning emphasise the importance of immediate social partners such as teachers and co-workers. Yet, much of our learning for work occurs without such experts. This paper provides an understanding of how and why new home care workers use scaffolding to learn and enact safe manual handling techniques in their workplaces,…

  6. Bilingual Lexical Interactions in an Unsupervised Neural Network Model

    Science.gov (United States)

    Zhao, Xiaowei; Li, Ping

    2010-01-01

    In this paper we present an unsupervised neural network model of bilingual lexical development and interaction. We focus on how the representational structures of the bilingual lexicons can emerge, develop, and interact with each other as a function of the learning history. The results show that: (1) distinct representations for the two lexicons…

  7. Bilingual Lexical Interactions in an Unsupervised Neural Network Model

    Science.gov (United States)

    Zhao, Xiaowei; Li, Ping

    2010-01-01

    In this paper we present an unsupervised neural network model of bilingual lexical development and interaction. We focus on how the representational structures of the bilingual lexicons can emerge, develop, and interact with each other as a function of the learning history. The results show that: (1) distinct representations for the two lexicons…

  8. Enhancing Time Series Clustering by Incorporating Multiple Distance Measures with Semi-Supervised Learning

    Institute of Scientific and Technical Information of China (English)

    周竞; 朱山风; 黄晓地; 张彦春

    2015-01-01

    Time series clustering is widely applied in various areas. Existing researches focus mainly on distance measures between two time series, such as dynamic time warping (DTW) based methods, edit-distance based methods, and shapelets-based methods. In this work, we experimentally demonstrate, for the first time, that no single distance measure performs significantly better than others on clustering datasets of time series where spectral clustering is used. As such, a question arises as to how to choose an appropriate measure for a given dataset of time series. To answer this question, we propose an integration scheme that incorporates multiple distance measures using semi-supervised clustering. Our approach is able to integrate all the measures by extracting valuable underlying information for the clustering. To the best of our knowledge, this work demonstrates for the first time that the semi-supervised clustering method based on constraints is able to enhance time series clustering by combining multiple distance measures. Having tested on clustering various time series datasets, we show that our method outperforms individual measures, as well as typical integration approaches.

  9. Deep learning in neural networks: an overview.

    Science.gov (United States)

    Schmidhuber, Jürgen

    2015-01-01

    In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.

  10. Algorithm of Supervised Learning on Outlier Manifold%有监督的噪音流形学习算法

    Institute of Scientific and Technical Information of China (English)

    黄添强; 李凯; 郑之

    2011-01-01

    流形学习算法是维度约简与数据可视化领域的重要工具,提高算法的效率与健壮性对其实际应用有积极意义.经典的流形学习算法普遍的对噪音点较为敏感,现有的改进算法尚存在不足.本文提出一种基于监督学习与核函数的健壮流形学习算法,把核方法与监督学习引入降维过程,利用已知标签数据信息与核函数特性,使得同类样本变得紧密,不同类样本变成分散,提高后续分类任务的效果,降低算法对流形上噪音的敏感性.在UCI数据与白血病拉曼光谱数据上的实验表明本文改进的算法具有更高的抗噪性.%Manifold learning algorithm is an important tool in the field of dimension reduction and data visualization. Improving the algorithm's efficiency and robustness is of positive significance to its practical application. Classical manifold learning algorithm is sensitive to noise points,and its improved algorithms have been imperfect. This paper presents a robust manifold learning algorithm based on supervised learning and kernel function. It introduces nuclear methods and supervised learning into the dimensionality reduction ,and takes full advantage of the label of some data and the property of kernel function. The proposed algorithm can make close and same types of samples and distribute different types of samples,thus to improves the effect of the classification task and reduce the noise sensitivity of outliers on manifold. The experiments on the UCI data and Raman data of leukemia reveal that the algorithm has better noise immunity.

  11. Enhancing the Standard of Teaching and Learning in the 21st Century via Qualitative School-Based Supervision in Secondary Schools in Abuja Municipal Area Council (AMAC)

    Science.gov (United States)

    Ebele, Uju F.; Olofu, Paul A.

    2017-01-01

    The study focused on enhancing the standard of teaching and learning in the 21st century via qualitative school-based supervision in secondary schools in Abuja municipal area council. To guide the study, two null hypotheses were formulated. A descriptive survey research design was adopted. The sample of the study constituted of 270 secondary…

  12. Exploration of joint redundancy but not task space variability facilitates supervised motor learning.

    Science.gov (United States)

    Singh, Puneet; Jana, Sumitash; Ghosal, Ashitava; Murthy, Aditya

    2016-12-13

    The number of joints and muscles in a human arm is more than what is required for reaching to a desired point in 3D space. Although previous studies have emphasized how such redundancy and the associated flexibility may play an important role in path planning, control of noise, and optimization of motion, whether and how redundancy might promote motor learning has not been investigated. In this work, we quantify redundancy space and investigate its significance and effect on motor learning. We propose that a larger redundancy space leads to faster learning across subjects. We observed this pattern in subjects learning novel kinematics (visuomotor adaptation) and dynamics (force-field adaptation). Interestingly, we also observed differences in the redundancy space between the dominant hand and nondominant hand that explained differences in the learning of dynamics. Taken together, these results provide support for the hypothesis that redundancy aids in motor learning and that the redundant component of motor variability is not noise.

  13. Reflections on Doctoral Supervision: Drawing from the Experiences of Students with Additional Learning Needs in Two Universities

    Science.gov (United States)

    Collins, Bethan

    2015-01-01

    Supervision is an essential part of doctoral study, consisting of relationship and process aspects, underpinned by a range of values. To date there has been limited research specifically about disabled doctoral students' experiences of supervision. This paper draws on qualitative, narrative interviews about doctoral supervision with disabled…

  14. Unsupervised pattern recognition in continuous seismic wavefield records using Self-Organizing Maps

    Science.gov (United States)

    Köhler, Andreas; Ohrnberger, Matthias; Scherbaum, Frank

    2010-09-01

    Modern acquisition of seismic data on receiver networks worldwide produces an increasing amount of continuous wavefield recordings. In addition to manual data inspection, seismogram interpretation requires therefore new processing utilities for event detection, signal classification and data visualization. The use of machine learning techniques automatises decision processes and reveals the statistical properties of data. This approach is becoming more and more important and valuable for large and complex seismic records. Unsupervised learning allows the recognition of wavefield patterns, such as short-term transients and long-term variations, with a minimum of domain knowledge. This study applies an unsupervised pattern recognition approach for the discovery, imaging and interpretation of temporal patterns in seismic array recordings. For this purpose, the data is parameterized by feature vectors, which combine different real-valued wavefield attributes for short time windows. Standard seismic analysis tools are used as feature generation methods, such as frequency-wavenumber, polarization and spectral analysis. We use Self-Organizing Maps (SOMs) for a data-driven feature selection, visualization and clustering procedure. The application to continuous recordings of seismic signals from an active volcano (Mount Merapi, Java, Indonesia) shows that volcano-tectonic and rockfall events can be detected and distinguished by clustering the feature vectors. Similar results are obtained in terms of correctly classifying events compared to a previously implemented supervised classification system. Furthermore, patterns in the background wavefield, that is the 24-hr cycle due to human activity, are intuitively visualized by means of the SOM representation. Finally, we apply our technique to an ambient seismic vibration record, which has been acquired for local site characterization. Disturbing wavefield patterns are identified which affect the quality of Love wave dispersion

  15. Evaluation of supervised machine-learning algorithms to distinguish between inflammatory bowel disease and alimentary lymphoma in cats.

