WorldWideScience

Sample records for supervised learning pattern

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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...

  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. Learning Data Driven Representations from Large Collections of Multidimensional Patterns with Minimal Supervision

    Science.gov (United States)

    2008-08-04

    knowledge is provided about the pattern that one is searching for, the task becomes that of aligning the prior with the observed patterns, and choos ...high-throughput, parallel fashion, we used the mass-isolation procedure developed by Eugene et al. [ Eugene et al., 1979] to gather hundreds of thousands...International Conference on Computer Vision, Nice, France, October 2003. [ Eugene et al., 1979] O. Eugene , A. Yund, and J. W. Fristrom. Tissue Culture

  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. 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.

  11. 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

  12. 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).

  13. 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 ...

  14. 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...

  15. 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

  16. 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.

  17. 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.

  18. Intra-individual gait patterns across different time-scales as revealed by means of a supervised learning model using kernel-based discriminant regression.

    Science.gov (United States)

    Horst, Fabian; Eekhoff, Alexander; Newell, Karl M; Schöllhorn, Wolfgang I

    2017-01-01

    Traditionally, gait analysis has been centered on the idea of average behavior and normality. On one hand, clinical diagnoses and therapeutic interventions typically assume that average gait patterns remain constant over time. On the other hand, it is well known that all our movements are accompanied by a certain amount of variability, which does not allow us to make two identical steps. The purpose of this study was to examine changes in the intra-individual gait patterns across different time-scales (i.e., tens-of-mins, tens-of-hours). Nine healthy subjects performed 15 gait trials at a self-selected speed on 6 sessions within one day (duration between two subsequent sessions from 10 to 90 mins). For each trial, time-continuous ground reaction forces and lower body joint angles were measured. A supervised learning model using a kernel-based discriminant regression was applied for classifying sessions within individual gait patterns. Discernable characteristics of intra-individual gait patterns could be distinguished between repeated sessions by classification rates of 67.8 ± 8.8% and 86.3 ± 7.9% for the six-session-classification of ground reaction forces and lower body joint angles, respectively. Furthermore, the one-on-one-classification showed that increasing classification rates go along with increasing time durations between two sessions and indicate that changes of gait patterns appear at different time-scales. Discernable characteristics between repeated sessions indicate continuous intrinsic changes in intra-individual gait patterns and suggest a predominant role of deterministic processes in human motor control and learning. Natural changes of gait patterns without any externally induced injury or intervention may reflect continuous adaptations of the motor system over several time-scales. Accordingly, the modelling of walking by means of average gait patterns that are assumed to be near constant over time needs to be reconsidered in the context of

  19. 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...

  20. 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.

  1. 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...

  2. 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...

  3. 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...

  4. 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...

  5. Coupled Semi-Supervised Learning

    Science.gov (United States)

    2010-05-01

    with the most patterns, ignoring instances that have already been promoted. An analogous procedure is used to extract candidate patterns using recently...promoted, which led to lots of technology-related in- stances being promoted. Also, strings ending in "recipe"were common, like " chocolate chip cookie

  6. 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…

  7. 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

  8. 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.

  9. 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

  10. 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…

  11. 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.

  12. 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.

  13. 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.

  14. 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...

  15. 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.

  16. 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.

  17. 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.

  18. 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...

  19. 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.…

  20. 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.

  1. 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...

  2. 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…

  3. 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.

  4. 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...

  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. 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.

  7. 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...

  8. 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...

  9. 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.

  10. 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...

  11. 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...

  12. 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.

  13. 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

  14. 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

  15. 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.

  16. 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.

  17. Supervised Generative Reconstruction: An Efficient Way To Flexibly Store and Recognize Patterns

    CERN Document Server

    Achler, Tsvi

    2011-01-01

    Matching animal-like flexibility in recognition and the ability to quickly incorporate new information remains difficult. Limits are yet to be adequately addressed in neural models and recognition algorithms. This work proposes a configuration for recognition that maintains the same function of conventional algorithms but avoids combinatorial problems. Feedforward recognition algorithms such as classical artificial neural networks and machine learning algorithms are known to be subject to catastrophic interference and forgetting. Modifying or learning new information (associations between patterns and labels) causes loss of previously learned information. I demonstrate using mathematical analysis how supervised generative models, with feedforward and feedback connections, can emulate feedforward algorithms yet avoid catastrophic interference and forgetting. Learned information in generative models is stored in a more intuitive form that represents the fixed points or solutions of the network and moreover disp...

  18. 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…

  19. 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.

  20. 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.

  1. Pattern recognition & machine learning

    CERN Document Server

    Anzai, Y

    1992-01-01

    This is the first text to provide a unified and self-contained introduction to visual pattern recognition and machine learning. It is useful as a general introduction to artifical intelligence and knowledge engineering, and no previous knowledge of pattern recognition or machine learning is necessary. Basic for various pattern recognition and machine learning methods. Translated from Japanese, the book also features chapter exercises, keywords, and summaries.

  2. 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...

  3. 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).

  4. 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.

  5. 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.

  6. 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...

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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...

  12. 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...

  13. [Patterns in nursing supervision at hospitals in Feira de Santana-Ba].

    Science.gov (United States)

    Leite, M L

    1997-01-01

    This is an exploratory descriptive study about Nurse Supervision Pattern (NSP) performed in Feira de Santana-Bahia-Brazil, in 1994, which aims at describing supervision pattern and identifying interference factors. A questionnaire and descriptive statistics have been used. Supervision pattern proved that the kind of hospital is a factor that interferes on NSP. Based on literature, percentual exam of results and author's professional experience, it asserted that sex, academic graduation, earnings, health politics, planning and technical administrative structure are conditional factors for NSP, as well as lack of professional autonomy, interference of political parties, deficiency of material and human resources, low salaries, unsuitable environment and unsatisfactory interpersonal relationships mentioned by the subjects.

  14. 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...

  15. 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. 

  16. 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.

  17. 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.

  18. 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.

  19. Understanding Exhaustive Pattern Learning

    CERN Document Server

    Shen, Libin

    2011-01-01

    Pattern learning in an important problem in Natural Language Processing (NLP). Some exhaustive pattern learning (EPL) methods (Bod, 1992) were proved to be flawed (Johnson, 2002), while similar algorithms (Och and Ney, 2004) showed great advantages on other tasks, such as machine translation. In this article, we first formalize EPL, and then show that the probability given by an EPL model is constant-factor approximation of the probability given by an ensemble method that integrates exponential number of models obtained with various segmentations of the training data. This work for the first time provides theoretical justification for the widely used EPL algorithm in NLP, which was previously viewed as a flawed heuristic method. Better understanding of EPL may lead to improved pattern learning algorithms in future.

  20. 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.

  1. 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.

  2. 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

  3. 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.

  4. 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...

  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. 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.

  7. 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 ...

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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...

  13. 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.

  14. 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.

  15. 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.

  16. 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…

  17. 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…

  18. 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.

  19. 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

  20. 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...

  1. 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...

  2. 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.

  3. 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.

  4. Hidden Hypnotic Patterns: Implications for Counseling and Supervision.

    Science.gov (United States)

    Gunnison, Hugh; Renick, T. F.

    1985-01-01

    Examines the hypothesis that subtle hypnotic patterns used by Milton H. Erickson are found in the person-centered approach to counseling of Carl Rogers. Points out that counselors and supervisors should be aware of the possible hypnotic elements in simple suggestions. Presents examples of counselor-client and supervisor-trainee dialogue to…

  5. Hidden Hypnotic Patterns: Implications for Counseling and Supervision.

    Science.gov (United States)

    Gunnison, Hugh; Renick, T. F.

    1985-01-01

    Examines the hypothesis that subtle hypnotic patterns used by Milton H. Erickson are found in the person-centered approach to counseling of Carl Rogers. Points out that counselors and supervisors should be aware of the possible hypnotic elements in simple suggestions. Presents examples of counselor-client and supervisor-trainee dialogue to…

  6. 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...

  7. Design Patterns for Complex Learning

    Science.gov (United States)

    Rohse, Shanta; Anderson, Terry

    2006-01-01

    A complex view of learning recognises that learning cannot be pre-determined by teaching, but is as much defined by circumstances and context as pre-defined learning objectives. Learning designs that accept uncertainty help us to envision classrooms and curricula that are open, dynamic and innovative. Architect Christopher Alexander's patterns and…

  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. 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...

  10. 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

  11. 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.

  12. 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...

  13. 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 ...

  14. 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

  15. 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…

  16. Patterns of Personal Learning Environments

    Science.gov (United States)

    Wilson, Scott

    2008-01-01

    The use of design patterns is now well established as an approach within the field of software systems as well as within the field of architecture. An initial effort was made to harness patterns as a tool for elaborating the design of the elements of personal learning environments as part of the University of Bolton's Personal Learning Environment…

  17. 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...

  18. 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.

  19. 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...

  20. 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...

  1. An evaluation of the current patterns and practices of educational supervision in postgraduate medical education in the UK.

    Science.gov (United States)

    Patel, Priyank

    2016-08-01

    Globally, clinical supervision has been widely adopted and studied. But in the UK, another variant of supervision has developed in the form of educational supervision. The quality of supervision remains highly variable and inadequate time, investment and guidance hinders its ability to actually benefit trainees. Therefore, undertaking a detailed study of the patterns and practices in educational supervision to inform developments in supervisory practice would be extremely beneficial. In this mixed methods study, educational supervisors and trainees working within a large London Trust were surveyed online about their experiences of educational supervision. In addition, observations of supervision sessions with a small group of supervisor and trainee pairs followed-up by semi-structured interviews were conducted. The quantitative data were analyzed using statistical software via descriptive statistics. The qualitative data underwent thematic framework analysis. Both the qualitative and quantitative data revealed that whilst most junior doctors and supervisors value the ideal of educational supervision as a process for engaging in mentoring dialogues, it can become a tick box exercise, devaluing its usefulness and purpose. Trainees highlighted the need for more frequent formal meeting along with better preparation by supervisors. Supervisors would appreciate more support from trusts to help them enhance supervision for trainees. The effectiveness of educational supervision can be improved with trainees and supervisors engaging in meaningful preparation and proactive communication before meetings. During these formal meetings, improving the quality of feedback and ensuring that regular mentoring dialogues occurred would be highly valuable.

  2. TU-C-17A-03: An Integrated Contour Evaluation Software Tool Using Supervised Pattern Recognition for Radiotherapy

    Energy Technology Data Exchange (ETDEWEB)

    Chen, H; Tan, J; Kavanaugh, J; Dolly, S; Gay, H; Thorstad, W; Anastasio, M; Altman, M; Mutic, S; Li, H [Washington University School of Medicine, Saint Louis, MO (United States)

    2014-06-15

    Purpose: Radiotherapy (RT) contours delineated either manually or semiautomatically require verification before clinical usage. Manual evaluation is very time consuming. A new integrated software tool using supervised pattern contour recognition was thus developed to facilitate this process. Methods: The contouring tool was developed using an object-oriented programming language C# and application programming interfaces, e.g. visualization toolkit (VTK). The C# language served as the tool design basis. The Accord.Net scientific computing libraries were utilized for the required statistical data processing and pattern recognition, while the VTK was used to build and render 3-D mesh models from critical RT structures in real-time and 360° visualization. Principal component analysis (PCA) was used for system self-updating geometry variations of normal structures based on physician-approved RT contours as a training dataset. The inhouse design of supervised PCA-based contour recognition method was used for automatically evaluating contour normality/abnormality. The function for reporting the contour evaluation results was implemented by using C# and Windows Form Designer. Results: The software input was RT simulation images and RT structures from commercial clinical treatment planning systems. Several abilities were demonstrated: automatic assessment of RT contours, file loading/saving of various modality medical images and RT contours, and generation/visualization of 3-D images and anatomical models. Moreover, it supported the 360° rendering of the RT structures in a multi-slice view, which allows physicians to visually check and edit abnormally contoured structures. Conclusion: This new software integrates the supervised learning framework with image processing and graphical visualization modules for RT contour verification. This tool has great potential for facilitating treatment planning with the assistance of an automatic contour evaluation module in avoiding

  3. 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...

  4. 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...

  5. 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

  6. 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

  7. 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.

  8. Learning Python design patterns

    CERN Document Server

    Zlobin, Gennadiy

    2013-01-01

    This book takes a tutorial-based and user-friendly approach to covering Python design patterns. Its concise presentation means that in a short space of time, you will get a good introduction to various design patterns.If you are an intermediate level Python user, this book is for you. Prior knowledge of Python programming is essential. Some knowledge of UML is also required to understand the UML diagrams which are used to describe some design patterns.

  9. 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.

  10. 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

  11. 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…

  12. 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…

  13. 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

  14. 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

  15. 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...

  16. 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...

  17. 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.

  18. 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...

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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…

  4. 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…

  5. 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

  6. 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

  7. 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…

  8. 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....

  9. 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

  10. 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...

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. Learning Smooth Pattern Transformation Manifolds

    CERN Document Server

    Vural, Elif

    2011-01-01

    Manifold models provide low-dimensional representations that are useful for processing and analyzing data in a transformation-invariant way. In this paper, we study the problem of learning smooth pattern transformation manifolds from image sets that represent observations of geometrically transformed signals. In order to construct a manifold, we build a representative pattern whose transformations accurately fit various input images. We examine two objectives of the manifold building problem, namely, approximation and classification. For the approximation problem, we propose a greedy method that constructs a representative pattern by selecting analytic atoms from a continuous dictionary manifold. We present a DC (Difference-of-Convex) optimization scheme that is applicable to a wide range of transformation and dictionary models, and demonstrate its application to transformation manifolds generated by rotation, translation and anisotropic scaling of a reference pattern. Then, we generalize this approach to a s...

  19. 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.

  20. Video games use patterns and parenteral supervision in a clinical sample of Hispanic adolescents 13-17 years old.

    Science.gov (United States)

    Colón-de Martí, Luz N; Rodríguez-Figueroa, Linnette; Nazario, Lelis L; Gutiérrez, Roberto; González, Alexis

    2012-01-01

    Video games have become a popular entertainment among adolescents. Although some video games are educational, there are others with high content of violence and the potential for other harmful effects. Lack of appropriate supervision of video games use during adolescence, a crucial stage of development, may lead to serious behavioral consequences in some adolescents. There is also concern about time spent playing video games and the subsequent neglect of more developmentally appropriate activities, such as completing academic tasks. Self-administered questionnaires were used to assess video game use patterns and parental supervision among 55 adolescent patients 13-17 years old (mean age 14.4 years; 56.4% males) and their parents. Parental supervision /monitoring of the adolescents video games use was not consistent and gender related differences were found regarding their video game use. Close to one third (32%) of the participants reported video game playing had interfered with their academic performance. Parents who understood video games rating system were more likely to prohibit their use due to rating. These findings underscore the need for clear and consistently enforced rules and monitoring of video games use by adolescents. Parents need to be educated about the relevance of their supervision, video games content and rating system; so they will decrease time playing and exposure to potentially harmful video games. It also supports the relevance of addressing supervision, gender-based parental supervisory styles, and patterns of video games use in the evaluation and treatment of adolescents.

  1. 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.

  2. 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.

  3. "Counselor Education and Supervision" Golden Anniversary Publication Pattern Review: Author and Article Characteristics from 1985 to 2009

    Science.gov (United States)

    Crockett, Stephanie A.; Byrd, Rebekah; Erford, Bradley T.; Hays, Danica G.

    2010-01-01

    Patterns of articles accepted for publication in "Counselor Education and Supervision" from the past 25 years were reviewed in this meta-study. Results were described and statistically analyzed to identify trends over time in author characteristics (sex, institutional classifications, employment setting, domicile) and article characteristics…

  4. 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.

  5. 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.

  6. 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.

  7. Learning Spatiotemporally Encoded Pattern Transformations in Structured Spiking Neural Networks.

    Science.gov (United States)

    Gardner, Brian; Sporea, Ioana; Grüning, André

    2015-12-01

    Information encoding in the nervous system is supported through the precise spike timings of neurons; however, an understanding of the underlying processes by which such representations are formed in the first place remains an open question. Here we examine how multilayered networks of spiking neurons can learn to encode for input patterns using a fully temporal coding scheme. To this end, we introduce a new supervised learning rule, MultilayerSpiker, that can train spiking networks containing hidden layer neurons to perform transformations between spatiotemporal input and output spike patterns. The performance of the proposed learning rule is demonstrated in terms of the number of pattern mappings it can learn, the complexity of network structures it can be used on, and its classification accuracy when using multispike-based encodings. In particular, the learning rule displays robustness against input noise and can generalize well on an example data set. Our approach contributes to both a systematic understanding of how computations might take place in the nervous system and a learning rule that displays strong technical capability.

  8. 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.

  9. 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.

  10. 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%.