    Science.gov (United States)

    Awaysheh, Abdullah; Wilcke, Jeffrey; Elvinger, François; Rees, Loren; Fan, Weiguo; Zimmerman, Kurt L

    2016-11-01

    Inflammatory bowel disease (IBD) and alimentary lymphoma (ALA) are common gastrointestinal diseases in cats. The very similar clinical signs and histopathologic features of these diseases make the distinction between them diagnostically challenging. We tested the use of supervised machine-learning algorithms to differentiate between the 2 diseases using data generated from noninvasive diagnostic tests. Three prediction models were developed using 3 machine-learning algorithms: naive Bayes, decision trees, and artificial neural networks. The models were trained and tested on data from complete blood count (CBC) and serum chemistry (SC) results for the following 3 groups of client-owned cats: normal, inflammatory bowel disease (IBD), or alimentary lymphoma (ALA). Naive Bayes and artificial neural networks achieved higher classification accuracy (sensitivities of 70.8% and 69.2%, respectively) than the decision tree algorithm (63%, p machine learning provided a method for distinguishing between ALA-IBD, ALA-normal, and IBD-normal. The naive Bayes and artificial neural networks classifiers used 10 and 4 of the CBC and SC variables, respectively, to outperform the C4.5 decision tree, which used 5 CBC and SC variables in classifying cats into the 3 classes. These models can provide another noninvasive diagnostic tool to assist clinicians with differentiating between IBD and ALA, and between diseased and nondiseased cats. © 2016 The Author(s).

  16. Analysed potential of big data and supervised machine learning techniques in effectively forecasting travel times from fused data

    Directory of Open Access Journals (Sweden)

    Ivana Šemanjski

    2015-12-01

    Full Text Available Travel time forecasting is an interesting topic for many ITS services. Increased availability of data collection sensors increases the availability of the predictor variables but also highlights the high processing issues related to this big data availability. In this paper we aimed to analyse the potential of big data and supervised machine learning techniques in effectively forecasting travel times. For this purpose we used fused data from three data sources (Global Positioning System vehicles tracks, road network infrastructure data and meteorological data and four machine learning techniques (k-nearest neighbours, support vector machines, boosting trees and random forest. To evaluate the forecasting results we compared them in-between different road classes in the context of absolute values, measured in minutes, and the mean squared percentage error. For the road classes with the high average speed and long road segments, machine learning techniques forecasted travel times with small relative error, while for the road classes with the small average speeds and segment lengths this was a more demanding task. All three data sources were proven itself to have a high impact on the travel time forecast accuracy and the best results (taking into account all road classes were achieved for the k-nearest neighbours and random forest techniques.

  17. Multiclass Semi-Supervised Learning on Graphs using Ginzburg-Landau Functional Minimization

    CERN Document Server

    Garcia-Cardona, Cristina; Percus, Allon G

    2013-01-01

    We present a graph-based variational algorithm for classification of high-dimensional data, generalizing the binary diffuse interface model to the case of multiple classes. Motivated by total variation techniques, the method involves minimizing an energy functional made up of three terms. The first two terms promote a stepwise continuous classification function with sharp transitions between classes, while preserving symmetry among the class labels. The third term is a data fidelity term, allowing us to incorporate prior information into the model in a semi-supervised framework. The performance of the algorithm on synthetic data, as well as on the COIL and MNIST benchmark datasets, is competitive with state-of-the-art graph-based multiclass segmentation methods.

  18. Student experiences in learning person-centred care of patients with Alzheimer's disease as perceived by nursing students and supervising nurses.

    Science.gov (United States)

    Skaalvik, Mari W; Normann, Hans Ketil; Henriksen, Nils

    2010-09-01

    The aims and objectives of this paper are to illuminate and discuss the experiences and perceptions of nursing students and supervising nurses regarding the students' learning of person- centred care of patients with Alzheimer's disease in a teaching nursing home. This information is then used to develop recommendations as to how student learning could be improved. The clinical experiences of nursing students are an important part of learning person-centred care. Caring for patients with Alzheimer's disease may cause frustration, sadness, fear and empathy. Person-centred care can be learned in clinical practice. A qualitative study. The study was performed in 2006 using field work with field notes and qualitative interviews with seven-fifth-semester nursing students and six supervising nurses. This study determined the variation in the perceptions of nursing students and supervising nurses with regards to the students' expertise in caring for patients with Alzheimer's disease. The nursing students experienced limited learning regarding person-centred approaches in caring for patients with Alzheimer's disease. However, the supervising nurses perceived the teaching nursing home as a site representing multiple learning opportunities in this area. Nursing students perceived limited learning outcomes because they did not observe or experience systematic person-centred approaches in caring for patients with Alzheimer's disease. It is important that measures of quality improvements in the care of patients with Alzheimer's disease are communicated and demonstrated for nursing students working in clinical practices in a teaching nursing home. Introduction of person-centred approaches is vital regarding learning outcomes for nursing students caring for patients with Alzheimer's disease. © 2010 The Authors. Journal compilation © 2010 Blackwell Publishing Ltd.

  19. Automatic learning rate adjustment for self-supervising autonomous robot control

    Science.gov (United States)

    Arras, Michael K.; Protzel, Peter W.; Palumbo, Daniel L.

    1992-01-01

    Described is an application in which an Artificial Neural Network (ANN) controls the positioning of a robot arm with five degrees of freedom by using visual feedback provided by two cameras. This application and the specific ANN model, local liner maps, are based on the work of Ritter, Martinetz, and Schulten. We extended their approach by generating a filtered, average positioning error from the continuous camera feedback and by coupling the learning rate to this error. When the network learns to position the arm, the positioning error decreases and so does the learning rate until the system stabilizes at a minimum error and learning rate. This abolishes the need for a predetermined cooling schedule. The automatic cooling procedure results in a closed loop control with no distinction between a learning phase and a production phase. If the positioning error suddenly starts to increase due to an internal failure such as a broken joint, or an environmental change such as a camera moving, the learning rate increases accordingly. Thus, learning is automatically activated and the network adapts to the new condition after which the error decreases again and learning is 'shut off'. The automatic cooling is therefore a prerequisite for the autonomy and the fault tolerance of the system.

  20. Dolanan Dance Learning on Supervising Pre-Service Teachers during Teaching Practicum Program

    Directory of Open Access Journals (Sweden)

    Nilam Cahyaningrum

    2015-01-01

    Full Text Available Taman Kanak- kanak Mekarsari (Mekarsari Kindergarten is a school that choses dolanan anak dance lesson which is taught using demonstration methods. This study aims to find, understand, and describe the process and learning outcomes of dolanan anak dance in Mekarsari Kindergarten, Kandeman District of Batang. This study uses qualitative research methods with a phenomenological approach to research sites in Mekarsari Kindergarten, Kandeman District of Batang. Data collection techniques used were observation, interview techniques, and technical documentation. Data analysis were using data reduction, data presentation, drawing conclusions, and verification. The validity test were using triangulation of data sources, techniques, and time. Dolanan anak dance learning in Mekarsari Kindergarten consists of several components, namely teaching and learning activities, goals, teachers, students, materials, methods, media, tools and learning resources, and evaluation. Dolanan dance learning was using demonstration method implemented through three stages: pre-development activities, core activities, and closing activities. The learning outcomes of dolanan anak dance learning in Mekarsari kindergarten were categorized into three aspects, namely cognitive, affective, and psychomotor. Cognitive aspects can be seen from the students’ ability to remember, memorize and understand the dance. Affective aspects include familiar levels, namely learning to know friends and dance movements, respond the movements amomg friends, and appreciate the teacher’s explanation given to each student. Psychomotor aspects can be seen from the students’ ability to imitate the dance movements, use the concept of doing the movements and precision of movements, weave movement and exercise appropriately.

  1. Dimensionality reduction with unsupervised nearest neighbors

    CERN Document Server

    Kramer, Oliver

    2013-01-01

    This book is devoted to a novel approach for dimensionality reduction based on the famous nearest neighbor method that is a powerful classification and regression approach. It starts with an introduction to machine learning concepts and a real-world application from the energy domain. Then, unsupervised nearest neighbors (UNN) is introduced as efficient iterative method for dimensionality reduction. Various UNN models are developed step by step, reaching from a simple iterative strategy for discrete latent spaces to a stochastic kernel-based algorithm for learning submanifolds with independent parameterizations. Extensions that allow the embedding of incomplete and noisy patterns are introduced. Various optimization approaches are compared, from evolutionary to swarm-based heuristics. Experimental comparisons to related methodologies taking into account artificial test data sets and also real-world data demonstrate the behavior of UNN in practical scenarios. The book contains numerous color figures to illustr...

  2. On Deep Representation Learning from Noisy Web Images

    OpenAIRE

    Vo, Phong D.; Ginsca, Alexandru; Borgne, Hervé Le; Popescu, Adrian

    2015-01-01

    The keep-growing content of Web images may be the next important data source to scale up deep neural networks, which recently obtained a great success in the ImageNet classification challenge and related tasks. This prospect, however, has not been validated on convolutional networks (convnet) -- one of best performing deep models -- because of their supervised regime. While unsupervised alternatives are not so good as convnet in generalizing the learned model to new domains, we use convnet to...

  3. A Semi-Supervised Learning Approach to Enhance Health Care Community–Based Question Answering: A Case Study in Alcoholism

    Science.gov (United States)

    Klabjan, Diego; Jonnalagadda, Siddhartha Reddy

    2016-01-01

    Background Community-based question answering (CQA) sites play an important role in addressing health information needs. However, a significant number of posted questions remain unanswered. Automatically answering the posted questions can provide a useful source of information for Web-based health communities. Objective In this study, we developed an algorithm to automatically answer health-related questions based on past questions and answers (QA). We also aimed to understand information embedded within Web-based health content that are good features in identifying valid answers. Methods Our proposed algorithm uses information retrieval techniques to identify candidate answers from resolved QA. To rank these candidates, we implemented a semi-supervised leaning algorithm that extracts the best answer to a question. We assessed this approach on a curated corpus from Yahoo! Answers and compared against a rule-based string similarity baseline. Results On our dataset, the semi-supervised learning algorithm has an accuracy of 86.2%. Unified medical language system–based (health related) features used in the model enhance the algorithm’s performance by proximately 8%. A reasonably high rate of accuracy is obtained given that the data are considerably noisy. Important features distinguishing a valid answer from an invalid answer include text length, number of stop words contained in a test question, a distance between the test question and other questions in the corpus, and a number of overlapping health-related terms between questions. Conclusions Overall, our automated QA system based on historical QA pairs is shown to be effective according to the dataset in this case study. It is developed for general use in the health care domain, which can also be applied to other CQA sites. PMID:27485666

  4. Assessment of work-integrated learning: comparison of the usage of a grading rubric by supervising radiographers and teachers

    Energy Technology Data Exchange (ETDEWEB)

    Kilgour, Andrew J, E-mail: akilgour@csu.edu.au [Charles Sturt University, Wagga Wagga, NSW (Australia); Kilgour, Peter W [Avondale College of Higher Education, Cooranbong, NSW (Australia); Gerzina, Tania [Dental Educational Research, Faculty of Dentistry, Jaw Function and Orofacial Pain Research Unit, Westmead Centre for Oral Health, C24- Westmead Hospital, The University of Sydney, Sydney, NSW, 2006 (Australia); Christian, Beverly [Avondale College of Higher Education, Cooranbong, NSW (Australia); Charles Sturt University, Wagga Wagga, NSW (Australia)

    2014-02-15

    Introduction: Professional work-integrated learning (WIL) that integrates the academic experience with off-campus professional experience placements is an integral part of many tertiary courses. Issues with the reliability and validity of assessment grades in these placements suggest that there is a need to strengthen the level of academic rigour of placements in these programmes. This study aims to compare the attitudes to the usage of assessment rubrics of radiographers supervising medical imaging students and teachers supervising pre-service teachers. Methods: WIL placement assessment practices in two programmes, pre-service teacher training (Avondale College of Higher Education, NSW) and medical diagnostic radiography (Faculty of Health Sciences, University of Sydney, NSW), were compared with a view to comparing assessment strategies across these two different educational domains. Educators (course coordinators) responsible for teaching professional development placements of teacher trainees and diagnostic radiography students developed a standards-based grading rubric designed to guide assessment of students’ work during WIL placement by assessors. After ∼12 months of implementation of the rubrics, assessors’ reaction to the effectiveness and usefulness of the grading rubric was determined using a specially created survey form. Data were collected over the period from March to June 2011. Quantitative and qualitative data found that assessors in both programmes considered the grading rubric to be a vital tool in the assessment process, though teacher supervisors were more positive about the benefits of its use than the radiographer supervisors. Results: Benefits of the grading rubric included accuracy and consistency of grading, ability to identify specific areas of desired development and facilitation of the provision of supervisor feedback. The use of assessment grading rubrics is of benefit to assessors in WIL placements from two very different

  5. Assessment of work-integrated learning: comparison of the usage of a grading rubric by supervising radiographers and teachers.

    Science.gov (United States)

    Kilgour, Andrew J; Kilgour, Peter W; Gerzina, Tania; Christian, Beverly

    2014-02-01

    IntroductionProfessional work-integrated learning (WIL) that integrates the academic experience with off-campus professional experience placements is an integral part of many tertiary courses. Issues with the reliability and validity of assessment grades in these placements suggest that there is a need to strengthen the level of academic rigour of placements in these programmes. This study aims to compare the attitudes to the usage of assessment rubrics of radiographers supervising medical imaging students and teachers supervising pre-service teachers. MethodsWIL placement assessment practices in two programmes, pre-service teacher training (Avondale College of Higher Education, NSW) and medical diagnostic radiography (Faculty of Health Sciences, University of Sydney, NSW), were compared with a view to comparing assessment strategies across these two different educational domains. Educators (course coordinators) responsible for teaching professional development placements of teacher trainees and diagnostic radiography students developed a standards-based grading rubric designed to guide assessment of students' work during WIL placement by assessors. After ∼12 months of implementation of the rubrics, assessors' reaction to the effectiveness and usefulness of the grading rubric was determined using a specially created survey form. Data were collected over the period from March to June 2011. Quantitative and qualitative data found that assessors in both programmes considered the grading rubric to be a vital tool in the assessment process, though teacher supervisors were more positive about the benefits of its use than the radiographer supervisors. ResultsBenefits of the grading rubric included accuracy and consistency of grading, ability to identify specific areas of desired development and facilitation of the provision of supervisor feedback. The use of assessment grading rubrics is of benefit to assessors in WIL placements from two very different teaching