  11. Supervised dimensionality reduction and contextual pattern recognition in medical image processing

    NARCIS (Netherlands)

    Loog, Marco

    2004-01-01

    The past few years have witnessed a significant increase in the number of supervised methods employed in diverse image processing tasks. Especially in medical image analysis the use of, for example, supervised shape and appearance modelling has increased considerably and has proven to be successful.

  12. 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.

  13. 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...

  14. 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.

  15. 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...

  16. 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...

  17. 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.

  18. 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...

  19. 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

  20. 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.

  1. 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.

  2. 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

  3. 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.

  4. 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.

  5. 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.

  6. 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...

  7. 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.

  8. 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.

  9. 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

  10. 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.

  11. 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…

  12. 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…

  13. 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

  14. 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

  15. 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.,…

  16. 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…

  17. Entropy-based generation of supervised neural networks for classification of structured patterns.

    Science.gov (United States)

    Tsai, Hsien-Leing; Lee, Shie-Jue

    2004-03-01

    Sperduti and Starita proposed a new type of neural network which consists of generalized recursive neurons for classification of structures. In this paper, we propose an entropy-based approach for constructing such neural networks for classification of acyclic structured patterns. Given a classification problem, the architecture, i.e., the number of hidden layers and the number of neurons in each hidden layer, and all the values of the link weights associated with the corresponding neural network are automatically determined. Experimental results have shown that the networks constructed by our method can have a better performance, with respect to network size, learning speed, or recognition accuracy, than the networks obtained by other methods.

  18. 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.

  19. 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.

  20. Formalization of Learning Patterns Through SNKA

    Directory of Open Access Journals (Sweden)

    Mr Rajesh D

    2016-01-01

    Full Text Available The Learning patterns found among the learners community is steadily progressing towards the digitalized world. The learning patterns arise from acquiring and sharing knowledge. More impact is found on the usage of knowledge sharing tools such as facebook, linkedin, weblogs, etc that are dominating the traditional means of learning. Since the knowledge patterns acquired through web unstructured data is insecure, it leads to poor decision making or decision making without a root cause. These acquired patterns are also shared to others which indirectly affect the trust patterns between users. In this paper, In order to streamline the knowledge acquisition patterns and their sharing means a new framework is defined as Social Networking based Knowledge Acquisition (SNKA to formalize the observed data and the Dynamic Itemset Count (DIC algorithm is tried for predicting the users about the usage of web content before and after the knowledge is acquired. Finally the rough idea in building a tool is also suggested.

  1. 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.

  2. 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.

  3. 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.

  4. Discovering treatment pattern in Traditional Chinese Medicine clinical cases by exploiting supervised topic model and domain knowledge.

    Science.gov (United States)

    Yao, Liang; Zhang, Yin; Wei, Baogang; Wang, Wei; Zhang, Yuejiao; Ren, Xiaolin; Bian, Yali

    2015-12-01

    In Traditional Chinese Medicine (TCM), the prescription is the crystallization of clinical experience of doctors, which is the main way to cure diseases in China for thousands of years. Clinical cases, on the other hand, describe how doctors diagnose and prescribe. In this paper, we propose a framework which mines treatment patterns in TCM clinical cases by exploiting supervised topic model and TCM domain knowledge. The framework can reflect principle rules in TCM and improve function prediction of a new prescription. We evaluate our method on 3090 real world TCM clinical cases. The experiment validates the effectiveness of our method.

  5. 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.

  6. 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.

  7. Two Approaches to Clinical Supervision.

    Science.gov (United States)

    Anderson, Eugene M.

    Criteria are established for a definition of "clinical supervision" and the effectiveness of such supervisory programs in a student teaching context are considered. Two differing genres of clinical supervision are constructed: "supervision by pattern analysis" is contrasted with "supervision by performance objectives." An outline of procedural…

  8. 局部学习半监督多类分类机%Local learning semi-supervised multi-class classifier

    Institute of Scientific and Technical Information of China (English)

    吕佳; 邓乃扬; 田英杰; 邵元海; 杨新民

    2013-01-01

    半监督多类分类问题是机器学习和模式识别领域中的一个研究热点,目前大多数多类分类算法是将问题分解成若干个二类分类问题来求解.提出两种类标号表示方法来避免多个二类分类问题的求解,一种是单位圆类标号表示方法,一种是二进制序列类标号表示方法,并利用局部学习在二类分类问题中的良好学习特性,提出基于局部学习的半监督多类分类机.实验结果证明采用了基于局部学习的半监督多类分类机错分率更小,稳定性更高.%Semi-supervised multi-class classification problem opens research focuses in machine learning and pattern recognition, currently it is decomposed into a set of binary classification problems. Two kinds of class label presentation methods that one was class label presentation method of unit disc and the other was that of binary string were proposed for fear that multiple binary classification problems were solved. Besides, local learning has the good feature in semi-supervised binary classification problem. On the basis of it, local learning semi-supervised multi-class classifier was presented in this paper. The effectiveness of the algorithms was confirmed with experiments on benchmark datasets compared to other related algorithms.

  9. 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.

  10. 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.

  11. 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...

  12. 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.

  13. 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.

  14. 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.

  15. 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…

  16. 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…

  17. 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…

  18. 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…

  19. 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.

  20. 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…

  1. 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 ...

  2. 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.

  3. 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

  4. 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...

  5. 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.

  6. Study on discrimination of Roast green tea ( Camellia sinensis L.) according to geographical origin by FT-NIR spectroscopy and supervised pattern recognition

    Science.gov (United States)

    Chen, Quansheng; Zhao, Jiewen; Lin, Hao

    2009-05-01

    Rapid discrimination of roast green tea according to geographical origin is crucial to quality control. Fourier transform near-infrared (FT-NIR) spectroscopy and supervised pattern recognition was attempted to discriminate Chinese green tea according to geographical origins (i.e. Anhui Province, Henan Province, Jiangsu Province, and Zhejiang Province) in this work. Four supervised pattern recognitions methods were used to construct the discrimination models based on principal component analysis (PCA), respectively. The number of principal components factors (PCs) and model parameters were optimized by cross-validation in the constructing model. The performances of four discrimination models were compared. Experimental results showed that the performance of SVM model is the best among four models. The optimal SVM model was achieved when 4 PCs were used, discrimination rates being all 100% in the training and prediction set. The overall results demonstrated that FT-NIR spectroscopy with supervised pattern recognition could be successfully applied to discriminate green tea according to geographical origins.

  7. 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...

  8. 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).

  9. 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.

  10. 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

  11. 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

  12. Learning Patterns of Teams at the Workplace

    Science.gov (United States)

    Baert, Herman; Govaerts, Natalie

    2012-01-01

    Purpose: With the intention of detecting and describing a series of team learning patterns within a selection of organisations, an extensive exploratory and qualitative research project was conducted in seven phases. The study at hand aims to report on the most recent phase, namely eight case studies in the public employment service of Flanders…

  13. 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

  14. 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

  15. 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

  16. 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

  17. Semi-supervised sparse coding

    KAUST Repository

    Wang, Jim Jing-Yan

    2014-07-06

    Sparse coding approximates the data sample as a sparse linear combination of some basic codewords and uses the sparse codes as new presentations. In this paper, we investigate learning discriminative sparse codes by sparse coding in a semi-supervised manner, where only a few training samples are labeled. By using the manifold structure spanned by the data set of both labeled and unlabeled samples and the constraints provided by the labels of the labeled samples, we learn the variable class labels for all the samples. Furthermore, to improve the discriminative ability of the learned sparse codes, we assume that the class labels could be predicted from the sparse codes directly using a linear classifier. By solving the codebook, sparse codes, class labels and classifier parameters simultaneously in a unified objective function, we develop a semi-supervised sparse coding algorithm. Experiments on two real-world pattern recognition problems demonstrate the advantage of the proposed methods over supervised sparse coding methods on partially labeled data sets.

  18. Deep Learning for Climate Pattern Detection

    Science.gov (United States)

    Prabhat, M.; Liu, Y.; Correa, J.; Racah, E.; Oh, S. Y.; Khosrowshahi, A.; Lavers, D. A.; Wehner, M. F.; Collins, W.

    2015-12-01

    Science motivation In the era of 'Big Data', mining large observational products (satellite measurements, ground-based readings) and massive climate simulations output is key for gaining scientific insights. An important scientific goal is the characterization of extreme weather in current day, and future climate change scenarios. In this work, we consider the problem of finding extreme patterns (such as Tropical Cyclones, Extra-Tropical Cyclones, Atmospheric Rivers) in large climate archives. We present the successful application of Deep Learning, a state-of-the-art machine learning methodology, for finding spatio-temporal patterns. The results from the application of this method can be used for characterizing statistical changes in extreme weather events (both their intensity and frequency) under climate change scenarios. Methods We formulate the problem of finding patterns as a classic image classification task. We prepare labeled data (ground truth is obtained from the application of the TECA tool, a catalog of known events from the literature and hand-labeling). We utilize 8 input variables for Tropical Cyclones and 2 variables for Atmospheric Rivers. We construct a Deep Convolutional Neural Network based on the deep learning library-NEON-developed at Nervana System, in conjunction with the Spearmint package for hyperparameter optimization. Our optimal network consists of 4 layers (2 convolutional layer and 2 fully connected layers). Results We obtain good classification performance for extreme weather patterns: 99% accuracy for Tropical Cyclones, 90.5% (US Atmospheric Rivers) and 89.5% (European Atmospheric Rivers). The attached figure shows sample weather patterns correctly classified by the Deep Learning architecture.

  19. Patterns in clinical students' self-regulated learning behavior: a Q-methodology study.

    Science.gov (United States)

    Berkhout, Joris J; Teunissen, Pim W; Helmich, Esther; van Exel, Job; van der Vleuten, Cees P M; Jaarsma, Debbie A D C

    2017-03-01

    Students feel insufficiently supported in clinical environments to engage in active learning and achieve a high level of self-regulation. As a result clinical learning is highly demanding for students. Because of large differences between students, supervisors may not know how to support them in their learning process. We explored patterns in undergraduate students' self-regulated learning behavior in the clinical environment, to improve tailored supervision, using Q-methodology. Q-methodology uses features of both qualitative and quantitative methods for the systematic investigation of subjective issues by having participants sort statements along a continuum to represent their opinion. We enrolled 74 students between December 2014 and April 2015 and had them characterize their learning behavior by sorting 52 statements about self-regulated learning behavior and explaining their response. The statements used for the sorting were extracted from a previous study. The data was analyzed using by-person factor analysis to identify clusters of individuals with similar sorts of the statements. The resulting factors and qualitative data were used to interpret and describe the patterns that emerged. Five resulting patterns were identified in students' self-regulated learning behavior in the clinical environment, which we labelled: Engaged, Critically opportunistic, Uncertain, Restrained and Effortful. The five patterns varied mostly regarding goals, metacognition, communication, effort, and dependence on external regulation for learning. These discrete patterns in students' self-regulated learning behavior in the clinical environment are part of a complex interaction between student and learning context. The results suggest that developing self-regulated learning behavior might best be supported regarding individual students' needs.

  20. 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.…

  1. 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…

  2. 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.

  3. 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

  4. 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…

  5. 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…

  6. 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.

  7. 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.

  8. Real-Time Classification of Complex Patterns Using Spike-Based Learning in Neuromorphic VLSI.

    Science.gov (United States)

    Mitra, S; Fusi, S; Indiveri, G

    2009-02-01

    Real-time classification of patterns of spike trains is a difficult computational problem that both natural and artificial networks of spiking neurons are confronted with. The solution to this problem not only could contribute to understanding the fundamental mechanisms of computation used in the biological brain, but could also lead to efficient hardware implementations of a wide range of applications ranging from autonomous sensory-motor systems to brain-machine interfaces. Here we demonstrate real-time classification of complex patterns of mean firing rates, using a VLSI network of spiking neurons and dynamic synapses which implement a robust spike-driven plasticity mechanism. The learning rule implemented is a supervised one: a teacher signal provides the output neuron with an extra input spike-train during training, in parallel to the spike-trains that represent the input pattern. The teacher signal simply indicates if the neuron should respond to the input pattern with a high rate or with a low one. The learning mechanism modifies the synaptic weights only as long as the current generated by all the stimulated plastic synapses does not match the output desired by the teacher, as in the perceptron learning rule. We describe the implementation of this learning mechanism and present experimental data that demonstrate how the VLSI neural network can learn to classify patterns of neural activities, also in the case in which they are highly correlated.

  9. Particle Swarm Optimization with Double Learning Patterns.

    Science.gov (United States)

    Shen, Yuanxia; Wei, Linna; Zeng, Chuanhua; Chen, Jian

    2016-01-01

    Particle Swarm Optimization (PSO) is an effective tool in solving optimization problems. However, PSO usually suffers from the premature convergence due to the quick losing of the swarm diversity. In this paper, we first analyze the motion behavior of the swarm based on the probability characteristic of learning parameters. Then a PSO with double learning patterns (PSO-DLP) is developed, which employs the master swarm and the slave swarm with different learning patterns to achieve a trade-off between the convergence speed and the swarm diversity. The particles in the master swarm and the slave swarm are encouraged to explore search for keeping the swarm diversity and to learn from the global best particle for refining a promising solution, respectively. When the evolutionary states of two swarms interact, an interaction mechanism is enabled. This mechanism can help the slave swarm in jumping out of the local optima and improve the convergence precision of the master swarm. The proposed PSO-DLP is evaluated on 20 benchmark functions, including rotated multimodal and complex shifted problems. The simulation results and statistical analysis show that PSO-DLP obtains a promising performance and outperforms eight PSO variants.

  10. 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

  11. 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.

  12. 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.

  13. 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.

  14. 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

  15. 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

  16. 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

  17. 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).

  18. Unraveling flow patterns through nonlinear manifold learning.

    Science.gov (United States)

    Tauro, Flavia; Grimaldi, Salvatore; Porfiri, Maurizio

    2014-01-01

    From climatology to biofluidics, the characterization of complex flows relies on computationally expensive kinematic and kinetic measurements. In addition, such big data are difficult to handle in real time, thereby hampering advancements in the area of flow control and distributed sensing. Here, we propose a novel framework for unsupervised characterization of flow patterns through nonlinear manifold learning. Specifically, we apply the isometric feature mapping (Isomap) to experimental video data of the wake past a circular cylinder from steady to turbulent flows. Without direct velocity measurements, we show that manifold topology is intrinsically related to flow regime and that Isomap global coordinates can unravel salient flow features.

  19. Unraveling flow patterns through nonlinear manifold learning.

    Directory of Open Access Journals (Sweden)

    Flavia Tauro

    Full Text Available From climatology to biofluidics, the characterization of complex flows relies on computationally expensive kinematic and kinetic measurements. In addition, such big data are difficult to handle in real time, thereby hampering advancements in the area of flow control and distributed sensing. Here, we propose a novel framework for unsupervised characterization of flow patterns through nonlinear manifold learning. Specifically, we apply the isometric feature mapping (Isomap to experimental video data of the wake past a circular cylinder from steady to turbulent flows. Without direct velocity measurements, we show that manifold topology is intrinsically related to flow regime and that Isomap global coordinates can unravel salient flow features.

  20. 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.

  1. 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,…

  2. Multisubject Learning for Common Spatial Patterns in Motor-Imagery BCI

    Directory of Open Access Journals (Sweden)

    Dieter Devlaminck

    2011-01-01

    Full Text Available Motor-imagery-based brain-computer interfaces (BCIs commonly use the common spatial pattern filter (CSP as preprocessing step before feature extraction and classification. The CSP method is a supervised algorithm and therefore needs subject-specific training data for calibration, which is very time consuming to collect. In order to reduce the amount of calibration data that is needed for a new subject, one can apply multitask (from now on called multisubject machine learning techniques to the preprocessing phase. Here, the goal of multisubject learning is to learn a spatial filter for a new subject based on its own data and that of other subjects. This paper outlines the details of the multitask CSP algorithm and shows results on two data sets. In certain subjects a clear improvement can be seen, especially when the number of training trials is relatively low.

  3. 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.

  4. Optimizing a Workplace Learning Pattern: A Case Study from Aviation

    Science.gov (United States)

    Mavin, Timothy John; Roth, Wolff-Michael

    2015-01-01

    Purpose: This study aims to contribute to current research on team learning patterns. It specifically addresses some negative perceptions of the job performance learning pattern. Design/methodology/approach: Over a period of three years, qualitative and quantitative data were gathered on pilot learning in the workplace. The instructional modes…

  5. Optimizing a Workplace Learning Pattern: A Case Study from Aviation

    Science.gov (United States)

    Mavin, Timothy John; Roth, Wolff-Michael

    2015-01-01

    Purpose: This study aims to contribute to current research on team learning patterns. It specifically addresses some negative perceptions of the job performance learning pattern. Design/methodology/approach: Over a period of three years, qualitative and quantitative data were gathered on pilot learning in the workplace. The instructional modes…

  6. 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.

  7. 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…

  8. 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…

  9. 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).

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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

  16. 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

  17. 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

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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...

  4. Electroencephalographic Coherence and Learning: Distinct Patterns of Change during Word Learning and Figure Learning Tasks

    Science.gov (United States)

    Collins, Peter; Hogan, Michael; Kilmartin, Liam; Keane, Michael; Kaiser, Jochen; Fischer, Kurt

    2010-01-01

    One likely mechanism in learning new skills is change in synchronous connections between distributed neural networks, which can be measured by coherence analysis of electroencephalographic patterns. This study examined coherence changes during the learning of two tasks, a word association task and a figure association task. Although learning…

  5. 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

  6. 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.

  7. Roots run deep: Investigating psychological mechanisms between history of family aggression and abusive supervision.

    Science.gov (United States)

    Garcia, Patrick Raymund James M; Restubog, Simon Lloyd D; Kiewitz, Christian; Scott, Kristin L; Tang, Robert L

    2014-09-01

    In this article, we examine the relationships between supervisor-level factors and abusive supervision. Drawing from social learning theory (Bandura, 1973), we argue that supervisors' history of family aggression indirectly impacts abusive supervision via both hostile cognitions and hostile affect, with angry rumination functioning as a first-stage moderator. Using multisource data, we tested the proposed relationships in a series of 4 studies, each providing evidence of constructive replication. In Study 1, we found positive relationships between supervisors' history of family aggression, hostile affect, explicit hostile cognitions, and abusive supervision. We obtained the same pattern of results in Studies 2, 3, and 4 using an implicit measure of hostile cognitions and controlling for previously established antecedents of abusive supervision. Angry rumination moderated the indirect relationship between supervisors' history of family aggression and abusive supervision via hostile affect only. Overall, the results highlight the important role of supervisor-level factors in the abusive supervision dynamics.

  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. 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...

  10. 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…

  11. Research Issues in Evaluating Learning Pattern Development in Higher Education

    Science.gov (United States)

    Richardson, John T. E.

    2013-01-01

    This article concludes the special issue of "Studies in Educational Evaluation" concerned with "Evaluating learning pattern development in higher education" by discussing research issues that have emerged from the previous contributions. The article considers in turn: stability versus variability in learning patterns; old versus new analytic…

  12. 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.

  13. Learning builds on learning: infants' use of native language sound patterns to learn words.

    Science.gov (United States)

    Graf Estes, Katharine

    2014-10-01

    The current research investigated how infants apply prior knowledge of environmental regularities to support new learning. The experiments tested whether infants could exploit experience with native language (English) phonotactic patterns to facilitate associating sounds with meanings during word learning. Infants (14-month-olds) heard fluent speech that contained cues for detecting target words; the target words were embedded in sequences that occur across word boundaries. A separate group heard the target words embedded without word boundary cues. Infants then participated in an object label learning task. With the opportunity to use native language patterns to segment the target words, infants subsequently learned the labels. Without this experience, infants failed. Novice word learners can take advantage of early learning about sounds to scaffold lexical development. Copyright © 2014 Elsevier Inc. All rights reserved.

  14. 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...

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. Two ways to save a newly learned motor pattern.

    Science.gov (United States)

    Roemmich, Ryan T; Bastian, Amy J

    2015-06-01

    Savings, or faster relearning after initial learning, demonstrates humans' remarkable ability to retain learned movements amid changing environments. This is important within the context of locomotion, as the ability of the nervous system to "remember" how to walk in specific environments enables us to navigate changing terrains and progressively improve gait patterns with rehabilitation. Here, we used a split-belt treadmill to study precisely how people save newly learned walking patterns. In Experiment 1, we investigated savings by systematically varying the learning and unlearning environments. Savings was predominantly influenced by 1) previous exposure to similar abrupt changes in the environment and 2) the amount of exposure to the new environment. Relearning was fastest when these two factors coincided, and we did not observe savings after the environment was introduced gradually during initial learning. In Experiment 2, we then studied whether people store explicit information about different walking environments that mirrors savings of a new walking pattern. Like savings, we found that previous exposure to abrupt changes in the environment also drove the ability to recall a previously experienced walking environment accurately. Crucially, the information recalled was extrinsic information about the learning environment (i.e., treadmill speeds) and not intrinsic information about the walking pattern itself. We conclude that simply learning a new walking pattern is not enough for long-term savings; rather, savings of a learned walking pattern involves recall of the environment or extended training at the learned state.

  20. 基于流形正则化半监督学习的污水处理操作工况识别方法%Identification of wastewater operational conditions based on manifold regularization semi-supervised learning

    Institute of Scientific and Technical Information of China (English)

    赵立杰; 王海龙; 陈斌

    2016-01-01

    The wastewater treatment process is vulnerable to the impact of external shocks to cause sludge floating, aging, poisoning, expansion and other failure conditions, resulting in effluent deterioration and high energy consumption. It is urgent to quickly and accurately identify the operating conditions of wastewater treatment process. In the existing supervised learning methods all the data are labeled which are time consuming and expensive. A multitude of unlabeled data to collect easily and cheaply have rich and useful information about the operating condition. To overcome the disadvantage of supervised learning algorithms that they cannot make use of unlabeled data, a semi-supervised extreme learning machine algorithm based on manifold regularization is adopted to monitor the operation states of biochemical wastewater treatment process. The graph Laplacian matrix is constructed from both the labeled patterns and the unlabeled patterns. Extreme learning machine algorithm is adopted to handle the semi-supervised learning task under the framework of the manifold regularization. It constructs the hidden layer using random feature mapping and solves the weights between the hidden layer and the output layer, which exhibit the computational efficiency and generalization performance of the random neural network. The results of simulation experiments show that the fault identification method based on semi supervised learning machine has superiority to the basic extreme learning machine in improving the accuracy and reliability.%污水处理过程容易受外界冲激扰动影响,引发污泥上浮、老化、中毒、膨胀等故障工况,导致出水水质质量差,能源消耗高等问题,如何快速准确识别污水操作工况故障至关重要。针对污水工况识别过程中现有监督学习方法未利用大量未标记数据蕴含的丰富操作工况信息,采用基于流形正则化极限学习机的半监督学习方法,监视生化污水处

  1. 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...

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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...

  9. 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...

  10. Novel Approaches for Diagnosing Melanoma Skin Lesions Through Supervised and Deep Learning Algorithms.

    Science.gov (United States)

    Premaladha, J; Ravichandran, K S

    2016-04-01

    Dermoscopy is a technique used to capture the images of skin, and these images are useful to analyze the different types of skin diseases. Malignant melanoma is a kind of skin cancer whose severity even leads to death. Earlier detection of melanoma prevents death and the clinicians can treat the patients to increase the chances of survival. Only few machine learning algorithms are developed to detect the melanoma using its features. This paper proposes a Computer Aided Diagnosis (CAD) system which equips efficient algorithms to classify and predict the melanoma. Enhancement of the images are done using Contrast Limited Adaptive Histogram Equalization technique (CLAHE) and median filter. A new segmentation algorithm called Normalized Otsu's Segmentation (NOS) is implemented to segment the affected skin lesion from the normal skin, which overcomes the problem of variable illumination. Fifteen features are derived and extracted from the segmented images are fed into the proposed classification techniques like Deep Learning based Neural Networks and Hybrid Adaboost-Support Vector Machine (SVM) algorithms. The proposed system is tested and validated with nearly 992 images (malignant & benign lesions) and it provides a high classification accuracy of 93 %. The proposed CAD system can assist the dermatologists to confirm the decision of the diagnosis and to avoid excisional biopsies.

  11. 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.

  12. Photometric classification of type Ia supernovae in the SuperNova Legacy Survey with supervised learning

    Science.gov (United States)

    Möller, A.; Ruhlmann-Kleider, V.; Leloup, C.; Neveu, J.; Palanque-Delabrouille, N.; Rich, J.; Carlberg, R.; Lidman, C.; Pritchet, C.

    2016-12-01

    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 (0.2 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 samples. Using the Area Under the Curve (AUC) metric, where perfect classification is given by 1, we find that our best-performing classifier (Extreme Gradient Boosting Decision Tree) has an AUC of 0.98.We show that it is possible to obtain a large photometrically selected type Ia SN sample with an estimated contamination of less than 5%. When applied to data from the first three years of SNLS, we obtain 529 events. We investigate the differences between classifying simulated SNe, and real SN survey data. In particular, we find that applying a thorough set of selection cuts to the SN sample is essential for good classification. This work demonstrates for the first time the feasibility of machine learning classification in a high-z SN survey with application to real SN data.

  13. Supervised machine learning on a network scale: application to seismic event classification and detection

    Science.gov (United States)

    Reynen, Andrew; Audet, Pascal

    2017-09-01

    A new method using a machine learning technique is applied to event classification and detection at seismic networks. This method is applicable to a variety of network sizes and settings. The algorithm makes use of a small catalogue of known observations across the entire network. Two attributes, the polarization and frequency content, are used as input to regression. These attributes are extracted at predicted arrival times for P and S waves using only an approximate velocity model, as attributes are calculated over large time spans. This method of waveform characterization is shown to be able to distinguish between blasts and earthquakes with 99 per cent accuracy using a network of 13 stations located in Southern California. The combination of machine learning with generalized waveform features is further applied to event detection in Oklahoma, United States. The event detection algorithm makes use of a pair of unique seismic phases to locate events, with a precision directly related to the sampling rate of the generalized waveform features. Over a week of data from 30 stations in Oklahoma, United States are used to automatically detect 25 times more events than the catalogue of the local geological survey, with a false detection rate of less than 2 per cent. This method provides a highly confident way of detecting and locating events. Furthermore, a large number of seismic events can be automatically detected with low false alarm, allowing for a larger automatic event catalogue with a high degree of trust.

  14. Impact of corpus domain for sentiment classification: An evaluation study using supervised machine learning techniques

    Science.gov (United States)

    Karsi, Redouane; Zaim, Mounia; El Alami, Jamila

    2017-07-01

    Thanks to the development of the internet, a large community now has the possibility to communicate and express its opinions and preferences through multiple media such as blogs, forums, social networks and e-commerce sites. Today, it becomes clearer that opinions published on the web are a very valuable source for decision-making, so a rapidly growing field of research called “sentiment analysis” is born to address the problem of automatically determining the polarity (Positive, negative, neutral,…) of textual opinions. People expressing themselves in a particular domain often use specific domain language expressions, thus, building a classifier, which performs well in different domains is a challenging problem. The purpose of this paper is to evaluate the impact of domain for sentiment classification when using machine learning techniques. In our study three popular machine learning techniques: Support Vector Machines (SVM), Naive Bayes and K nearest neighbors(KNN) were applied on datasets collected from different domains. Experimental results show that Support Vector Machines outperforms other classifiers in all domains, since it achieved at least 74.75% accuracy with a standard deviation of 4,08.

  15. Doctoral Student Learning Patterns: Learning about Active Knowledge Creation or Passive Production

    Science.gov (United States)

    Vekkaila, Jenna; Pyhältö, Kirsi

    2016-01-01

    Doctoral studies are about learning to create new knowledge and to become a researcher. Yet surprisingly little is known about the individual learning patterns of doctoral students. The study aims to explore learning patterns among natural science doctoral students. The participants included 19 doctoral students from a top-level natural science…

  16. Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection

    Energy Technology Data Exchange (ETDEWEB)

    Park, Sang Hyun [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Gao, Yaozong, E-mail: yzgao@cs.unc.edu [Department of Computer Science, Department of Radiology, and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Shi, Yinghuan, E-mail: syh@nju.edu.cn [State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023 (China); Shen, Dinggang, E-mail: dgshen@med.unc.edu [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul 136-713 (Korea, Republic of)

    2014-11-01

    Purpose: Accurate prostate segmentation is necessary for maximizing the effectiveness of radiation therapy of prostate cancer. However, manual segmentation from 3D CT images is very time-consuming and often causes large intra- and interobserver variations across clinicians. Many segmentation methods have been proposed to automate this labor-intensive process, but tedious manual editing is still required due to the limited performance. In this paper, the authors propose a new interactive segmentation method that can (1) flexibly generate the editing result with a few scribbles or dots provided by a clinician, (2) fast deliver intermediate results to the clinician, and (3) sequentially correct the segmentations from any type of automatic or interactive segmentation methods. Methods: The authors formulate the editing problem as a semisupervised learning problem which can utilize a priori knowledge of training data and also the valuable information from user interactions. Specifically, from a region of interest near the given user interactions, the appropriate training labels, which are well matched with the user interactions, can be locally searched from a training set. With voting from the selected training labels, both confident prostate and background voxels, as well as unconfident voxels can be estimated. To reflect informative relationship between voxels, location-adaptive features are selected from the confident voxels by using regression forest and Fisher separation criterion. Then, the manifold configuration computed in the derived feature space is enforced into the semisupervised learning algorithm. The labels of unconfident voxels are then predicted by regularizing semisupervised learning algorithm. Results: The proposed interactive segmentation method was applied to correct automatic segmentation results of 30 challenging CT images. The correction was conducted three times with different user interactions performed at different time periods, in order to

  17. The chronotron: a neuron that learns to fire temporally precise spike patterns.

    Directory of Open Access Journals (Sweden)

    Răzvan V Florian

    Full Text Available In many cases, neurons process information carried by the precise timings of spikes. Here we show how neurons can learn to generate specific temporally precise output spikes in response to input patterns of spikes having precise timings, thus processing and memorizing information that is entirely temporally coded, both as input and as output. We introduce two new supervised learning rules for spiking neurons with temporal coding of information (chronotrons, one that provides high memory capacity (E-learning, and one that has a higher biological plausibility (I-learning. With I-learning, the neuron learns to fire the target spike trains through synaptic changes that are proportional to the synaptic currents at the timings of real and target output spikes. We study these learning rules in computer simulations where we train integrate-and-fire neurons. Both learning rules allow neurons to fire at the desired timings, with sub-millisecond precision. We show how chronotrons can learn to classify their inputs, by firing identical, temporally precise spike trains for different inputs belonging to the same class. When the input is noisy, the classification also leads to noise reduction. We compute lower bounds for the memory capacity of chronotrons and explore the influence of various parameters on chronotrons' performance. The chronotrons can model neurons that encode information in the time of the first spike relative to the onset of salient stimuli or neurons in oscillatory networks that encode information in the phases of spikes relative to the background oscillation. Our results show that firing one spike per cycle optimizes memory capacity in neurons encoding information in the phase of firing relative to a background rhythm.

  18. 高校学生管理工作的辩证思考%Dialectical thought about the supervision of students in institutions of higher learning

    Institute of Scientific and Technical Information of China (English)

    李宜祥; 邢大伟; 沈广元

    2001-01-01

    针对强化素质教育问题,研究了高校学生管理工作,论述了学生管理与自身建设、行为管理与思想疏导、理性说服与人情感化、群体教育与个体工作的辩证关系,提出加强自我修养、强化思想疏导、加大感情投入、做好个体工作,是新形势下做好学生管理工作的重要手段.%In accordance with the development of quality education thispaper deals with the supervision of students in institutions of higher learning and discusses the dialectical relations between the supervision of students and colleges and universities′ self reconstruction,the supervision of students′ behaviour and ideological mediation,rational persuasion and human feeling change by persuasion ,groups education and individual education,expounds important measures to improve the supervision of students such as raise teachers′ self quality,strengthening thought mediation,giving more affection to the work and neglecting no student.

  19. Application of supervised machine learning algorithms for the classification of regulatory RNA riboswitches.

    Science.gov (United States)

    Singh, Swadha; Singh, Raghvendra

    2016-04-03

    Riboswitches, the small structured RNA elements, were discovered about a decade ago. It has been the subject of intense interest to identify riboswitches, understand their mechanisms of action and use them in genetic engineering. The accumulation of genome and transcriptome sequence data and comparative genomics provide unprecedented opportunities to identify riboswitches in the genome. In the present study, we have evaluated the following six machine learning algorithms for their efficiency to classify riboswitches: J48, BayesNet, Naïve Bayes, Multilayer Perceptron, sequential minimal optimization, hidden Markov model (HMM). For determining effective classifier, the algorithms were compared on the statistical measures of specificity, sensitivity, accuracy, F-measure and receiver operating characteristic (ROC) plot analysis. The classifier Multilayer Perceptron achieved the best performance, with the highest specificity, sensitivity, F-score and accuracy, and with the largest area under the ROC curve, whereas HMM was the poorest performer. At present, the available tools for the prediction and classification of riboswitches are based on covariance model, support vector machine and HMM. The present study determines Multilayer Perceptron as a better classifier for the genome-wide riboswitch searches.

  20. How to guide group to create learning-type project supervision department%如何带领团队创建学习型项目部

    Institute of Scientific and Technical Information of China (English)

    高春玉

    2011-01-01

    阐述了在工作中学习的重要性,介绍了如何创建学习型项目部的方法,并从三个方面加以分析,以建立和完善学习体制,有效地提高监理人员自身素质。%This paper expounds the significance of study in work, introduces methods of how to creating learning-type project supervision department, and makes an analysis from three aspects, with a view to establish and improve learning system and to effectively improve supervisors' quality.