  6. Correlates of Unsupervised Bathing of Infants: A Cross-Sectional Study

    Directory of Open Access Journals (Sweden)

    Tinneke M. J. Beirens

    2013-03-01

    Full Text Available Drowning represents the third leading cause of fatal unintentional injury in infants (0–1 years. The aim of this study is to investigate correlates of unsupervised bathing. This cross-sectional study included 1,410 parents with an infant. Parents completed a questionnaire regarding supervision during bathing, socio-demographic factors, and Protection Motivation Theory-constructs. To determine correlates of parents who leave their infant unsupervised, logistic regression analyses were performed. Of the parents, 6.2% left their child unsupervised in the bathtub. Parents with older children (OR 1.24; 95%CI 1.00–1.54 were more likely to leave their child unsupervised in the bathtub. First-time parents (OR 0.59; 95%CI 0.36–0.97 and non-Western migrant fathers (OR 0.18; 95%CI 0.05–0.63 were less likely to leave their child unsupervised in the bathtub. Furthermore, parents who perceived higher self-efficacy (OR 0.57; 95%CI 0.47–0.69, higher response efficacy (OR 0.34; 95%CI 0.24–0.48, and higher severity (OR 0.74; 95%CI 0.58–0.93 were less likely to leave their child unsupervised. Since young children are at great risk of drowning if supervision is absent, effective strategies for drowning prevention should be developed and evaluated. In the meantime, health care professionals should inform parents with regard to the importance of supervision during bathing.

  7. Unsupervised Knowledge Structuring

    DEFF Research Database (Denmark)

    Kano Glückstad, Fumiko; Herlau, Tue; Schmidt, Mikkel N.;

    2013-01-01

    This work presents a conceptual framework for learning an ontological structure of domain knowledge, which combines Jaccard similarity coefficient with the Infinite Relational Model (IRM) by (Kemp et al. 2006) and its extended model, i.e. the normal-Infinite Relational Model (n-IRM) by (Herlau et...

  8. Locally Embedding Autoencoders: A Semi-Supervised Manifold Learning Approach of Document Representation.

    Directory of Open Access Journals (Sweden)

    Chao Wei

    Full Text Available Topic models and neural networks can discover meaningful low-dimensional latent representations of text corpora; as such, they have become a key technology of document representation. However, such models presume all documents are non-discriminatory, resulting in latent representation dependent upon all other documents and an inability to provide discriminative document representation. To address this problem, we propose a semi-supervised manifold-inspired autoencoder to extract meaningful latent representations of documents, taking the local perspective that the latent representation of nearby documents should be correlative. We first determine the discriminative neighbors set with Euclidean distance in observation spaces. Then, the autoencoder is trained by joint minimization of the Bernoulli cross-entropy error between input and output and the sum of the square error between neighbors of input and output. The results of two widely used corpora show that our method yields at least a 15% improvement in document clustering and a nearly 7% improvement in classification tasks compared to comparative methods. The evidence demonstrates that our method can readily capture more discriminative latent representation of new documents. Moreover, some meaningful combinations of words can be efficiently discovered by activating features that promote the comprehensibility of latent representation.

  9. Locally Embedding Autoencoders: A Semi-Supervised Manifold Learning Approach of Document Representation.

    Science.gov (United States)

    Wei, Chao; Luo, Senlin; Ma, Xincheng; Ren, Hao; Zhang, Ji; Pan, Limin

    2016-01-01

    Topic models and neural networks can discover meaningful low-dimensional latent representations of text corpora; as such, they have become a key technology of document representation. However, such models presume all documents are non-discriminatory, resulting in latent representation dependent upon all other documents and an inability to provide discriminative document representation. To address this problem, we propose a semi-supervised manifold-inspired autoencoder to extract meaningful latent representations of documents, taking the local perspective that the latent representation of nearby documents should be correlative. We first determine the discriminative neighbors set with Euclidean distance in observation spaces. Then, the autoencoder is trained by joint minimization of the Bernoulli cross-entropy error between input and output and the sum of the square error between neighbors of input and output. The results of two widely used corpora show that our method yields at least a 15% improvement in document clustering and a nearly 7% improvement in classification tasks compared to comparative methods. The evidence demonstrates that our method can readily capture more discriminative latent representation of new documents. Moreover, some meaningful combinations of words can be efficiently discovered by activating features that promote the comprehensibility of latent representation.

  10. Classification models for clear cell renal carcinoma stage progression, based on tumor RNAseq expression trained supervised machine learning algorithms.

    Science.gov (United States)

    Jagga, Zeenia; Gupta, Dinesh

    2014-01-01

    Clear-cell Renal Cell Carcinoma (ccRCC) is the most- prevalent, chemotherapy resistant and lethal adult kidney cancer. There is a need for novel diagnostic and prognostic biomarkers for ccRCC, due to its heterogeneous molecular profiles and asymptomatic early stage. This study aims to develop classification models to distinguish early stage and late stage of ccRCC based on gene expression profiles. We employed supervised learning algorithms- J48, Random Forest, SMO and Naïve Bayes; with enriched model learning by fast correlation based feature selection to develop classification models trained on sequencing based gene expression data of RNAseq experiments, obtained from The Cancer Genome Atlas. Different models developed in the study were evaluated on the basis of 10 fold cross validations and independent dataset testing. Random Forest based prediction model performed best amongst the models developed in the study, with a sensitivity of 89%, accuracy of 77% and area under Receivers Operating Curve of 0.8. We anticipate that the prioritized subset of 62 genes and prediction models developed in this study will aid experimental oncologists to expedite understanding of the molecular mechanisms of stage progression and discovery of prognostic factors for ccRCC tumors.

  11. SAR Target Recognition via Supervised Discriminative Dictionary Learning and Sparse Representation of the SAR-HOG Feature

    Directory of Open Access Journals (Sweden)

    Shengli Song

    2016-08-01

    Full Text Available Automatic target recognition (ATR in synthetic aperture radar (SAR images plays an important role in both national defense and civil applications. Although many methods have been proposed, SAR ATR is still very challenging due to the complex application environment. Feature extraction and classification are key points in SAR ATR. In this paper, we first design a novel feature, which is a histogram of oriented gradients (HOG-like feature for SAR ATR (called SAR-HOG. Then, we propose a supervised discriminative dictionary learning (SDDL method to learn a discriminative dictionary for SAR ATR and propose a strategy to simplify the optimization problem. Finally, we propose a SAR ATR classifier based on SDDL and sparse representation (called SDDLSR, in which both the reconstruction error and the classification error are considered. Extensive experiments are performed on the MSTAR database under standard operating conditions and extended operating conditions. The experimental results show that SAR-HOG can reliably capture the structures of targets in SAR images, and SDDL can further capture subtle differences among the different classes. By virtue of the SAR-HOG feature and SDDLSR, the proposed method achieves the state-of-the-art performance on MSTAR database. Especially for the extended operating conditions (EOC scenario “Training 17 ∘ —Testing 45 ∘ ”, the proposed method improves remarkably with respect to the previous works.