  1. A survey of supervised machine learning models for mobile-phone based pathogen identification and classification

    Science.gov (United States)

    Ceylan Koydemir, Hatice; Feng, Steve; Liang, Kyle; Nadkarni, Rohan; Tseng, Derek; Benien, Parul; Ozcan, Aydogan

    2017-03-01

    Giardia lamblia causes a disease known as giardiasis, which results in diarrhea, abdominal cramps, and bloating. Although conventional pathogen detection methods used in water analysis laboratories offer high sensitivity and specificity, they are time consuming, and need experts to operate bulky equipment and analyze the samples. Here we present a field-portable and cost-effective smartphone-based waterborne pathogen detection platform that can automatically classify Giardia cysts using machine learning. Our platform enables the detection and quantification of Giardia cysts in one hour, including sample collection, labeling, filtration, and automated counting steps. We evaluated the performance of three prototypes using Giardia-spiked water samples from different sources (e.g., reagent-grade, tap, non-potable, and pond water samples). We populated a training database with >30,000 cysts and estimated our detection sensitivity and specificity using 20 different classifier models, including decision trees, nearest neighbor classifiers, support vector machines (SVMs), and ensemble classifiers, and compared their speed of training and classification, as well as predicted accuracies. Among them, cubic SVM, medium Gaussian SVM, and bagged-trees were the most promising classifier types with accuracies of 94.1%, 94.2%, and 95%, respectively; we selected the latter as our preferred classifier for the detection and enumeration of Giardia cysts that are imaged using our mobile-phone fluorescence microscope. Without the need for any experts or microbiologists, this field-portable pathogen detection platform can present a useful tool for water quality monitoring in resource-limited-settings.

  2. Supervised Learning of Two-Layer Perceptron under the Existence of External Noise — Learning Curve of Boolean Functions of Two Variables in Tree-Like Architecture —

    Science.gov (United States)

    Uezu, Tatsuya; Kiyokawa, Shuji

    2016-06-01

    We investigate the supervised batch learning of Boolean functions expressed by a two-layer perceptron with a tree-like structure. We adopt continuous weights (spherical model) and the Gibbs algorithm. We study the Parity and And machines and two types of noise, input and output noise, together with the noiseless case. We assume that only the teacher suffers from noise. By using the replica method, we derive the saddle point equations for order parameters under the replica symmetric (RS) ansatz. We study the critical value αC of the loading rate α above which the learning phase exists for cases with and without noise. We find that αC is nonzero for the Parity machine, while it is zero for the And machine. We derive the exponents barβ of order parameters expressed as (α - α C)bar{β} when α is near to αC. Furthermore, in the Parity machine, when noise exists, we find a spin glass solution, in which the overlap between the teacher and student vectors is zero but that between student vectors is nonzero. We perform Markov chain Monte Carlo simulations by simulated annealing and also by exchange Monte Carlo simulations in both machines. In the Parity machine, we study the de Almeida-Thouless stability, and by comparing theoretical and numerical results, we find that there exist parameter regions where the RS solution is unstable, and that the spin glass solution is metastable or unstable. We also study asymptotic learning behavior for large α and derive the exponents hat{β } of order parameters expressed as α - hat{β } when α is large in both machines. By simulated annealing simulations, we confirm these results and conclude that learning takes place for the input noise case with any noise amplitude and for the output noise case when the probability that the teacher's output is reversed is less than one-half.

  3. Patterns for Designing Learning Management Systems

    NARCIS (Netherlands)

    Avgeriou, Paris; Retalis, Symeon; Papasalouros, Andreas

    2003-01-01

    Learning Management Systems are sophisticated web-based applications that are being engineered today in increasing numbers by numerous institutions and companies that want to get involved in e-learning either for providing services to third parties, or for educating and training their own people. Ev

  4. Towards a Pattern Language for Networked Learning

    NARCIS (Netherlands)

    Goodyear, Peter; Avgeriou, Paris; Baggetun, Rune; Bartoluzzi, Sonia; Retalis, Simeon; Ronteltap, Frans; Rusman, Ellen

    2004-01-01

    The work of designing a useful, convivial networked learning environment is complex and demanding. People new to designing for networked learning face a number of major challenges when they try to draw on the experience of others – whether that experience is shared informally, in the everyday langua

  5. Human-interpretable feature pattern classification system using learning classifier systems.

    Science.gov (United States)

    Ebadi, Toktam; Kukenys, Ignas; Browne, Will N; Zhang, Mengjie

    2014-01-01

    Image pattern classification is a challenging task due to the large search space of pixel data. Supervised and subsymbolic approaches have proven accurate in learning a problem's classes. However, in the complex image recognition domain, there is a need for investigation of learning techniques that allow humans to interpret the learned rules in order to gain an insight about the problem. Learning classifier systems (LCSs) are a machine learning technique that have been minimally explored for image classification. This work has developed the feature pattern classification system (FPCS) framework by adopting Haar-like features from the image recognition domain for feature extraction. The FPCS integrates Haar-like features with XCS, which is an accuracy-based LCS. A major contribution of this work is that the developed framework is capable of producing human-interpretable rules. The FPCS system achieved 91 [Formula: see text] 1% accuracy on the unseen test set of the MNIST dataset. In addition, the FPCS is capable of autonomously adjusting the rotation angle in unaligned images. This rotation adjustment raised the accuracy of FPCS to 95%. Although the performance is competitive with equivalent approaches, this was not as accurate as subsymbolic approaches on this dataset. However, the benefit of the interpretability of rules produced by FPCS enabled us to identify the distribution of the learned angles-a normal distribution around [Formula: see text]-which would have been very difficult in subsymbolic approaches. The analyzable nature of FPCS is anticipated to be beneficial in domains such as speed sign recognition, where underlying reasoning and confidence of recognition needs to be human interpretable.

  6. Spatio-Temporal Pattern of Tuberculosis in the Regions Super-vised by Shiraz University of Medical Sciences 2006-2012

    Directory of Open Access Journals (Sweden)

    Hamidreza TABATABAEE

    2015-10-01

    Full Text Available Background: The present study aimed to identify the spatial distribution of tuberculosis and determine the TB control program parameters in the regions supervised by Shiraz University of Medical Sciences in 2006-2012.Methods: The present ecological study was performed on 1797 TB patients in Shiraz University in 2006-2012 which were recorded by health centers using TB Register software. The study data were collected through over-counting and analyzed using the SPSS statistical software (ver. 19. Besides, the maps were drawn by ArcGIS, version 10.Results: The incidence rate of TB was 4.8 in 100,000 at the end of 2012. Success in treatment was adequate only in 2012 (89.7%. However, recovery of pulmonary TB was not adequate in any of the study years. In our study, the majority of the patients belonged to the 25-34 years age group that constitutes the active faction of the society.  Moreover, the maps provided by GIS showed a high incidence rate of extra pulmonary TB in Firozabad Township during 7 years (2.7 in 100000 populations.Conclusion: Incidence of TB in the regions supervised by Shiraz University of Medical Sciences follows a specific pattern, which requires exclusive studies for further evaluation of the incidence determinatives in various environmental and social conditions. 

  7. Patterns Characterising the Teaching and Learning Practices of ...

    African Journals Online (AJOL)

    Patterns Characterising the Teaching and Learning Practices of Religious Education ... This was done with the intention to find different methods and approaches that can ... with learning difficulties in sciences, mathematics and commercial subjects. ... In this century Religious Studies should be committed to the open, plural, ...

  8. Listeners learn phonotactic patterns conditioned on suprasegmental cues.

    Science.gov (United States)

    White, Katherine S; Chambers, Kyle E; Miller, Zachary; Jethava, Vibhuti

    2017-12-01

    Language learners are sensitive to phonotactic patterns from an early age, and can acquire both simple and 2nd-order positional restrictions contingent on segment identity (e.g., /f/ is an onset with /æ/but a coda with /ɪ/). The present study explored the learning of phonototactic patterns conditioned on a suprasegmental cue: lexical stress. Adults first heard non-words in which trochaic and iambic items had different consonant restrictions. In Experiment 1, participants trained with phonotactic patterns involving natural classes of consonants later falsely recognized novel items that were consistent with the training patterns (legal items), demonstrating that they had learned the stress-conditioned phonotactic patterns. However, this was only true for iambic items. In Experiment 2, participants completed a forced-choice test between novel legal and novel illegal items and were again successful only for the iambic items. Experiment 3 demonstrated learning for trochaic items when they were presented alone. Finally, in Experiment 4, in which the training phase was lengthened, participants successfully learned both sets of phonotactic patterns. These experiments provide evidence that learners consider more global phonological properties in the computation of phonotactic patterns, and that learners can acquire multiple sets of patterns simultaneously, even contradictory ones.

  9. Eliciting Design Patterns for E-Learning Systems

    Science.gov (United States)

    Retalis, Symeon; Georgiakakis, Petros; Dimitriadis, Yannis

    2006-01-01

    Design pattern creation, especially in the e-learning domain, is a highly complex process that has not been sufficiently studied and formalized. In this paper, we propose a systematic pattern development cycle, whose most important aspects focus on reverse engineering of existing systems in order to elicit features that are cross-validated through…

  10. Semi-supervised Phonetic Category Learning: Does Word-level Information Enhance the Efficacy of Distributional Learning?

    Directory of Open Access Journals (Sweden)

    Till Poppels

    2014-08-01

    Full Text Available To test whether word-level information facilitates the learning of phonetic categories, 40 adult native English speakers were exposed to a bimodal distribution of vowels embedded in non-words. Half of the subjects received phonetic categories aligned with lexical categories, while the other half received no such cue. It was hypothesized that the subjects exposed to lexically-informative training stimuli that were aligned with the target categories would outperform the control subjects on a perceptual categorization task after training. While the results revealed no such group differences, the data indicated that many subjects used the relevant dimension for categorization before having received any training. Implications regarding experimental design and suggestions for future research based on the results are discussed.

  11. Implementability of Instructional Supervision as a Contemporary Educational Supervision Model in Turkish Education System

    OpenAIRE

    2012-01-01

    In this study, implementability of instructional supervision as one of contemporary educational supervision models in Turkish Education System was evaluated. Instructional supervision which aims to develop instructional processes and increase the quality of student learning based on observation of classroom activities requires collaboration among supervisors and teachers. In this literature review, significant problems have been detected due to structural organization, structural and control-...

  12. The art and learning patterns of knowing in nursing

    Directory of Open Access Journals (Sweden)

    Cristina Lavareda Baixinho

    2014-12-01

    Full Text Available Objective To identify the perception of the students about the use of art as a pedagogical strategy in learning the patterns of knowing in nursing; to identify the dimensions of each pattern valued in the analysis of pieces of art. Method Descriptive mixed study. Data collection used a questionnaire applied to 31 nursing students. Results In the analysis of the students’ discourse, it was explicit that empirical knowledge includes scientific knowledge, tradition and nature of care. The aesthetic knowledge implies expressiveness, subjectivity and sensitivity. Self-knowledge, experience, reflective attitude and relationships with others are the subcategories of personal knowledge and the moral and ethics support ethical knowledge. Conclusion It is possible to learn patterns of knowledge through art, especially the aesthetic, ethical and personal. It is necessary to investigate further pedagogical strategies that contribute to the learning patterns of nursing knowledge.

  13. Interaction Patterns and Facilitation of Peer Learning.

    Science.gov (United States)

    Shaw, Marvin E.; And Others

    1979-01-01

    Data show that giving information to members of a group is more important in determining the perception by others that the person is facilitating group performance. Asking for information and opinions is more important in actual facilitation of group learning. Social-emotional support becomes important after initial phases of group interaction.…

  14. 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.

  15. Quality-Related Monitoring and Grading of Granulated Products by Weibull-Distribution Modeling of Visual Images with Semi-Supervised Learning

    Directory of Open Access Journals (Sweden)

    Jinping Liu

    2016-06-01

    Full Text Available The topic of online product quality inspection (OPQI with smart visual sensors is attracting increasing interest in both the academic and industrial communities on account of the natural connection between the visual appearance of products with their underlying qualities. Visual images captured from granulated products (GPs, e.g., cereal products, fabric textiles, are comprised of a large number of independent particles or stochastically stacking locally homogeneous fragments, whose analysis and understanding remains challenging. A method of image statistical modeling-based OPQI for GP quality grading and monitoring by a Weibull distribution(WD model with a semi-supervised learning classifier is presented. WD-model parameters (WD-MPs of GP images’ spatial structures, obtained with omnidirectional Gaussian derivative filtering (OGDF, which were demonstrated theoretically to obey a specific WD model of integral form, were extracted as the visual features. Then, a co-training-style semi-supervised classifier algorithm, named COSC-Boosting, was exploited for semi-supervised GP quality grading, by integrating two independent classifiers with complementary nature in the face of scarce labeled samples. Effectiveness of the proposed OPQI method was verified and compared in the field of automated rice quality grading with commonly-used methods and showed superior performance, which lays a foundation for the quality control of GP on assembly lines.

  16. Quality-Related Monitoring and Grading of Granulated Products by Weibull-Distribution Modeling of Visual Images with Semi-Supervised Learning

    Science.gov (United States)

    Liu, Jinping; Tang, Zhaohui; Xu, Pengfei; Liu, Wenzhong; Zhang, Jin; Zhu, Jianyong

    2016-01-01

    The topic of online product quality inspection (OPQI) with smart visual sensors is attracting increasing interest in both the academic and industrial communities on account of the natural connection between the visual appearance of products with their underlying qualities. Visual images captured from granulated products (GPs), e.g., cereal products, fabric textiles, are comprised of a large number of independent particles or stochastically stacking locally homogeneous fragments, whose analysis and understanding remains challenging. A method of image statistical modeling-based OPQI for GP quality grading and monitoring by a Weibull distribution(WD) model with a semi-supervised learning classifier is presented. WD-model parameters (WD-MPs) of GP images’ spatial structures, obtained with omnidirectional Gaussian derivative filtering (OGDF), which were demonstrated theoretically to obey a specific WD model of integral form, were extracted as the visual features. Then, a co-training-style semi-supervised classifier algorithm, named COSC-Boosting, was exploited for semi-supervised GP quality grading, by integrating two independent classifiers with complementary nature in the face of scarce labeled samples. Effectiveness of the proposed OPQI method was verified and compared in the field of automated rice quality grading with commonly-used methods and showed superior performance, which lays a foundation for the quality control of GP on assembly lines. PMID:27367703

  17. 基于半监督流形学习的人脸识别方法%Face Recognition Based on Semi-supervised Manifold Learning

    Institute of Scientific and Technical Information of China (English)

    黄鸿; 李见为; 冯海亮

    2008-01-01

    如何有效地将流形学习(Manifold learning,ML)和半监督学习(Semi-supervised learning,SSL)方法进行结合是近年来模式识别和机器学习领域研究的热点问题.提出一种基于半监督流形学习(Semi-supervised manifold learning,SSML)的人脸识别方法,它在部分有标签信息的人脸数据的情况下,通过利用人脸数据本身的非线性流形结构信息和部分标签信息来调整点与点之间的距离形成距离矩阵,而后基于被调整的距离矩阵进行线性近邻重建来实现维数约简,提取低维鉴别特征用于人脸识别.基于公开的人脸数据库上的实验结果表明,该方法能有效地提高人脸识别的性能.

  18. Temporal-pattern learning in neural models

    CERN Document Server

    Genís, Carme Torras

    1985-01-01

    While the ability of animals to learn rhythms is an unquestionable fact, the underlying neurophysiological mechanisms are still no more than conjectures. This monograph explores the requirements of such mechanisms, reviews those previously proposed and postulates a new one based on a direct electric coding of stimulation frequencies. Experi­ mental support for the option taken is provided both at the single neuron and neural network levels. More specifically, the material presented divides naturally into four parts: a description of the experimental and theoretical framework where this work becomes meaningful (Chapter 2), a detailed specifica­ tion of the pacemaker neuron model proposed together with its valida­ tion through simulation (Chapter 3), an analytic study of the behavior of this model when submitted to rhythmic stimulation (Chapter 4) and a description of the neural network model proposed for learning, together with an analysis of the simulation results obtained when varying seve­ ral factors r...

  19. The Learning Process of Supervisees Who Engage in the Reflecting Team Model within Group Supervision: A Grounded Theory Inquiry

    Science.gov (United States)

    Pender, Rebecca Lynn

    2012-01-01

    In recent years, counselor educators have begun to incorporate the use of the reflecting team process with the training of counselors. Specifically, the reflecting team has been used in didactic courses (Cox, 2003; Landis & Young, 1994; Harrawood, Wilde & Parmanand, 2011) and in supervision (Cox, 1997; Prest, Darden, & Keller, 1990;…

  20. Learning pattern recognition and decision making in the insect brain

    Science.gov (United States)

    Huerta, R.