  12. Supervised practice in occupational therapy in a psychosocial care center: Challenges for the assistance and the teaching and learning process

    Directory of Open Access Journals (Sweden)

    Milton Carlos Mariotti

    2014-09-01

    Full Text Available The psychiatric reform in Brazil has replaced the hospital-centered model by the reintegration of users to their respective communities. The Center of Psychosocial Care (CAPS has been the main equipment in that scope. Objectives: To report the development of Supervised Practice in Occupational Therapy in a CAPS II unit in Curitiba, Parana state, Brazil. Methods: This is an experience report. It features the training field and describes the stages of the teaching and learning process which involved institutional observation, reporting and intervention proposal, collecting data about the users’ profile and attendances. The work focused the non-intensive users because they are close to hospital discharge. Results: We found that users of the non-intensive system, rather than crave the discharge, would like to return to the semi-intensive or intensive systems, aiming to regain sickness and transportation benefits, which are lost as users make progress. This fact denotes great contradictions in the system. We also attended intensive and semi-intensive systems users. Conclusions: The students’ learning included aspects such as direct contact with the institutional reality; knowledge about the health system, its limitations and contradictions; approach to users, their families, realities, socioeconomic conditions, desires, aspirations, or lack thereof; difficulties in engaging in meaningful occupations in their territories, limitations, and social stigma; working with frustrations, reflecting about ways to change the reality; in addition to expanded clinical practice, participating in the discussions and formulation of public policies on mental healthcare and social control.

  13. On Training Targets for Supervised Speech Separation

    OpenAIRE

    Wang, Yuxuan; Narayanan, Arun; Wang, DeLiang

    2014-01-01

    Formulation of speech separation as a supervised learning problem has shown considerable promise. In its simplest form, a supervised learning algorithm, typically a deep neural network, is trained to learn a mapping from noisy features to a time-frequency representation of the target of interest. Traditionally, the ideal binary mask (IBM) is used as the target because of its simplicity and large speech intelligibility gains. The supervised learning framework, however, is not restricted to the...

  14. Integrating dynamic stopping, transfer learning and language models in an adaptive zero-training ERP speller

    Science.gov (United States)

    Kindermans, Pieter-Jan; Tangermann, Michael; Müller, Klaus-Robert; Schrauwen, Benjamin

    2014-06-01

    Objective. Most BCIs have to undergo a calibration session in which data is recorded to train decoders with machine learning. Only recently zero-training methods have become a subject of study. This work proposes a probabilistic framework for BCI applications which exploit event-related potentials (ERPs). For the example of a visual P300 speller we show how the framework harvests the structure suitable to solve the decoding task by (a) transfer learning, (b) unsupervised adaptation, (c) language model and (d) dynamic stopping. Approach. A simulation study compares the proposed probabilistic zero framework (using transfer learning and task structure) to a state-of-the-art supervised model on n = 22 subjects. The individual influence of the involved components (a)-(d) are investigated. Main results. Without any need for a calibration session, the probabilistic zero-training framework with inter-subject transfer learning shows excellent performance—competitive to a state-of-the-art supervised method using calibration. Its decoding quality is carried mainly by the effect of transfer learning in combination with continuous unsupervised adaptation. Significance. A high-performing zero-training BCI is within reach for one of the most popular BCI paradigms: ERP spelling. Recording calibration data for a supervised BCI would require valuable time which is lost for spelling. The time spent on calibration would allow a novel user to spell 29 symbols with our unsupervised approach. It could be of use for various clinical and non-clinical ERP-applications of BCI.

  15. Integrating dynamic stopping, transfer learning and language models in an adaptive zero-training ERP speller.

    Science.gov (United States)

    Kindermans, Pieter-Jan; Tangermann, Michael; Müller, Klaus-Robert; Schrauwen, Benjamin

    2014-06-01

    Most BCIs have to undergo a calibration session in which data is recorded to train decoders with machine learning. Only recently zero-training methods have become a subject of study. This work proposes a probabilistic framework for BCI applications which exploit event-related potentials (ERPs). For the example of a visual P300 speller we show how the framework harvests the structure suitable to solve the decoding task by (a) transfer learning, (b) unsupervised adaptation, (c) language model and (d) dynamic stopping. A simulation study compares the proposed probabilistic zero framework (using transfer learning and task structure) to a state-of-the-art supervised model on n = 22 subjects. The individual influence of the involved components (a)-(d) are investigated. Without any need for a calibration session, the probabilistic zero-training framework with inter-subject transfer learning shows excellent performance--competitive to a state-of-the-art supervised method using calibration. Its decoding quality is carried mainly by the effect of transfer learning in combination with continuous unsupervised adaptation. A high-performing zero-training BCI is within reach for one of the most popular BCI paradigms: ERP spelling. Recording calibration data for a supervised BCI would require valuable time which is lost for spelling. The time spent on calibration would allow a novel user to spell 29 symbols with our unsupervised approach. It could be of use for various clinical and non-clinical ERP-applications of BCI.

  16. Online adaptation of a c-VEP Brain-computer Interface(BCI) based on error-related potentials and unsupervised learning.

    Science.gov (United States)

    Spüler, Martin; Rosenstiel, Wolfgang; Bogdan, Martin

    2012-01-01

    The goal of a Brain-Computer Interface (BCI) is to control a computer by pure brain activity. Recently, BCIs based on code-modulated visual evoked potentials (c-VEPs) have shown great potential to establish high-performance communication. In this paper we present a c-VEP BCI that uses online adaptation of the classifier to reduce calibration time and increase performance. We compare two different approaches for online adaptation of the system: an unsupervised method and a method that uses the detection of error-related potentials. Both approaches were tested in an online study, in which an average accuracy of 96% was achieved with adaptation based on error-related potentials. This accuracy corresponds to an average information transfer rate of 144 bit/min, which is the highest bitrate reported so far for a non-invasive BCI. In a free-spelling mode, the subjects were able to write with an average of 21.3 error-free letters per minute, which shows the feasibility of the BCI system in a normal-use scenario. In addition we show that a calibration of the BCI system solely based on the detection of error-related potentials is possible, without knowing the true class labels.

  17. Classification and Recognition of Mint Genus Based on Unsupervised Learning Neural Network%基于无导师神经网络的薄荷植物分类识别

    Institute of Scientific and Technical Information of China (English)

    黄劲潮

    2015-01-01

    This paper selects some strongly-related attributes of different classification about Mentha plant as the training group and uses unsupervised learning competitive neural networks in neural network and the self-organizing feature map ( SOFM) neural networks to classify and identify Mentha plant, which ensures the accuracy but also plays a supplementary role in the previously adopted tutor learning neural network.%选取薄荷属植物类别区分的一些强相关属性作为训练组,采用无导师学习神经网络中的竞争神经网络与自组织特征映射( SOFM )神经网络对薄荷属植物进行分类和识别,在保证准确度的同时对先前采用有导师学习神经网络的不足起到了补充。

  18. Collective academic supervision

    DEFF Research Database (Denmark)

    Nordentoft, Helle Merete; Thomsen, Rie; Wichmann-Hansen, Gitte

    2013-01-01

    are interconnected. Collective Academic Supervision provides possibilities for systematic interaction between individual master students in their writing process. In this process they learn core academic competencies, such as the ability to assess theoretical and practical problems in their practice and present them...

  19. Hypothetical Pattern Recognition Design Using Multi-Layer Perceptorn Neural Network For Supervised Learning

    Directory of Open Access Journals (Sweden)

    Md. Abdullah-al-mamun

    2015-08-01

    Full Text Available Abstract Humans are capable to identifying diverse shape in the different pattern in the real world as effortless fashion due to their intelligence is grow since born with facing several learning process. Same way we can prepared an machine using human like brain called Artificial Neural Network that can be recognize different pattern from the real world object. Although the various techniques is exists to implementation the pattern recognition but recently the artificial neural network approaches have been giving the significant attention. Because the approached of artificial neural network is like a human brain that is learn from different observation and give a decision the previously learning rule. Over the 50 years research now a days pattern recognition for machine learning using artificial neural network got a significant achievement. For this reason many real world problem can be solve by modeling the pattern recognition process. The objective of this paper is to present the theoretical concept for pattern recognition design using Multi-Layer Perceptorn neural networkin the algorithm of artificial Intelligence as the best possible way of utilizing available resources to make a decision that can be a human like performance.

  20. Supervision of Teachers Based on Adjusted Arithmetic Learning in Special Education

    Science.gov (United States)

    Eriksson, Gota

    2008-01-01

    This article reports on 20 children's learning in arithmetic after teaching was adjusted to their conceptual development. The report covers periods from three months up to three terms in an ongoing intervention study of teachers and children in schools for the intellectually disabled and of remedial teaching in regular schools. The researcher…

  1. Clinical supervision.

    Science.gov (United States)

    Goorapah, D

    1997-05-01

    The introduction of clinical supervision to a wider sphere of nursing is being considered from a professional and organizational point of view. Positive views are being expressed about adopting this concept, although there are indications to suggest that there are also strong reservations. This paper examines the potential for its success amidst the scepticism that exists. One important question raised is whether clinical supervision will replace or run alongside other support systems.

  2. Semi-supervised clustering using soft-constraint affinity propagation

    CERN Document Server

    Leone, Michele; Weigt, Martin

    2007-01-01

    Semi-supervised clustering aims at dividing partially labeled data into groups, assigning labels to previously unlabeled points. It uses both the geometrical organization of the data set and the available labels assigned to few points, giving additional information compared to unsupervised clustering methods. In this letter, we present a novel, computationally efficient and statistically robust semi-supervised clustering algorithm based on soft-constraint affinity propagation. The method is successfully tested on artificial and biological benchmark data.

  3. Machine learning in radiation oncology theory and applications

    CERN Document Server

    El Naqa, Issam; Murphy, Martin J

    2015-01-01

    ​This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided rad

  4. Toward unsupervised adaptation of LDA for brain-computer interfaces.

    Science.gov (United States)

    Vidaurre, C; Kawanabe, M; von Bünau, P; Blankertz, B; Müller, K R

    2011-03-01

    There is a step of significant difficulty experienced by brain-computer interface (BCI) users when going from the calibration recording to the feedback application. This effect has been previously studied and a supervised adaptation solution has been proposed. In this paper, we suggest a simple unsupervised adaptation method of the linear discriminant analysis (LDA) classifier that effectively solves this problem by counteracting the harmful effect of nonclass-related nonstationarities in electroencephalography (EEG) during BCI sessions performed with motor imagery tasks. For this, we first introduce three types of adaptation procedures and investigate them in an offline study with 19 datasets. Then, we select one of the proposed methods and analyze it further. The chosen classifier is offline tested in data from 80 healthy users and four high spinal cord injury patients. Finally, for the first time in BCI literature, we apply this unsupervised classifier in online experiments. Additionally, we show that its performance is significantly better than the state-of-the-art supervised approach.

  5. Photometric classification of type Ia supernovae in the SuperNova Legacy Survey with supervised learning

    CERN Document Server

    Möller, A; Leloup, C; Neveu, J; Palanque-Delabrouille, N; Rich, J; Carlberg, R; Lidman, C; Pritchet, C

    2016-01-01

    In the era of large astronomical surveys, photometric classification of supernovae (SNe) has become an important research field due to limited spectroscopic resources for candidate follow-up and classification. In this work, we present a method to photometrically classify type Ia supernovae based on machine learning with redshifts that are derived from the SN light-curves. This method is implemented on real data from the SNLS deferred pipeline, a purely photometric pipeline that identifies SNe Ia at high-redshifts ($0.2learning classification. We study the performance of different algorithms such as Random Forest and Boosted Decision Trees. We evaluate the performance using SN simulations and real data from the first 3 years of the Supernova Legacy Survey (SNLS), which contains large spectroscopically and photometrically classified type Ia sa...

  6. Supervised Learning Approach for Spam Classification Analysis using Data Mining Tools

    Directory of Open Access Journals (Sweden)

    R.Deepa Lakshmi

    2010-12-01

    Full Text Available E-mail is one of the most popular and frequently used ways of communication due to its worldwide accessibility, relatively fast message transfer, and low sending cost. The flaws in the e-mail protocols and the increasing amount of electronic business and financial transactions directly contribute to the increase in e-mail-based threats. Email spam is one of the major problems of the today’s Internet, bringing financial damage to companies and annoying individual users. Among the approaches developed to stop spam, filtering is the one of the most important technique. Many researches in spam filtering have been centered on the more sophisticated classifierrelated issues. In recent days, Machine learning for spamclassification is an important research issue. This paper exploresand identifies the use of different learning algorithms for classifying spam messages from e-mail. A comparative analysisamong the algorithms has also been presented.

  7. Supervised Learning Approach for Spam Classification Analysis using Data Mining Tools

    Directory of Open Access Journals (Sweden)

    R.Deepa Lakshmi

    2010-11-01

    Full Text Available E-mail is one of the most popular and frequently used ways of communication due to its worldwide accessibility, relatively fast message transfer, and low sending cost. The flaws in the e-mail protocols and the increasing amount of electronic business and financial transactions directly contribute to the increase in e-mail-based threats. Email spam is one of the major problems of the today’s Internet, bringing financial damage to companies and annoying individual users. Among the approaches developed to stop spam, filtering is the one of the most important technique. Many researches in spam filtering have been centered on the more sophisticated classifierrelated issues. In recent days, Machine learning for spamclassification is an important research issue. This paper exploresand identifies the use of different learning algorithms for classifying spam messages from e-mail. A comparative analysisamong the algorithms has also been presented.

  8. Physical Realization of a Supervised Learning System Built with Organic Memristive Synapses

    Science.gov (United States)

    Lin, Yu-Pu; Bennett, Christopher H.; Cabaret, Théo; Vodenicarevic, Damir; Chabi, Djaafar; Querlioz, Damien; Jousselme, Bruno; Derycke, Vincent; Klein, Jacques-Olivier

    2016-09-01

    Multiple modern applications of electronics call for inexpensive chips that can perform complex operations on natural data with limited energy. A vision for accomplishing this is implementing hardware neural networks, which fuse computation and memory, with low cost organic electronics. A challenge, however, is the implementation of synapses (analog memories) composed of such materials. In this work, we introduce robust, fastly programmable, nonvolatile organic memristive nanodevices based on electrografted redox complexes that implement synapses thanks to a wide range of accessible intermediate conductivity states. We demonstrate experimentally an elementary neural network, capable of learning functions, which combines four pairs of organic memristors as synapses and conventional electronics as neurons. Our architecture is highly resilient to issues caused by imperfect devices. It tolerates inter-device variability and an adaptable learning rule offers immunity against asymmetries in device switching. Highly compliant with conventional fabrication processes, the system can be extended to larger computing systems capable of complex cognitive tasks, as demonstrated in complementary simulations.