    2013-01-01

    We revise the current model of learning pattern recognition in the Mushroom Bodies of the insects using current experimental knowledge about the location of learning, olfactory coding and connectivity. We show that it is possible to have an efficient pattern recognition device based on the architecture of the Mushroom Bodies, sparse code, mutual inhibition and Hebbian leaning only in the connections from the Kenyon cells to the output neurons. We also show that despite the conventional wisdom that believes that artificial neural networks are the bioinspired model of the brain, the Mushroom Bodies actually resemble very closely Support Vector Machines (SVMs). The derived SVM learning rules are situated in the Mushroom Bodies, are nearly identical to standard Hebbian rules, and require inhibition in the output. A very particular prediction of the model is that random elimination of the Kenyon cells in the Mushroom Bodies do not impair the ability to recognize odorants previously learned.

  1. submitter Studies of CMS data access patterns with machine learning techniques

    CERN Document Server

    De Luca, Silvia

    This thesis presents a study of the Grid data access patterns in distributed analysis in the CMS experiment at the LHC accelerator. This study ranges from the deep analysis of the historical patterns of access to the most relevant data types in CMS, to the exploitation of a supervised Machine Learning classification system to set-up a machinery able to eventually predict future data access patterns - i.e. the so-called dataset “popularity” of the CMS datasets on the Grid - with focus on specific data types. All the CMS workflows run on the Worldwide LHC Computing Grid (WCG) computing centers (Tiers), and in particular the distributed analysis systems sustains hundreds of users and applications submitted every day. These applications (or “jobs”) access different data types hosted on disk storage systems at a large set of WLCG Tiers. The detailed study of how this data is accessed, in terms of data types, hosting Tiers, and different time periods, allows to gain precious insight on storage occupancy ove...

  2. Machine Learning of the Reactor Core Loading Pattern Critical Parameters

    Directory of Open Access Journals (Sweden)

    Krešimir Trontl

    2008-01-01

    Full Text Available The usual approach to loading pattern optimization involves high degree of engineering judgment, a set of heuristic rules, an optimization algorithm, and a computer code used for evaluating proposed loading patterns. The speed of the optimization process is highly dependent on the computer code used for the evaluation. In this paper, we investigate the applicability of a machine learning model which could be used for fast loading pattern evaluation. We employ a recently introduced machine learning technique, support vector regression (SVR, which is a data driven, kernel based, nonlinear modeling paradigm, in which model parameters are automatically determined by solving a quadratic optimization problem. The main objective of the work reported in this paper was to evaluate the possibility of applying SVR method for reactor core loading pattern modeling. We illustrate the performance of the solution and discuss its applicability, that is, complexity, speed, and accuracy.

  3. 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。

  4. Ambient Displays and Game Design Patterns for Social Learning

    NARCIS (Netherlands)

    Kelle, Sebastian; Börner, Dirk; Kalz, Marco; Specht, Marcus; Glahn, Christian

    2011-01-01

    Kelle, S., Börner, D., Kalz, M., Specht, M., & Glahn, C. (2010). Ambient Displays and Game Design Patterns for Social Learning. In B. Chang, T. Hirashima, & H. Ogata (Eds.), Joint Proceedings of the Work-in-Progress Poster and Invited Young Researcher Symposium for the 18th International Conference

  5. Influence of Organisational Defensive Patterns on Learning ICT

    Science.gov (United States)

    Yau, Hon Keung; Cheng, Alison Lai Fong

    2011-01-01

    Purpose: The purpose of this paper is to investigate whether the IT professionals in a Hong Kong public transport company have a general perception of influence of the organisational defensive patterns on learning of ICT; and whether skilled incompetence, organisational defensive routines and fancy footwork are positively associated with each…

  6. Ambient Displays and Game Design Patterns for Social Learning

    NARCIS (Netherlands)

    Kelle, Sebastian; Börner, Dirk; Kalz, Marco; Specht, Marcus; Glahn, Christian

    2011-01-01

    Kelle, S., Börner, D., Kalz, M., Specht, M., & Glahn, C. (2010). Ambient Displays and Game Design Patterns for Social Learning. In B. Chang, T. Hirashima, & H. Ogata (Eds.), Joint Proceedings of the Work-in-Progress Poster and Invited Young Researcher Symposium for the 18th International Conference

  7. 基于半监督的SVM迁移学习文本分类算法%Semi-Supervised Transfer Learning Text Classiifcation Algorithms Based on SVM

    Institute of Scientific and Technical Information of China (English)

    谭建平; 刘波; 肖燕珊

    2016-01-01

    随着互联网的快速发展,文本信息量巨大,大规模的文本处理已经成为一个挑战。文本处理的一个重要技术便是分类,基于SVM的传统文本分类算法已经无法满足快速的文本增长分类。于是如何利用过时的历史文本数据(源任务数据)进行迁移来帮助新产生文本数据进行分类显得异常重要。文章提出了基于半监督的SVM迁移学习算法(Semi-supervised TL_SVM)来对文本进行分类。首先,在半监督SVM的模型中引入迁移学习,构建分类模型。其次,采用交互迭代的方法对目标方程求解,最终得到面向目标领域的分类器。实验验证了基于半监督的SVM迁移学习分类器具有比传统分类器更高的精确度。%With the rapid development of the Internet, texts contain a huge amount of information and the large-scale text processing has become a challenge. An important technical of the text processing is classiifcation, the traditional text categorization algorithm based on SVM has been unable to meet the rapid growth of text classiifcation. So how to utilize the source tasks data to help build a transfer learning classiifer for the target task is especially important. Semi-supervised TL_SVM algorithms is proposed to text classiifcation. First, semi-supervised SVM model combines transfer learning to build the model of classiifcation. Second, we utilize the iterative algorithm to solve the optimization function and obtain the transfer classiifer for the target task. Experiments have shown that our Semi-supervised-based transfer SVM can obtain higher accuracy compared with the traditional method.

  8. Electrocardiogram (ECG) pattern modeling and recognition via deterministic learning

    Institute of Scientific and Technical Information of China (English)

    Xunde DONG; Cong WANG; Junmin HU; Shanxing OU

    2014-01-01

    A method for electrocardiogram (ECG) pattern modeling and recognition via deterministic learning theory is presented in this paper. Instead of recognizing ECG signals beat-to-beat, each ECG signal which contains a number of heartbeats is recognized. The method is based entirely on the temporal features (i.e., the dynamics) of ECG patterns, which contains complete information of ECG patterns. A dynamical model is employed to demonstrate the method, which is capable of generating synthetic ECG signals. Based on the dynamical model, the method is shown in the following two phases:the identification (training) phase and the recognition (test) phase. In the identification phase, the dynamics of ECG patterns is accurately modeled and expressed as constant RBF neural weights through the deterministic learning. In the recognition phase, the modeling results are used for ECG pattern recognition. The main feature of the proposed method is that the dynamics of ECG patterns is accurately modeled and is used for ECG pattern recognition. Experimental studies using the Physikalisch-Technische Bundesanstalt (PTB) database are included to demonstrate the effectiveness of the approach.

  9. Learning and Prospective Recall of Noisy Spike Pattern Episodes

    Directory of Open Access Journals (Sweden)

    Karl eDockendorf

    2013-06-01

    Full Text Available Spike patterns in vivo are often incomplete or corrupted with noise that makes inputs to neuronal networks appear to vary although they may, in fact, be samples of a single underlying pattern or repeated presentation. Here we present a recurrent spiking neural network (SNN model that learns noisy pattern sequences through the use of homeostasis and spike-timing dependent plasticity (STDP. We find that the changes in the synaptic weight vector during learning of patterns of random ensembles are approximately orthogonal in a reduced dimension space when the patterns are constructed to minimize overlap in representations. Using this model, representations of sparse patterns maybe associated through co-activated firing and integrated into ensemble representations. While the model is tolerant to noise, prospective activity and pattern completion differ in their ability to adapt in the presence of noise. One version of the model is able to demonstrate the recently discovered phenomena of preplay and replay reminiscent of hippocampal like behaviors.

  10. Learning shapes spatiotemporal brain patterns for flexible categorical decisions.

    Science.gov (United States)

    Li, Sheng; Mayhew, Stephen D; Kourtzi, Zoe

    2012-10-01

    Learning is thought to facilitate our ability to perform complex perceptual tasks and optimize brain circuits involved in decision making. However, little is known about the experience-dependent mechanisms in the human brain that support our ability to make fine categorical judgments. Previous work has focused on identifying spatial brain patterns (i.e., areas) that change with learning. Here, we take advantage of the complementary high spatial and temporal resolution of simultaneous electroencephalography-functional magnetic resonance imaging (EEG-fMRI) to identify the spatiotemporal dynamics between cortical networks involved in flexible category learning. Observers were trained to use different decision criteria (i.e., category boundaries) when making fine categorical judgments on morphed stimuli (i.e., radial vs. concentric patterns). Our findings demonstrate that learning acts on a feedback-based circuit that supports fine categorical judgments. Experience-dependent changes in the behavioral decision criterion were associated with changes in later perceptual processes engaging higher occipitotemporal and frontoparietal circuits. In contrast, category learning did not modulate early processes in a medial frontotemporal network that are thought to support the coarse interpretation of visual scenes. These findings provide evidence that learning flexible criteria for fine categorical judgments acts on distinct spatiotemporal brain circuits and shapes the readout of sensory signals that provide evidence for categorical decisions.

  11. Learning Patterns as Criterion for Forming Work Groups in 3D Simulation Learning Environments

    Science.gov (United States)

    Maria Cela-Ranilla, Jose; Molías, Luis Marqués; Cervera, Mercè Gisbert

    2016-01-01

    This study analyzes the relationship between the use of learning patterns as a grouping criterion to develop learning activities in the 3D simulation environment at University. Participants included 72 Spanish students from the Education and Marketing disciplines. Descriptive statistics and non-parametric tests were conducted. The process was…

  12. The Role of Statistical Learning and Working Memory in L2 Speakers' Pattern Learning

    Science.gov (United States)

    McDonough, Kim; Trofimovich, Pavel

    2016-01-01

    This study investigated whether second language (L2) speakers' morphosyntactic pattern learning was predicted by their statistical learning and working memory abilities. Across three experiments, Thai English as a Foreign Language (EFL) university students (N = 140) were exposed to either the transitive construction in Esperanto (e.g., "tauro…

  13. A Learning Patterns Perspective on Student Learning in Higher Education: State of the Art and Moving Forward

    Science.gov (United States)

    Vermunt, Jan D.; Donche, Vincent

    2017-01-01

    The aim of this article is to review the state of the art of research and theory development on student learning patterns in higher education and beyond. First, the learning patterns perspective and the theoretical framework are introduced. Second, research published since 2004 on student learning patterns is systematically identified and…

  14. Comparison of Three Supervised Learning Methods for Digital Soil Mapping: Application to a Complex Terrain in the Ecuadorian Andes

    Directory of Open Access Journals (Sweden)

    Martin Hitziger

    2014-01-01

    Full Text Available A digital soil mapping approach is applied to a complex, mountainous terrain in the Ecuadorian Andes. Relief features are derived from a digital elevation model and used as predictors for topsoil texture classes sand, silt, and clay. The performance of three statistical learning methods is compared: linear regression, random forest, and stochastic gradient boosting of regression trees. In linear regression, a stepwise backward variable selection procedure is applied and overfitting is controlled by minimizing Mallow’s Cp. For random forest and boosting, the effect of predictor selection and tuning procedures is assessed. 100-fold repetitions of a 5-fold cross-validation of the selected modelling procedures are employed for validation, uncertainty assessment, and method comparison. Absolute assessment of model performance is achieved by comparing the prediction error of the selected method and the mean. Boosting performs best, providing predictions that are reliably better than the mean. The median reduction of the root mean square error is around 5%. Elevation is the most important predictor. All models clearly distinguish ridges and slopes. The predicted texture patterns are interpreted as result of catena sequences (eluviation of fine particles on slope shoulders and landslides (mixing up mineral soil horizons on slopes.

  15. Incremental Image Classification Method Based on Semi-Supervised Learning%基于半监督学习的增量图像分类方法

    Institute of Scientific and Technical Information of China (English)

    梁鹏; 黎绍发; 覃姜维; 罗剑高

    2012-01-01

    In order to use large numbers of unlabeled images effectively, an image classification method is proposed based on semi-supervised learning. The proposed method bridges a large amount of unlabeled images and limited numbers of labeled images by exploiting the common topics. The classification accuracy is improved by using the must-link constraint and cannot-link constraint of labeled images. The experimental results on Caltech-101 and 7-classes image dataset demonstrate that the classification accuracy improves about 10% by the proposed method. Furthermore, due to the present semi-supervised image classification methods lacking of incremental learning ability, an incremental implementation of our method is proposed. Comparing with non-incremental learning model in literature, the incrementallearning method improves the computation efficiency of nearly 90%.%为有效使用大量未标注的图像进行分类,提出一种基于半监督学习的图像分类方法.通过共同的隐含话题桥接少量已标注的图像和大量未标注的图像,利用已标注图像的Must-link约束和Cannot-link约束提高未标注图像分类的精度.实验结果表明,该方法有效提高Caltech-101数据集和7类图像集约10%的分类精度.此外,针对目前绝大部分半监督图像分类方法不具备增量学习能力这一缺点,提出该方法的增量学习模型.实验结果表明,增量学习模型相比无增量学习模型提高近90%的计算效率.

  16. Catalog Learning: Carabid Beetles Learn to Manipulate with Innate Coherent Behavioral Patterns

    Directory of Open Access Journals (Sweden)

    Zhanna Reznikova

    2013-07-01

    Full Text Available One of the most fascinating problems in comparative psychology is how learning contributes to solving specific functional problems in animal life, and which forms of learning our species shares with non-human animals. Simulating a natural situation of territorial conflicts between predatory carabids and red wood ants in field and laboratory experiments, we have revealed a relatively simple and quite natural form of learning that has been overlooked. We call it catalog learning, the name we give to the ability of animals to establish associations between stimuli and coherent behavioral patterns (patterns consist of elementary motor acts that have a fixed order. Instead of budgeting their motor acts gradually, from chaotic to rational sequences in order to learn something new, which is characteristic for a conditioning response, animals seem to be “cataloguing” their repertoire of innate coherent behavioral patterns in order to optimize their response to a certain repetitive event. This form of learning can be described as “stimulus-pattern” learning. In our experiments four “wild” carabid species, whose cognitive abilities have never been studied before, modified their behavior in a rather natural manner in order to avoid damage from aggressive ants. Beetles learned to select the relevant coherent behavioral patterns from the set of seven patterns, which are common to all four species and apparently innate. We suggest that this form of learning differs from the known forms of associative learning, and speculate that it is quite universal and can be present in a wide variety of species, both invertebrate and vertebrate. This study suggests a new link between the concepts of cognition and innateness.

  17. Out-of-Sample Extrapolation utilizing Semi-Supervised Manifold Learning (OSE-SSL): Content Based Image Retrieval for Histopathology Images.

    Science.gov (United States)

    Sparks, Rachel; Madabhushi, Anant

    2016-06-06

    Content-based image retrieval (CBIR) retrieves database images most similar to the query image by (1) extracting quantitative image descriptors and (2) calculating similarity between database and query image descriptors. Recently, manifold learning (ML) has been used to perform CBIR in a low dimensional representation of the high dimensional image descriptor space to avoid the curse of dimensionality. ML schemes are computationally expensive, requiring an eigenvalue decomposition (EVD) for every new query image to learn its low dimensional representation. We present out-of-sample extrapolation utilizing semi-supervised ML (OSE-SSL) to learn the low dimensional representation without recomputing the EVD for each query image. OSE-SSL incorporates semantic information, partial class label, into a ML scheme such that the low dimensional representation co-localizes semantically similar images. In the context of prostate histopathology, gland morphology is an integral component of the Gleason score which enables discrimination between prostate cancer aggressiveness. Images are represented by shape features extracted from the prostate gland. CBIR with OSE-SSL for prostate histology obtained from 58 patient studies, yielded an area under the precision recall curve (AUPRC) of 0.53 ± 0.03 comparatively a CBIR with Principal Component Analysis (PCA) to learn a low dimensional space yielded an AUPRC of 0.44 ± 0.01.

  18. Supervised Transfer Sparse Coding

    KAUST Repository

    Al-Shedivat, Maruan

    2014-07-27

    A combination of the sparse coding and transfer learn- ing techniques was shown to be accurate and robust in classification tasks where training and testing objects have a shared feature space but are sampled from differ- ent underlying distributions, i.e., belong to different do- mains. The key assumption in such case is that in spite of the domain disparity, samples from different domains share some common hidden factors. Previous methods often assumed that all the objects in the target domain are unlabeled, and thus the training set solely comprised objects from the source domain. However, in real world applications, the target domain often has some labeled objects, or one can always manually label a small num- ber of them. In this paper, we explore such possibil- ity and show how a small number of labeled data in the target domain can significantly leverage classifica- tion accuracy of the state-of-the-art transfer sparse cod- ing methods. We further propose a unified framework named supervised transfer sparse coding (STSC) which simultaneously optimizes sparse representation, domain transfer and classification. Experimental results on three applications demonstrate that a little manual labeling and then learning the model in a supervised fashion can significantly improve classification accuracy.

  19. 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.

  20. 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.

  1. Learning patterns of life from intelligence analyst chat

    Science.gov (United States)

    Schneider, Michael K.; Alford, Mark; Babko-Malaya, Olga; Blasch, Erik; Chen, Lingji; Crespi, Valentino; HandUber, Jason; Haney, Phil; Nagy, Jim; Richman, Mike; Von Pless, Gregory; Zhu, Howie; Rhodes, Bradley J.

    2016-05-01

    Our Multi-INT Data Association Tool (MIDAT) learns patterns of life (POL) of a geographical area from video analyst observations called out in textual reporting. Typical approaches to learning POLs from video make use of computer vision algorithms to extract locations in space and time of various activities. Such approaches are subject to the detection and tracking performance of the video processing algorithms. Numerous examples of human analysts monitoring live video streams annotating or "calling out" relevant entities and activities exist, such as security analysis, crime-scene forensics, news reports, and sports commentary. This user description typically corresponds with textual capture, such as chat. Although the purpose of these text products is primarily to describe events as they happen, organizations typically archive the reports for extended periods. This archive provides a basis to build POLs. Such POLs are useful for diagnosis to assess activities in an area based on historical context, and for consumers of products, who gain an understanding of historical patterns. MIDAT combines natural language processing, multi-hypothesis tracking, and Multi-INT Activity Pattern Learning and Exploitation (MAPLE) technologies in an end-to-end lab prototype that processes textual products produced by video analysts, infers POLs, and highlights anomalies relative to those POLs with links to "tracks" of related activities performed by the same entity. MIDAT technologies perform well, achieving, for example, a 90% F1-value on extracting activities from the textual reports.

  2. Learning Design Patterns for Hybrid Synchronous Video-Mediated Learning Environments

    DEFF Research Database (Denmark)

    Weitze, Charlotte Lærke

    2016-01-01

    of their daily practices and also participated in a design-based research project exploring new learning designs for this environment (Weitze, 2015). The teachers’ traditional learning designs were challenged, and this led to altered pedagogical approaches with less group-work and an extensive use of monologue......This article describes an innovative learning environment where remote and face-to-face full-time general upper secondary adult students jointly participate in the same live classes at VUC Storstrøm, an adult learning centre in Denmark. The teachers developed new learning designs as a part......-based teaching. The findings were, however, that the teachers, through pedagogically innovative strategies, developed knowledge about how their pedagogical patterns in this hybrid synchronous learning situation could be supported by an array of additional educational technologies and strategies to create...

  3. Learning Design Patterns for Hybrid Synchronous Video-Mediated Learning Environments

    DEFF Research Database (Denmark)

    Weitze, Charlotte Lærke

    2016-01-01

    -based teaching. The findings were, however, that the teachers, through pedagogically innovative strategies, developed knowledge about how their pedagogical patterns in this hybrid synchronous learning situation could be supported by an array of additional educational technologies and strategies to create......This article describes an innovative learning environment where remote and face-to-face full-time general upper secondary adult students jointly participate in the same live classes at VUC Storstrøm, an adult learning centre in Denmark. The teachers developed new learning designs as a part...... of their daily practices and also participated in a design-based research project exploring new learning designs for this environment (Weitze, 2015). The teachers’ traditional learning designs were challenged, and this led to altered pedagogical approaches with less group-work and an extensive use of monologue...

  4. 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.

  5. Learning Pitch Patterns in Lexical Identification by Native English-Speaking Adults

    Science.gov (United States)

    Wong, Patrick C. M.; Perrachione, Tyler K.

    2007-01-01

    The current study investigates the learning of nonnative suprasegmental patterns for word identification. Native English-speaking adults learned to use suprasegmentals (pitch patterns) to identify a vocabulary of six English pseudosyllables superimposed with three pitch patterns (18 words). Successful learning of the vocabulary necessarily…

  6. Differential Effects of a Long Teacher Training Internship on Students' Learning-to-Teach Patterns

    Science.gov (United States)

    Donche, Vincent; Endedijk, Maaike D.; van Daal, Tine

    2015-01-01

    To become a lifelong learner as a teacher, student teachers already have to learn how to direct their own learning during initial teacher education programmes. Previous empirical research has shown that student teachers differ in their patterns of learning-to-teach, but few is known about the changeability of these learning patterns throughout…

  7. Zebra finches are able to learn affixation-like patterns.

    Science.gov (United States)

    Chen, Jiani; Jansen, Naomi; ten Cate, Carel

    2016-01-01

    Adding an affix to transform a word is common across the world languages, with the edges of words more likely to carry out such a function. However, detecting affixation patterns is also observed in learning tasks outside the domain of language, suggesting that the underlying mechanism from which affixation patterns have arisen may not be language or even human specific. We addressed whether a songbird, the zebra finch, is able to discriminate between, and generalize, affixation-like patterns. Zebra finches were trained and tested in a Go/Nogo paradigm to discriminate artificial song element sequences resembling prefixed and suffixed 'words.' The 'stems' of the 'words,' consisted of different combinations of a triplet of song elements, to which a fourth element was added as either a 'prefix' or a 'suffix.' After training, the birds were tested with novel stems, consisting of either rearranged familiar element types or novel element types. The birds were able to generalize the affixation patterns to novel stems with both familiar and novel element types. Hence, the discrimination resulting from the training was not based on memorization of individual stimuli, but on a shared property among Go or Nogo stimuli, i.e., affixation patterns. Remarkably, birds trained with suffixation as Go pattern showed clear evidence of using both prefix and suffix, while those trained with the prefix as the Go stimulus used primarily the prefix. This finding illustrates that an asymmetry in attending to different affixations is not restricted to human languages.

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

    NARCIS (Netherlands)

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

    2014-01-01

    BACKGROUND: 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) a

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

    NARCIS (Netherlands)

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

    2014-01-01

    BACKGROUND: 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) a

  10. Learning Techniques for Automatic Test Pattern Generation using Boolean Satisfiability

    Directory of Open Access Journals (Sweden)

    Liu Xin

    2013-07-01

    Full Text Available Automatic Test Pattern Generation (ATPG is one of the core problems in testing of digital circuits. ATPG algorithms based on Boolean Satisfiability (SAT turned out to be very powerful, due to great advances in the performance of satisfiability solvers for propositional logic in the last two decades. SAT-based ATPG clearly outperforms classical approaches especially for hard-to-detect faults. But its inaccessibility of structural information and don’t care, there exists the over-specification problem of input patterns. In this paper we present techniques to delve into an additional layer to make use of structural properties of the circuit and value justification relations to a generic SAT algorithm. It joins binary decision graphs (BDD and SAT techniques to improve the efficiency of ATPG. It makes a study of inexpensive reconvergent fanout analysis of circuit to gather information on the local signal correlation by using BDD learning, then uses the above learned information to restrict and focus the overall search space of SAT-based ATPG. The learning technique is effective and lightweight. Experimental results show the effectiveness of the approach.

  11. Clinical supervision by consultants in teaching hospitals.

    Science.gov (United States)

    Hore, Craig T; Lancashire, William; Fassett, Robert G

    2009-08-17

    Clinical supervision is a vital part of postgraduate medical education. Without it, trainees may not learn effectively from their experiences; this may lead to acceptance by registrars and junior doctors of lower standards of care. Currently, supervision is provided by consultants to registrars and junior doctors, and by registrars to junior doctors. Evidence suggests that the clinical supervision provided to postgraduate doctors is inadequate. Registrars and juniors doctors have the right to expect supervision in the workplace. Impediments to the provision of clinical supervision include competing demands of hospital service provision on trainees and supervisors, lack of clarity of job descriptions, private versus public commitments of supervisors and lack of interest. Supervisors should be trained in the process of supervision and provided with the time and resources to conduct it. Those being supervised should be provided with clear expectations of the process. We need to create and develop systems, environments and cultures that support high standards of conduct and effective clinical supervision. These systems must ensure the right to supervision, feedback, support, decent working conditions and respect for both trainees and their supervisors.

  12. GOexpress: an R/Bioconductor package for the identification and visualisation of robust gene ontology signatures through supervised learning of gene expression data.

    Science.gov (United States)

    Rue-Albrecht, Kévin; McGettigan, Paul A; Hernández, Belinda; Nalpas, Nicolas C; Magee, David A; Parnell, Andrew C; Gordon, Stephen V; MacHugh, David E

    2016-03-11

    Identification of gene expression profiles that differentiate experimental groups is critical for discovery and analysis of key molecular pathways and also for selection of robust diagnostic or prognostic biomarkers. While integration of differential expression statistics has been used to refine gene set enrichment analyses, such approaches are typically limited to single gene lists resulting from simple two-group comparisons or time-series analyses. In contrast, functional class scoring and machine learning approaches provide powerful alternative methods to leverage molecular measurements for pathway analyses, and to compare continuous and multi-level categorical factors. We introduce GOexpress, a software package for scoring and summarising the capacity of gene ontology features to simultaneously classify samples from multiple experimental groups. GOexpress integrates normalised gene expression data (e.g., from microarray and RNA-seq experiments) and phenotypic information of individual samples with gene ontology annotations to derive a ranking of genes and gene ontology terms using a supervised learning approach. The default random forest algorithm allows interactions between all experimental factors, and competitive scoring of expressed genes to evaluate their relative importance in classifying predefined groups of samples. GOexpress enables rapid identification and visualisation of ontology-related gene panels that robustly classify groups of samples and supports both categorical (e.g., infection status, treatment) and continuous (e.g., time-series, drug concentrations) experimental factors. The use of standard Bioconductor extension packages and publicly available gene ontology annotations facilitates straightforward integration of GOexpress within existing computational biology pipelines.

  13. Evaluating students' perception of their clinical placements - testing the clinical learning environment and supervision and nurse teacher scale (CLES + T scale) in Germany.

    Science.gov (United States)

    Bergjan, Manuela; Hertel, Frank

    2013-11-01

    Clinical nursing education in Germany has not received attention in nursing science and practice for a long time, as it often seems to be a more or less "formalized appendix" of nursing education. Several development projects of clinical education taking place are mainly focused on the qualification of clinical preceptors. However, the clinical context and its influence on learning processes have still not been sufficiently investigated. The aim of this study was the testing of a German version of the clinical learning environment and supervision and nurse teacher scale (CLES + T scale). The sample of the pilot study consists of first-, second- and third-year student nurses (n=240) of a university nursing school from January to March 2011. Psychometric testing of the instrument is carried out by selected methods of classical testing theories using SPPS 19. The results show transferability of all subcategories of the CLES + T scale in the non-academic nursing education system of a university hospital in Germany, without the teacher scale. The strongest factor is "supervisory relationship". The German version of the CLES + T scale may help to evaluate and compare traditional and new models in clinical nursing education.

  14. Fast learning of biased patterns in neural networks.

    Science.gov (United States)

    Wendemuth, A; Sherrington, D

    1993-09-01

    Usual neural network gradient descent training algorithms require training times of the same order as the number of neurons N if the patterns are biased. In this paper, modified algorithms are presented which require training times equal to those in unbiased cases which are of order 1. Exact convergence proofs are given. Gain parameters which produce minimal learning times in large networks are computed by replica methods. It is demonstrated how these modified algorithms are applied in order to produce four types of solutions to the learning problem: 1. A solution with all internal fields equal to the desired output, 2. The Adaline (or pseudo-inverse) solution, 3. The perceptron of optimal stability without threshold and 4. The perceptron of optimal stability with threshold.

  15. Logic Learning Machine and standard supervised methods for Hodgkin's lymphoma prognosis using gene expression data and clinical variables.

    Science.gov (United States)

    Parodi, Stefano; Manneschi, Chiara; Verda, Damiano; Ferrari, Enrico; Muselli, Marco

    2016-06-27

    This study evaluates the performance of a set of machine learning techniques in predicting the prognosis of Hodgkin's lymphoma using clinical factors and gene expression data. Analysed samples from 130 Hodgkin's lymphoma patients included a small set of clinical variables and more than 54,000 gene features. Machine learning classifiers included three black-box algorithms (k-nearest neighbour, Artificial Neural Network, and Support Vector Machine) and two methods based on intelligible rules (Decision Tree and the innovative Logic Learning Machine method). Support Vector Machine clearly outperformed any of the other methods. Among the two rule-based algorithms, Logic Learning Machine performed better and identified a set of simple intelligible rules based on a combination of clinical variables and gene expressions. Decision Tree identified a non-coding gene (XIST) involved in the early phases of X chromosome inactivation that was overexpressed in females and in non-relapsed patients. XIST expression might be responsible for the better prognosis of female Hodgkin's lymphoma patients.

  16. Considering Alternate Futures to Classify Off-Task Behavior as Emotion Self-Regulation: A Supervised Learning Approach

    Science.gov (United States)

    Sabourin, Jennifer L.; Rowe, Jonathan P.; Mott, Bradford W.; Lester, James C.

    2013-01-01

    Over the past decade, there has been growing interest in real-time assessment of student engagement and motivation during interactions with educational software. Detecting symptoms of disengagement, such as off-task behavior, has shown considerable promise for understanding students' motivational characteristics during learning. In this paper, we…

  17. 一种结合半监督Boosting方法的迁移学习算法%Transfer Learning via Semi-supervised Boosting Method

    Institute of Scientific and Technical Information of China (English)

    洪佳明; 陈炳超; 印鉴

    2011-01-01

    迁移学习是数据挖掘中的一个研究方向,试图重用相关领域的数据样本,将相关领域的知识”迁移”到新领域中帮助训练.当前,基于实例的迁移学习算法容易产生过度拟合的问题,不能充分利用相关领域中的有用数据,为了避免这个问题,通过引入目标领域的无标记样本参与训练,利用半监督Boosting方法,提出一种新的迁移学习算法,能够对样本的相关性进行更好的判断,减少选择性偏差的影响,在大量文本数据集上的实验表明了新算法的有效性.%Transfer learning aims at reusing existing instances from other related domains to help learning models for the target domain. Existing algorithms in instance-transfer learning might easily suffer from the problem of overfitting. To address this problem, we propose to incorporate additional unlabeled instances from the target domain, so that more domain knowledge can be brought into the training process. Specifically, under the generalized framework of boosting methods, we show that a semi-supervised boosting method can be applied to help re-weighting the source domain instances, making the final classifiers less sensitive to the small amount of labeled instances in the target domain. Extensive experiments confirm the efficiency of the new algorithm.

  18. Supervised Multi-Manifold Learning Algorithm Based on ISOMAP%基于等距映射的监督多流形学习算法

    Institute of Scientific and Technical Information of China (English)

    邵超; 万春红

    2014-01-01

    The existing supervised multi-manifold learning algorithms adjust the distances between data points according to their class labels, and hence the multiple manifolds can be classified successfully. However, the poor generalization ability of these algorithms results in unfaithful display of the intrinsic geometric structure of some manifolds. A supervised multi-manifold learning algorithm based on Isometric mapping ( ISOMAP) is proposed. The shortest path algorithm suitable for the multi-manifold structure is used to compute the shortest path distances which can effectively approximate the corresponding geodesic distances even in the multi-manifold structure. Then, Sammon mapping is used to further preserve shorter distances in the low-dimensional embedding space. Consequently, the intrinsic geometric structure of each manifold can be faithfully displayed. Moreover, the manifolds of new data points can be precisely judged based on the similarities between neighboring local tangent spaces according to the local Euclidean nature of the manifold, and thus the proposed algorithm obtains a good generalization ability. The effectiveness of the proposed algorithm is verified by experimental results.%目前的监督多流形学习算法大多数都根据数据的类别标记对彼此间的距离进行调整,能较好实现多流形的分类,但难以成功展现各流形的内在几何结构,泛化能力也较差,因此文中提出一种基于等距映射的监督多流形学习算法。该算法采用适合于多流形的最短路径算法,得到在多流形下依然能正确逼近相应测地距离的最短路径距离,并采用Sammon映射以更好地保持短距离,最终可成功展现各流形的内在几何结构。此外,该算法根据邻近局部切空间的相似性可准确判定新数据点所在的流形,从而具有较强的泛化能力。该算法的有效性可通过实验结果得以证实。

  19. Supervision as Metaphor

    Science.gov (United States)

    Lee, Alison; Green, Bill

    2009-01-01

    This article takes up the question of the language within which discussion of research degree supervision is couched and framed, and the consequences of such framings for supervision as a field of pedagogical practice. It examines the proliferation and intensity of metaphor, allegory and allusion in the language of candidature and supervision,…

  20. A Supervision of Solidarity

    Science.gov (United States)

    Reynolds, Vikki

    2010-01-01

    This article illustrates an approach to therapeutic supervision informed by a philosophy of solidarity and social justice activism. Called a "Supervision of Solidarity", this approach addresses the particular challenges in the supervision of therapists who work alongside clients who are subjected to social injustice and extreme marginalization. It…

  1. Learning taxis’ cruising patterns with Ripley’s K function

    Institute of Scientific and Technical Information of China (English)

    宗芳; 张慧永

    2015-01-01

    Taxi drivers’ cruising patterns are learnt with GPS trajectory data collected in Shenzhen, China. By employing Ripley’s K function, the impacts of land use and pick-up experience on taxis’ cruising behavior are investigated concerning about both intensity of influence and radius of influence. The results indicate that, in general, taxi drivers tend to learn more from land use characteristics than from pick-up experience. The optimal radius of influence of land use points and previous pick-up points is 14.18 km and 9.93 km, respectively. The findings also show that the high-earning drivers or thorough drivers pay more attention to land use characteristics and tend to cruise in high-density area, while the low-earning drivers or focus drivers prefer to learn more from previous pick-up experience and select the area which is far away from the high-density area. These findings facilitate the development of measures of managing taxi’s travel behavior by providing useful insights into taxis’ cruising patterns. The results also provide useful advice for taxi drivers to make efficient cruising decision, which will contribute to the improvement of cruising efficiency and the reduction of negative effects.