  9. Manifold regularized multitask learning for semi-supervised multilabel image classification.

    Science.gov (United States)

    Luo, Yong; Tao, Dacheng; Geng, Bo; Xu, Chao; Maybank, Stephen J

    2013-02-01

    It is a significant challenge to classify images with multiple labels by using only a small number of labeled samples. One option is to learn a binary classifier for each label and use manifold regularization to improve the classification performance by exploring the underlying geometric structure of the data distribution. However, such an approach does not perform well in practice when images from multiple concepts are represented by high-dimensional visual features. Thus, manifold regularization is insufficient to control the model complexity. In this paper, we propose a manifold regularized multitask learning (MRMTL) algorithm. MRMTL learns a discriminative subspace shared by multiple classification tasks by exploiting the common structure of these tasks. It effectively controls the model complexity because different tasks limit one another's search volume, and the manifold regularization ensures that the functions in the shared hypothesis space are smooth along the data manifold. We conduct extensive experiments, on the PASCAL VOC'07 dataset with 20 classes and the MIR dataset with 38 classes, by comparing MRMTL with popular image classification algorithms. The results suggest that MRMTL is effective for image classification.

  10. a Heterosynaptic Learning Rule for Neural Networks

    Science.gov (United States)

    Emmert-Streib, Frank

    In this article we introduce a novel stochastic Hebb-like learning rule for neural networks that is neurobiologically motivated. This learning rule combines features of unsupervised (Hebbian) and supervised (reinforcement) learning and is stochastic with respect to the selection of the time points when a synapse is modified. Moreover, the learning rule does not only affect the synapse between pre- and postsynaptic neuron, which is called homosynaptic plasticity, but effects also further remote synapses of the pre- and postsynaptic neuron. This more complex form of synaptic plasticity has recently come under investigations in neurobiology and is called heterosynaptic plasticity. We demonstrate that this learning rule is useful in training neural networks by learning parity functions including the exclusive-or (XOR) mapping in a multilayer feed-forward network. We find, that our stochastic learning rule works well, even in the presence of noise. Importantly, the mean learning time increases with the number of patterns to be learned polynomially, indicating efficient learning.

  11. VDES J2325-5229 a z=2.7 gravitationally lensed quasar discovered using morphology independent supervised machine learning

    CERN Document Server

    Ostrovski, Fernanda; Connolly, Andrew J; Lemon, Cameron A; Auger, Matthew W; Banerji, Manda; Hung, Johnathan M; Koposov, Sergey E; Lidman, Christopher E; Reed, Sophie L; Allam, Sahar; Benoit-Lévy, Aurélien; Bertin, Emmanuel; Brooks, David; Buckley-Geer, Elizabeth; Rosell, Aurelio Carnero; Kind, Matias Carrasco; Carretero, Jorge; Cunha, Carlos E; da Costa, Luiz N; Desai, Shantanu; Diehl, H Thomas; Dietrich, Jörg P; Evrard, August E; Finley, David A; Flaugher, Brenna; Fosalba, Pablo; Frieman, Josh; Gerdes, David W; Goldstein, Daniel A; Gruen, Daniel; Gruendl, Robert A; Gutierrez, Gaston; Honscheid, Klaus; James, David J; Kuehn, Kyler; Kuropatkin, Nikolay; Lima, Marcos; Lin, Huan; Maia, Marcio A G; Marshall, Jennifer L; Martini, Paul; Melchior, Peter; Miquel, Ramon; Ogando, Ricardo; Malagón, Andrés Plazas; Reil, Kevin; Romer, Kathy; Sanchez, Eusebio; Santiago, Basilio; Scarpine, Vic; Sevilla-Noarbe, Ignacio; Soares-Santos, Marcelle; Sobreira, Flavia; Suchyta, Eric; Tarle, Gregory; Thomas, Daniel; Tucker, Douglas L; Walker, Alistair R

    2016-01-01

    We present the discovery and preliminary characterization of a gravitationally lensed quasar with a source redshift $z_{s}=2.74$ and image separation of $2.9"$ lensed by a foreground $z_{l}=0.40$ elliptical galaxy. Since the images of gravitationally lensed quasars are the superposition of multiple point sources and a foreground lensing galaxy, we have developed a morphology independent multi-wavelength approach to the photometric selection of lensed quasar candidates based on Gaussian Mixture Models (GMM) supervised machine learning. Using this technique and $gi$ multicolour photometric observations from the Dark Energy Survey (DES), near IR $JK$ photometry from the VISTA Hemisphere Survey (VHS) and WISE mid IR photometry, we have identified a candidate system with two catalogue components with $i_{AB}=18.61$ and $i_{AB}=20.44$ comprised of an elliptical galaxy and two blue point sources. Spectroscopic follow-up with NTT and the use of an archival AAT spectrum show that the point sources can be identified as...

  12. Extendable supervised dictionary learning for exploring diverse and concurrent brain activities in task-based fMRI.

    Science.gov (United States)

    Zhao, Shijie; Han, Junwei; Hu, Xintao; Jiang, Xi; Lv, Jinglei; Zhang, Tuo; Zhang, Shu; Guo, Lei; Liu, Tianming

    2017-06-09

    Recently, a growing body of studies have demonstrated the simultaneous existence of diverse brain activities, e.g., task-evoked dominant response activities, delayed response activities and intrinsic brain activities, under specific task conditions. However, current dominant task-based functional magnetic resonance imaging (tfMRI) analysis approach, i.e., the general linear model (GLM), might have difficulty in discovering those diverse and concurrent brain responses sufficiently. This subtraction-based model-driven approach focuses on the brain activities evoked directly from the task paradigm, thus likely overlooks other possible concurrent brain activities evoked during the information processing. To deal with this problem, in this paper, we propose a novel hybrid framework, called extendable supervised dictionary learning (E-SDL), to explore diverse and concurrent brain activities under task conditions. A critical difference between E-SDL framework and previous methods is that we systematically extend the basic task paradigm regressor into meaningful regressor groups to account for possible regressor variation during the information processing procedure in the brain. Applications of the proposed framework on five independent and publicly available tfMRI datasets from human connectome project (HCP) simultaneously revealed more meaningful group-wise consistent task-evoked networks and common intrinsic connectivity networks (ICNs). These results demonstrate the advantage of the proposed framework in identifying the diversity of concurrent brain activities in tfMRI datasets.

  13. Automated cell analysis tool for a genome-wide RNAi screen with support vector machine based supervised learning

    Science.gov (United States)

    Remmele, Steffen; Ritzerfeld, Julia; Nickel, Walter; Hesser, Jürgen

    2011-03-01

    RNAi-based high-throughput microscopy screens have become an important tool in biological sciences in order to decrypt mostly unknown biological functions of human genes. However, manual analysis is impossible for such screens since the amount of image data sets can often be in the hundred thousands. Reliable automated tools are thus required to analyse the fluorescence microscopy image data sets usually containing two or more reaction channels. The herein presented image analysis tool is designed to analyse an RNAi screen investigating the intracellular trafficking and targeting of acylated Src kinases. In this specific screen, a data set consists of three reaction channels and the investigated cells can appear in different phenotypes. The main issue of the image processing task is an automatic cell segmentation which has to be robust and accurate for all different phenotypes and a successive phenotype classification. The cell segmentation is done in two steps by segmenting the cell nuclei first and then using a classifier-enhanced region growing on basis of the cell nuclei to segment the cells. The classification of the cells is realized by a support vector machine which has to be trained manually using supervised learning. Furthermore, the tool is brightness invariant allowing different staining quality and it provides a quality control that copes with typical defects during preparation and acquisition. A first version of the tool has already been successfully applied for an RNAi-screen containing three hundred thousand image data sets and the SVM extended version is designed for additional screens.