  2. A High Accuracy Method for Semi-supervised Information Extraction

    Energy Technology Data Exchange (ETDEWEB)

    Tratz, Stephen C.; Sanfilippo, Antonio P.

    2007-04-22

    Customization to specific domains of dis-course and/or user requirements is one of the greatest challenges for today’s Information Extraction (IE) systems. While demonstrably effective, both rule-based and supervised machine learning approaches to IE customization pose too high a burden on the user. Semi-supervised learning approaches may in principle offer a more resource effective solution but are still insufficiently accurate to grant realistic application. We demonstrate that this limitation can be overcome by integrating fully-supervised learning techniques within a semi-supervised IE approach, without increasing resource requirements.

  3. Social Learning Network Analysis Model to Identify Learning Patterns Using Ontology Clustering Techniques and Meaningful Learning

    Science.gov (United States)

    Firdausiah Mansur, Andi Besse; Yusof, Norazah

    2013-01-01

    Clustering on Social Learning Network still not explored widely, especially when the network focuses on e-learning system. Any conventional methods are not really suitable for the e-learning data. SNA requires content analysis, which involves human intervention and need to be carried out manually. Some of the previous clustering techniques need…

  4. The Analysis of the Thematic Progression Patterns in "The Great Learning"

    Institute of Scientific and Technical Information of China (English)

    王利娜; 金俊淑

    2007-01-01

    This paper intends to introduce briefly the thematic progression patterns in Systemic- Functional Grammar, then analyze its application in "The Great Learning" which is one of the classics of the Confucius and his disciples. The analysis of the thematic progression patterns of "The Great Learning" is meaningful for both understanding and appreciating "The Great Learning".

  5. Involvement of Working Memory in College Students' Sequential Pattern Learning and Performance

    Science.gov (United States)

    Kundey, Shannon M. A.; De Los Reyes, Andres; Rowan, James D.; Lee, Bern; Delise, Justin; Molina, Sabrina; Cogdill, Lindsay

    2013-01-01

    When learning highly organized sequential patterns of information, humans and nonhuman animals learn rules regarding the hierarchical structures of these sequences. In three experiments, we explored the role of working memory in college students' sequential pattern learning and performance in a computerized task involving a sequential…

  6. Involvement of Working Memory in College Students' Sequential Pattern Learning and Performance

    Science.gov (United States)

    Kundey, Shannon M. A.; De Los Reyes, Andres; Rowan, James D.; Lee, Bern; Delise, Justin; Molina, Sabrina; Cogdill, Lindsay

    2013-01-01

    When learning highly organized sequential patterns of information, humans and nonhuman animals learn rules regarding the hierarchical structures of these sequences. In three experiments, we explored the role of working memory in college students' sequential pattern learning and performance in a computerized task involving a sequential…

  7. 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.

  8. 上司不当督导与下属绩效:反馈寻求行为和学习目标定向的作用%Abusive Supervision and Employee' Performance: Mechanisms of FSB and Learning Goral Orientation

    Institute of Scientific and Technical Information of China (English)

    申传刚; 马红宇; 杨璟; 刘腾飞

    2012-01-01

    本研究从下属反馈管理行为的视角来探索领导与下属的社会交换过程.具体为探讨下属的反馈寻求行为在上司不当督导与下属绩效之间的中介作用,下属的学习目标定向对上述过程中的调节作用.通过问卷法获得306名下属与上司的对偶数据,基于层级回归和Bootstrap分析的结果表明:上司不当督导不仅直接影响下属的绩效,还能通过抑制下属的反馈寻求行为间接地影响员工的绩效;下属的学习目标定向调节着上司不当督导与下属的反馈寻求行为的关系,当下属的学习目标定向越低,上司不当督导对反馈寻求行为的抑制作用更加明显.%The literature on abusive supervision has consistently demonstrated the negative relationship between member perception of supervisor's abusive behavior and member performance. The process through which relationship supervisor's abusive behavior influences subordinates' performance, however, is still not fully understood. The present study provides a mechanism for the process. Specifically, we predict that the feedback seeking behavior (FSB) of members mediates these relationships, and learning goal orientation moderates the relationship between abusive supervision and FSB.In order to avoid the common method variance problem, two sources of survey were administrated. Data was from a total of 306 matched supervisor-subordinate dyads in 7 enterprises located in Hubei, Zhejiang, Xiamen. Two structured questionnaires were employed as the research instrument for this study. One consisted of three scales designed to measure abusive supervision, FSB and learning goal orientation. Among the major measures, the 15-items abusive supervision was adopted from Tepper (2000); FSB was measured via 6 items that was adopted from Saori Yanagizawa (2008); the five item learning goal orientation scale was adopted from Vandewalle & Cummings (1997). We used a scale adopted from Tusi et al. (1997) in the other

  9. Using Supervised Machine Learning to Classify Real Alerts and Artifact in Online Multisignal Vital Sign Monitoring Data.

    Science.gov (United States)

    Chen, Lujie; Dubrawski, Artur; Wang, Donghan; Fiterau, Madalina; Guillame-Bert, Mathieu; Bose, Eliezer; Kaynar, Ata M; Wallace, David J; Guttendorf, Jane; Clermont, Gilles; Pinsky, Michael R; Hravnak, Marilyn

    2016-07-01

    The use of machine-learning algorithms to classify alerts as real or artifacts in online noninvasive vital sign data streams to reduce alarm fatigue and missed true instability. Observational cohort study. Twenty-four-bed trauma step-down unit. Two thousand one hundred fifty-three patients. Noninvasive vital sign monitoring data (heart rate, respiratory rate, peripheral oximetry) recorded on all admissions at 1/20 Hz, and noninvasive blood pressure less frequently, and partitioned data into training/validation (294 admissions; 22,980 monitoring hours) and test sets (2,057 admissions; 156,177 monitoring hours). Alerts were vital sign deviations beyond stability thresholds. A four-member expert committee annotated a subset of alerts (576 in training/validation set, 397 in test set) as real or artifact selected by active learning, upon which we trained machine-learning algorithms. The best model was evaluated on test set alerts to enact online alert classification over time. The Random Forest model discriminated between real and artifact as the alerts evolved online in the test set with area under the curve performance of 0.79 (95% CI, 0.67-0.93) for peripheral oximetry at the instant the vital sign first crossed threshold and increased to 0.87 (95% CI, 0.71-0.95) at 3 minutes into the alerting period. Blood pressure area under the curve started at 0.77 (95% CI, 0.64-0.95) and increased to 0.87 (95% CI, 0.71-0.98), whereas respiratory rate area under the curve started at 0.85 (95% CI, 0.77-0.95) and increased to 0.97 (95% CI, 0.94-1.00). Heart rate alerts were too few for model development. Machine-learning models can discern clinically relevant peripheral oximetry, blood pressure, and respiratory rate alerts from artifacts in an online monitoring dataset (area under the curve > 0.87).

  10. Good supervision and PBL

    DEFF Research Database (Denmark)

    Otrel-Cass, Kathrin

    This field study was conducted at the Faculty of Social Sciences at Aalborg University with the intention to investigate how students reflect on their experiences with supervision in a PBL environment. The overall aim of this study was to inform about the continued work in strengthening supervision...... at this faculty. This particular study invited Master level students to discuss: • How a typical supervision process proceeds • How they experienced and what they expected of PBL in the supervision process • What makes a good supervision process...

  11. Learning new gait patterns: Exploratory muscle activity during motor learning is not predicted by motor modules.

    Science.gov (United States)

    Ranganathan, Rajiv; Krishnan, Chandramouli; Dhaher, Yasin Y; Rymer, William Z

    2016-03-21

    The motor module hypothesis in motor control proposes that the nervous system can simplify the problem of controlling a large number of muscles in human movement by grouping muscles into a smaller number of modules. Here, we tested one prediction of the modular organization hypothesis by examining whether there is preferential exploration along these motor modules during the learning of a new gait pattern. Healthy college-aged participants learned a new gait pattern which required increased hip and knee flexion during the swing phase while walking in a lower-extremity robot (Lokomat). The new gait pattern was displayed as a foot trajectory in the sagittal plane and participants attempted to match their foot trajectory to this template. We recorded EMG from 8 lower-extremity muscles and we extracted motor modules during both baseline walking and target-tracking using non-negative matrix factorization (NMF). Results showed increased trajectory variability in the first block of learning, indicating that participants were engaged in exploratory behavior. Critically, when we examined the muscle activity during this exploratory phase, we found that the composition of motor modules changed significantly within the first few strides of attempting the new gait pattern. The lack of persistence of the motor modules under even short time scales suggests that motor modules extracted during locomotion may be more indicative of correlated muscle activity induced by the task constraints of walking, rather than reflecting a modular control strategy.

  12. Supervised Machine Learning Algorithms Can Classify Open-Text Feedback of Doctor Performance With Human-Level Accuracy.

    Science.gov (United States)

    Gibbons, Chris; Richards, Suzanne; Valderas, Jose Maria; Campbell, John

    2017-03-15

    Machine learning techniques may be an effective and efficient way to classify open-text reports on doctor's activity for the purposes of quality assurance, safety, and continuing professional development. The objective of the study was to evaluate the accuracy of machine learning algorithms trained to classify open-text reports of doctor performance and to assess the potential for classifications to identify significant differences in doctors' professional performance in the United Kingdom. We used 1636 open-text comments (34,283 words) relating to the performance of 548 doctors collected from a survey of clinicians' colleagues using the General Medical Council Colleague Questionnaire (GMC-CQ). We coded 77.75% (1272/1636) of the comments into 5 global themes (innovation, interpersonal skills, popularity, professionalism, and respect) using a qualitative framework. We trained 8 machine learning algorithms to classify comments and assessed their performance using several training samples. We evaluated doctor performance using the GMC-CQ and compared scores between doctors with different classifications using t tests. Individual algorithm performance was high (range F score=.68 to .83). Interrater agreement between the algorithms and the human coder was highest for codes relating to "popular" (recall=.97), "innovator" (recall=.98), and "respected" (recall=.87) codes and was lower for the "interpersonal" (recall=.80) and "professional" (recall=.82) codes. A 10-fold cross-validation demonstrated similar performance in each analysis. When combined together into an ensemble of multiple algorithms, mean human-computer interrater agreement was .88. Comments that were classified as "respected," "professional," and "interpersonal" related to higher doctor scores on the GMC-CQ compared with comments that were not classified (Pdoctors who were rated as popular or innovative and those who were not rated at all (P>.05). Machine learning algorithms can classify open-text feedback

  13. Instructional Leadership and Supervision in Special Language Programs.

    Science.gov (United States)

    Florez-Tighe, Viola

    A recent review of English as a Second Language (ESL) research revealed an increase in studies dealing with instructional approaches, language learning theories, ESL curriculum, and learning-aid study strategies; however, supervision of teaching in ESL programs was mentioned only occasionally. Supervision, when properly practiced, can provide a…

  14. Automated Classification and Correlation of Drill Cores using High-Resolution Hyperspectral Images and Supervised Pattern Classification Algorithms. Applications to Paleoseismology

    Science.gov (United States)

    Ragona, D. E.; Minster, B.; Rockwell, T.; Jasso, H.

    2006-12-01

    The standard methodology to describe, classify and correlate geologic materials in the field or lab rely on physical inspection of samples, sometimes with the assistance of conventional analytical techniques (e. g. XRD, microscopy, particle size analysis). This is commonly both time-consuming and inherently subjective. Many geological materials share identical visible properties (e.g. fine grained materials, alteration minerals) and therefore cannot be mapped using the human eye alone. Recent investigations have shown that ground- based hyperspectral imaging provides an effective method to study and digitally store stratigraphic and structural data from cores or field exposures. Neural networks and Naive Bayesian classifiers supply a variety of well-established techniques towards pattern recognition, especially for data examples with high- dimensionality input-outputs. In this poster, we present a new methodology for automatic mapping of sedimentary stratigraphy in the lab (drill cores, samples) or the field (outcrops, exposures) using short wave infrared (SWIR) hyperspectral images and these two supervised classification algorithms. High-spatial/spectral resolution data from large sediment samples (drill cores) from a paleoseismic excavation site were collected using a portable hyperspectral scanner with 245 continuous channels measured across the 960 to 2404 nm spectral range. The data were corrected for geometric and radiometric distortions and pre-processed to obtain reflectance at each pixel of the images. We built an example set using hundreds of reflectance spectra collected from the sediment core images. The examples were grouped into eight classes corresponding to materials found in the samples. We constructed two additional example sets by computing the 2-norm normalization, the derivative of the smoothed original reflectance examples. Each example set was divided into four subsets: training, training test, verification and validation. A multi

  15. General asymmetric neutral networks and structure design by genetic algorithms: A learning rule for temporal patterns

    Energy Technology Data Exchange (ETDEWEB)

    Bornholdt, S. [Heidelberg Univ., (Germany). Inst., fuer Theoretische Physik; Graudenz, D. [Lawrence Berkeley Lab., CA (United States)

    1993-07-01

    A learning algorithm based on genetic algorithms for asymmetric neural networks with an arbitrary structure is presented. It is suited for the learning of temporal patterns and leads to stable neural networks with feedback.

  16. Effects of mobile gaming patterns on learning outcomes: a literature review

    NARCIS (Netherlands)

    Schmitz, Birgit; Klemke, Roland; Specht, Marcus

    2013-01-01

    Schmitz, B., Klemke, R., & Specht, M. (2012). Effects of mobile gaming patterns on learning outcomes: A literature review. International Journal of Technology Enhanced Learning, 4(5-6), 345-358. doi:10.1504/IJTEL.2012.051817

  17. Using Optimal Ratio Mask as Training Target for Supervised Speech Separation

    OpenAIRE

    Xia, Shasha; Li, Hao; ZHANG Xueliang

    2017-01-01

    Supervised speech separation uses supervised learning algorithms to learn a mapping from an input noisy signal to an output target. With the fast development of deep learning, supervised separation has become the most important direction in speech separation area in recent years. For the supervised algorithm, training target has a significant impact on the performance. Ideal ratio mask is a commonly used training target, which can improve the speech intelligibility and quality of the separate...

  18. 3FGL Demographics Outside the Galactic Plane using Supervised Machine Learning: Pulsar and Dark Matter Subhalo Interpretations

    CERN Document Server

    Mirabal, N; Ferrara, E C; Gonthier, P L; Harding, A K; Sánchez-Conde, M A; Thompson, D J

    2016-01-01

    Nearly 1/3 of the sources listed in the Third Fermi Large Area Telescope (LAT) catalog (3FGL) remain unassociated. It is possible that predicted and even unanticipated gamma-ray source classes are present in these data waiting to be discovered. Taking advantage of the excellent spectral capabilities achieved by the Fermi LAT, we use machine learning classifiers (Random Forest and XGBoost) to pinpoint potentially novel source classes in the unassociated 3FGL sample outside the Galactic plane. Here we report a total of 34 high-confidence Galactic candidates at |b| > 5 degrees. The currently favored standard astrophysical interpretations for these objects are pulsars or low-luminosity globular clusters hosting millisecond pulsars (MSPs). Yet, these objects could also be interpreted as dark matter annihilation taking place in ultra-faint dwarf galaxies or dark matter subhalos. Unfortunately, Fermi LAT spectra are not sufficient to break degeneracies between the different scenarios. Careful visual inspection of ar...