  14. A spatio-temporal latent atlas for semi-supervised learning of fetal brain segmentations and morphological age estimation.

    Science.gov (United States)

    Dittrich, Eva; Riklin Raviv, Tammy; Kasprian, Gregor; Donner, René; Brugger, Peter C; Prayer, Daniela; Langs, Georg

    2014-01-01

    Prenatal neuroimaging requires reference models that reflect the normal spectrum of fetal brain development, and summarize observations from a representative sample of individuals. Collecting a sufficiently large data set of manually annotated data to construct a comprehensive in vivo atlas of rapidly developing structures is challenging but necessary for large population studies and clinical application. We propose a method for the semi-supervised learning of a spatio-temporal latent atlas of fetal brain development, and corresponding segmentations of emerging cerebral structures, such as the ventricles or cortex. The atlas is based on the annotation of a few examples, and a large number of imaging data without annotation. It models the morphological and developmental variability across the population. Furthermore, it serves as basis for the estimation of a structures' morphological age, and its deviation from the nominal gestational age during the assessment of pathologies. Experimental results covering the gestational period of 20-30 gestational weeks demonstrate segmentation accuracy achievable with minimal annotation, and precision of morphological age estimation. Age estimation results on fetuses suffering from lissencephaly demonstrate that they detect significant differences in the age offset compared to a control group. Copyright © 2013. Published by Elsevier B.V.

  15. Predicting the Ecological Quality Status of Marine Environments from eDNA Metabarcoding Data Using Supervised Machine Learning.

    Science.gov (United States)

    Cordier, Tristan; Esling, Philippe; Lejzerowicz, Franck; Visco, Joana; Ouadahi, Amine; Martins, Catarina; Cedhagen, Tomas; Pawlowski, Jan

    2017-08-15

    Monitoring biodiversity is essential to assess the impacts of increasing anthropogenic activities in marine environments. Traditionally, marine biomonitoring involves the sorting and morphological identification of benthic macro-invertebrates, which is time-consuming and taxonomic-expertise demanding. High-throughput amplicon sequencing of environmental DNA (eDNA metabarcoding) represents a promising alternative for benthic monitoring. However, an important fraction of eDNA sequences remains unassigned or belong to taxa of unknown ecology, which prevent their use for assessing the ecological quality status. Here, we show that supervised machine learning (SML) can be used to build robust predictive models for benthic monitoring, regardless of the taxonomic assignment of eDNA sequences. We tested three SML approaches to assess the environmental impact of marine aquaculture using benthic foraminifera eDNA, a group of unicellular eukaryotes known to be good bioindicators, as features to infer macro-invertebrates based biotic indices. We found similar ecological status as obtained from macro-invertebrates inventories. We argue that SML approaches could overcome and even bypass the cost and time-demanding morpho-taxonomic approaches in future biomonitoring.

  16. Translation and validation of the clinical learning environment, supervision and nurse teacher scale (CLES + T) in Croatian language.

    Science.gov (United States)

    Lovrić, Robert; Piškorjanac, Silvija; Pekić, Vlasta; Vujanić, Jasenka; Ratković, Karolina Kramarić; Luketić, Suzana; Plužarić, Jadranka; Matijašić-Bodalec, Dubravka; Barać, Ivana; Žvanut, Boštjan

    2016-07-01

    Clinical practice is essential to nursing education as it provides experience with patients and work environments that prepare students for future work as nurses. The aim of this study was to translate the "Clinical Learning Environment, Supervision and Nurse Teacher" questionnaire in Croatian language and test its validity and reliability in practice. The study was performed at the Faculty of medicine, Josip Juraj Strossmayer University of Osijek, Croatia in April 2014. The translated questionnaire was submitted to 136 nursing students: 20 males and 116 females. Our results reflected a slightly different factor structure, consisting of four factors. All translated items of the original constructs "Supervisory relationship", "Role of nurse teacher" and "Leadership style of the ward manager" loaded on factor 1. Items of "Pedagogical atmosphere on the ward" are distributed on two factors (3 and 4). The items of "Premises of nursing on the ward" loaded on factor 2. Three items were identified as problematic and iteratively removed from the analysis. The translated version of the aforementioned questionnaire has properties suitable for the evaluation of clinical practice for nursing students within a Croatian context and reflects the specifics of the nursing clinical education in this country.

  17. Application of graph-based semi-supervised learning for development of cyber COP and network intrusion detection

    Science.gov (United States)

    Levchuk, Georgiy; Colonna-Romano, John; Eslami, Mohammed

    2017-05-01

    The United States increasingly relies on cyber-physical systems to conduct military and commercial operations. Attacks on these systems have increased dramatically around the globe. The attackers constantly change their methods, making state-of-the-art commercial and military intrusion detection systems ineffective. In this paper, we present a model to identify functional behavior of network devices from netflow traces. Our model includes two innovations. First, we define novel features for a host IP using detection of application graph patterns in IP's host graph constructed from 5-min aggregated packet flows. Second, we present the first application, to the best of our knowledge, of Graph Semi-Supervised Learning (GSSL) to the space of IP behavior classification. Using a cyber-attack dataset collected from NetFlow packet traces, we show that GSSL trained with only 20% of the data achieves higher attack detection rates than Support Vector Machines (SVM) and Naïve Bayes (NB) classifiers trained with 80% of data points. We also show how to improve detection quality by filtering out web browsing data, and conclude with discussion of future research directions.

  18. A neuron model with trainable activation function (TAF) and its MFNN supervised learning

    Institute of Scientific and Technical Information of China (English)

    吴佑寿; 赵明生

    2001-01-01

    This paper addresses a new kind of neuron model, which has trainable activation function (TAF) in addition to only trainable weights in the conventional M-P model. The final neuron activation function can be derived from a primitive neuron activation function by training. The BP like learning algorithm has been presented for MFNN constructed by neurons of TAF model. Several simulation examples are given to show the network capacity and performance advantages of the new MFNN in comparison with that of conventional sigmoid MFNN.

  19. Anticipatory Driving for a Robot-Car Based on Supervised Learning

    DEFF Research Database (Denmark)

    Markelic, I.; Kulvicius, Tomas; Tamosiunaite, M.

    2009-01-01

    Using look ahead information and plan making improves hu- man driving. We therefore propose that also autonomously driving systems should dispose over such abilities. We adapt a machine learning approach, where the system, a car-like robot, is trained by an experienced driver by correlating visual...... adapt a two-level ap- proach, where the result of the database is combined with an additional reactive controller for robust behavior. Concerning velocity control this paper makes a novel contribution which is the ability of the system to react adequatly to upcoming curves...

  20. Anticipatory Driving for a Robot-Car Based on Supervised Learning

    DEFF Research Database (Denmark)

    Markelic, I.; Kulvicius, Tomas; Tamosiunaite, M.

    2009-01-01

    Using look ahead information and plan making improves hu- man driving. We therefore propose that also autonomously driving systems should dispose over such abilities. We adapt a machine learning approach, where the system, a car-like robot, is trained by an experienced driver by correlating visual...... adapt a two-level ap- proach, where the result of the database is combined with an additional reactive controller for robust behavior. Concerning velocity control this paper makes a novel contribution which is the ability of the system to react adequatly to upcoming curves...