  19. Using Supervised Machine Learning to Classify Real Alerts and Artifact in Online Multi-signal Vital Sign Monitoring Data

    Science.gov (United States)

    Chen, Lujie; Dubrawski, Artur; Wang, Donghan; Fiterau, Madalina; Guillame-Bert, Mathieu; Bose, Eliezer; Kaynar, Ata M.; Wallace, David J.; Guttendorf, Jane; Clermont, Gilles; Pinsky, Michael R.; Hravnak, Marilyn

    2015-01-01

    OBJECTIVE Use machine-learning (ML) algorithms to classify alerts as real or artifacts in online noninvasive vital sign (VS) data streams to reduce alarm fatigue and missed true instability. METHODS Using a 24-bed trauma step-down unit’s non-invasive VS monitoring data (heart rate [HR], respiratory rate [RR], peripheral oximetry [SpO2]) recorded at 1/20Hz, and noninvasive oscillometric blood pressure [BP] less frequently, we partitioned data into training/validation (294 admissions; 22,980 monitoring hours) and test sets (2,057 admissions; 156,177 monitoring hours). Alerts were VS deviations beyond stability thresholds. A four-member expert committee annotated a subset of alerts (576 in training/validation set, 397 in test set) as real or artifact selected by active learning, upon which we trained ML algorithms. The best model was evaluated on alerts in the test set to enact online alert classification as signals evolve over time. MAIN RESULTS The Random Forest model discriminated between real and artifact as the alerts evolved online in the test set with area under the curve (AUC) performance of 0.79 (95% CI 0.67-0.93) for SpO2 at the instant the VS first crossed threshold and increased to 0.87 (95% CI 0.71-0.95) at 3 minutes into the alerting period. BP AUC started at 0.77 (95%CI 0.64-0.95) and increased to 0.87 (95% CI 0.71-0.98), while RR AUC started at 0.85 (95%CI 0.77-0.95) and increased to 0.97 (95% CI 0.94–1.00). HR alerts were too few for model development. CONCLUSIONS ML models can discern clinically relevant SpO2, BP and RR alerts from artifacts in an online monitoring dataset (AUC>0.87). PMID:26992068

  20. Machine-learned pattern identification in olfactory subtest results

    Science.gov (United States)

    Lötsch, Jörn; Hummel, Thomas; Ultsch, Alfred

    2016-01-01

    The human sense of smell is often analyzed as being composed of three main components comprising olfactory threshold, odor discrimination and the ability to identify odors. A relevant distinction of the three components and their differential changes in distinct disorders remains a research focus. The present data-driven analysis aimed at establishing a cluster structure in the pattern of olfactory subtest results. Therefore, unsupervised machine-learning was applied onto olfactory subtest results acquired in 10,714 subjects with nine different olfactory pathologies. Using the U-matrix, Emergent Self-organizing feature maps (ESOM) identified three different clusters characterized by (i) low threshold and good discrimination and identification, (ii) very high threshold associated with absent to poor discrimination and identification ability, or (iii) medium threshold, i.e., in the mid-range of possible thresholds, associated with reduced discrimination and identification ability. Specific etiologies of olfactory (dys)function were unequally represented in the clusters (p pattern recognition. PMID:27762302

  1. 3FGL Demographics Outside the Galactic Plane using Supervised Machine Learning: Pulsar and Dark Matter Subhalo Interpretations

    Science.gov (United States)

    Mirabal, N.; Charles, E.; Ferrara, E. C.; Gonthier, P. L.; Harding, A. K.; Sánchez-Conde, M. A.; Thompson, D. J.

    2016-07-01

    Nearly one-third of the sources listed in the Third Fermi Large Area Telescope (LAT) catalog (3FGL) remain unassociated. It is possible that predicted and even unanticipated gamma-ray source classes are present in these data waiting to be discovered. Taking advantage of the excellent spectral capabilities achieved by the Fermi LAT, we use machine-learning classifiers (Random Forest and XGBoost) to pinpoint potentially novel source classes in the unassociated 3FGL sample outside the Galactic plane. Here we report a total of 34 high-confidence Galactic candidates at | b| ≥slant 5^\\circ . The currently favored standard astrophysical interpretations for these objects are pulsars or low-luminosity globular clusters hosting millisecond pulsars (MSPs). Yet these objects could also be interpreted as dark matter annihilation taking place in ultra-faint dwarf galaxies or dark matter subhalos. Unfortunately, Fermi LAT spectra are not sufficient to break degeneracies between the different scenarios. Careful visual inspection of archival optical images reveals no obvious evidence for low-luminosity globular clusters or ultra-faint dwarf galaxies inside the 95% error ellipses. If these are pulsars, this would bring the total number of MSPs at | b| ≥slant 5^\\circ to 106, down to an energy flux ≈4.0 × 10-12 erg cm-2 s-1 between 100 MeV and 100 GeV. We find this number to be in excellent agreement with predictions from a new population synthesis of MSPs that predicts 100-126 high-latitude 3FGL MSPs depending on the choice of high-energy emission model. If, however, these are dark matter substructures, we can place upper limits on the number of Galactic subhalos surviving today and on dark matter annihilation cross sections. These limits are beginning to approach the canonical thermal relic cross section for dark matter particle masses below ˜100 GeV in the bottom quark (b\\bar{b}) annihilation channel.

  2. Identifying learning patterns of children at risk for Specific Reading Disability.

    Science.gov (United States)

    Barbot, Baptiste; Krivulskaya, Suzanna; Hein, Sascha; Reich, Jodi; Thuma, Philip E; Grigorenko, Elena L

    2016-05-01

    Differences in learning patterns of vocabulary acquisition in children at risk (+SRD) and not at risk (-SRD) for Specific Reading Disability (SRD) were examined using a microdevelopmental paradigm applied to the multi-trial Foreign Language Learning Task (FLLT; Baddeley et al., 1995). The FLLT was administered to 905 children from rural Chitonga-speaking Zambia. A multi-group Latent Growth Curve Model (LGCM) was implemented to study interindividual differences in intraindividual change across trials. Results showed that the +SRD group recalled fewer words correctly in the first trial, learned at a slower rate during the subsequent trials, and demonstrated a more linear learning pattern compared to the -SRD group. This study illustrates the promise of LGCM applied to multi-trial learning tasks, by isolating three components of the learning process (initial recall, rate of learning, and functional pattern of learning). Implications of this microdevelopmental approach to SRD research in low-to-middle income countries are discussed.

  3. Clinical supervision training across contexts.

    Science.gov (United States)

    Tai, Joanna; Bearman, Margaret; Edouard, Vicki; Kent, Fiona; Nestel, Debra; Molloy, Elizabeth

    2016-08-01

    Clinicians require specific skills to teach or supervise students in the workplace; however, there are barriers to accessing faculty member development, such as time, cost and suitability. The Clinical Supervision Support Across Contexts (ClinSSAC) programme was designed to provide accessible interprofessional educator training to clinical supervisors across a wide range of clinical settings. In Australia there are increasing numbers of health care students, creating pressure on existing placements. Students are now increasingly learning in community settings, where clinicians have traditionally had less access to faculty member development. An interprofessional team collaborated in the development and implementation of ClinSSAC. A total of 978 clinicians participated in a face-to-face, interactive, introductory module to clinical supervision; 672 people accessed the equivalent online core module, with 23 per cent completing all activities. Additional profession-and discipline-specific modules were also developed. Formal project evaluation found that most participants rated the workshops as helpful or very helpful for their roles as clinical supervisors. Interdisciplinary learning from the workshops was reported to enable cross-discipline supervision. Large participant numbers and favourable ratings indicate a continuing need for basic training in education. Key factors to workshop success included expert facilitators, the interprofessional context and interactive model. The online modules were an important adjunct, and provided context-specific resources, but the low online completion rate suggests protected face-to-face time for faculty member development is still required. Programmes such as ClinSSAC have the capacity to promote interprofessional education and practice. There are barriers to accessing faculty member development, such as time, cost and suitability. © 2015 John Wiley & Sons Ltd.

  4. VDES J2325-5229 a z = 2.7 gravitationally lensed quasar discovered using morphology-independent supervised machine learning

    Science.gov (United States)

    Ostrovski, Fernanda; McMahon, Richard G.; 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; Carnero Rosell, Aurelio; Carrasco Kind, Matias; 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; Plazas Malagón, Andrés; 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.

    2017-03-01

    We present the discovery and preliminary characterization of a gravitationally lensed quasar with a source redshift zs = 2.74 and image separation of 2.9 arcsec lensed by a foreground zl = 0.40 elliptical galaxy. Since optical observations of gravitationally lensed quasars show the lens system as a 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 iAB = 18.61 and iAB = 20.44 comprising 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 a lensed quasar with an emission line redshift of z = 2.739 ± 0.003 and a foreground early-type galaxy with z = 0.400 ± 0.002. We model the system as a single isothermal ellipsoid and find the Einstein radius θE ∼ 1.47 arcsec, enclosed mass Menc ∼ 4 × 1011 M⊙ and a time delay of ∼52 d. The relatively wide separation, month scale time delay duration and high redshift make this an ideal system for constraining the expansion rate beyond a redshift of 1.

  5. Semi-Supervised Multi-View Learning in Big Data%半监督多视图学习在大数据分析中的应用探讨

    Institute of Scientific and Technical Information of China (English)

    蓝超; 饶泓; 浣军

    2015-01-01

    半监督多视图学习是机器学习领域一种极具潜力的大数据处理和分析方法,该方法能有效处理异构和半监督数据,并能方便地在线化和并行化,适合处理海量数据.该方法在大数据时代的应用前景值得研究人员和业界关注.指出未来需要通过引入其他领域新的研究技术和成果,不断丰富和完善半监督多视图学习的理论体系和算法设计,并在实验和实践中不断检验和探索.%This paper introduces a promising machine-learning paradigm cal ed semi-supervised multi-view learning. With this paradigm, information is extracted from heterogeneous and semi-supervised data sets. Lately, multi-view learning has been scaled up online and through paral elization to deal with emerging big data chal enges. Due to its successful application in many research domains and the fact that it has been explored and used by leading companies, multi-view learning may have a future in the big-data era as a major data analytic technique. New research techniques should be introduced into this area to improve the theoretical system and algorithm design of semi-supervised multi-view learning.

  6. Projected estimators for robust semi-supervised classification

    DEFF Research Database (Denmark)

    Krijthe, Jesse H.; Loog, Marco

    2017-01-01

    For semi-supervised techniques to be applied safely in practice we at least want methods to outperform their supervised counterparts. We study this question for classification using the well-known quadratic surrogate loss function. Unlike other approaches to semi-supervised learning, the procedure...... proposed in this work does not rely on assumptions that are not intrinsic to the classifier at hand. Using a projection of the supervised estimate onto a set of constraints imposed by the unlabeled data, we find we can safely improve over the supervised solution in terms of this quadratic loss. More...... specifically, we prove that, measured on the labeled and unlabeled training data, this semi-supervised procedure never gives a lower quadratic loss than the supervised alternative. To our knowledge this is the first approach that offers such strong, albeit conservative, guarantees for improvement over...

  7. Movement Pattern and Parameter Learning in Children: Effects of Feedback Frequency

    Science.gov (United States)

    Goh, Hui-Ting; Kantak, Shailesh S.; Sullivan, Katherine J.

    2012-01-01

    Reduced feedback during practice has been shown to be detrimental to movement accuracy in children but not in young adults. We hypothesized that the reduced accuracy is attributable to reduced movement parameter learning, but not pattern learning, in children. A rapid arm movement task that required the acquisition of a motor pattern scaled to…

  8. Using Pattern Languages to Mediate Theory-Praxis Conversations in Design for Networked Learning

    Science.gov (United States)

    Goodyear, Peter; de Laat, Maarten; Lally, Vic

    2006-01-01

    Educational design for networked learning is becoming more complex but also more inclusive, with teachers and learners playing more active roles in the design of tasks and of the learning environment. This paper connects emerging research on the use of design patterns and pattern languages with a conception of educational design as a conversation…

  9. Networks of Professional Supervision

    Science.gov (United States)

    Annan, Jean; Ryba, Ken

    2013-01-01

    An ecological analysis of the supervisory activity of 31 New Zealand school psychologists examined simultaneously the theories of school psychology, supervision practices, and the contextual qualities that mediated participants' supervisory actions. The findings indicated that the school psychologists worked to achieve the supervision goals of…

  10. Forskellighed i supervision

    DEFF Research Database (Denmark)

    Petersen, Birgitte; Beck, Emma

    2009-01-01

    Indtryk og tendenser fra den anden danske konference om supervision, som blev holdt på Københavns Universitet i oktober 2008......Indtryk og tendenser fra den anden danske konference om supervision, som blev holdt på Københavns Universitet i oktober 2008...

  11. Experiments in Virtual Supervision.

    Science.gov (United States)

    Walker, Rob

    This paper examines the use of First Class conferencing software to create a virtual culture among research students and as a vehicle for supervision and advising. Topics discussed include: computer-mediated communication and research; entry to cyberculture, i.e., research students' induction into the research community; supervision and the…

  12. Patterns in Clinical Students' Self-Regulated Learning Behavior: A Q-Methodology Study

    Science.gov (United States)

    Berkhout, Joris J.; Teunissen, Pim W.; Helmich, Esther; van Exel, Job; van der Vleuten, Cees P.; Jaarsma, Debbie A.

    2017-01-01

    Students feel insufficiently supported in clinical environments to engage in active learning and achieve a high level of self-regulation. As a result clinical learning is highly demanding for students. Because of large differences between students, supervisors may not know how to support them in their learning process. We explored patterns in…

  13. Patterns in Clinical Students' Self-Regulated Learning Behavior: A Q-Methodology Study

    Science.gov (United States)

    Berkhout, Joris J.; Teunissen, Pim W.; Helmich, Esther; van Exel, Job; van der Vleuten, Cees P.; Jaarsma, Debbie A.

    2017-01-01

    Students feel insufficiently supported in clinical environments to engage in active learning and achieve a high level of self-regulation. As a result clinical learning is highly demanding for students. Because of large differences between students, supervisors may not know how to support them in their learning process. We explored patterns in…

  14. Differential effects of a long teacher training internship on students’ learning-to-teach patterns

    NARCIS (Netherlands)

    Donche, Vincent; Endedijk, Maaike Dorine; van Daal, Tine

    2015-01-01

    To become a lifelong learner as a teacher, student teachers already have to learn how to direct their own learning during initial teacher education programmes. Previous empirical research has shown that student teachers differ in their patterns of learning-to-teach, but few is known about the

  15. Patterns of Learning. New Perspectives on Life-Span Education.

    Science.gov (United States)

    Houle, Cyril O.

    Basic methods of learning, most of which have been used through centuries of recorded thought, are discussed, along with learning as a lifelong process, and ways to enhance and diversify modern education. Numerous learning processes are studied by examining the lives of great individuals who have exemplified innovative and multifaceted approaches…

  16. Robust central pattern generators for embodied hierarchical reinforcement learning

    NARCIS (Netherlands)

    Snel, M.; Whiteson, S.; Kuniyoshi, Y.

    2011-01-01

    Hierarchical organization of behavior and learning is widespread in animals and robots, among others to facilitate dealing with multiple tasks. In hierarchical reinforcement learning, agents usually have to learn to recombine or modulate low-level behaviors when facing a new task, which costs time t

  17. COLLAGE: A Collaborative Learning Design Editor Based on Patterns

    Science.gov (United States)

    Hernandez-Leo, Davinia; Villasclaras-Fernandez, Eloy D.; Asensio-Perez, Juan I.; Dimitriadis, Yannis; Jorrin-Abellan, Ivan M.; Ruiz-Requies, Ines; Rubia-Avi, Bartolome

    2006-01-01

    This paper introduces "Collage", a high-level IMS-LD compliant authoring tool that is specialized for CSCL (Computer-Supported Collaborative Learning). Nowadays CSCL is a key trend in e-learning since it highlights the importance of social interactions as an essential element of learning. CSCL is an interdisciplinary domain, which…

  18. Supervision som undervisningsform i voksenspecialundervisningen

    DEFF Research Database (Denmark)

    Kristensen, René

    2000-01-01

    Supervision som undervisningsform i voksenspecialundervisningen. Procesarbejde i undervisning af voksne.......Supervision som undervisningsform i voksenspecialundervisningen. Procesarbejde i undervisning af voksne....

  19. A Process Model for Developing Learning Design Patterns with International Scope

    Science.gov (United States)

    Lotz, Nicole; Law, Effie Lai-Chong; Nguyen-Ngoc, Anh Vu

    2014-01-01

    This paper investigates the process of identifying design patterns in international collaborative learning environments. In this context, design patterns are referred to as structured descriptions of best practice with pre-defined sections such as problem, solution and consequences. We pay special attention to how the scope of a design pattern is…

  20. A Process Model for Developing Learning Design Patterns with International Scope

    Science.gov (United States)

    Lotz, Nicole; Law, Effie Lai-Chong; Nguyen-Ngoc, Anh Vu

    2014-01-01

    This paper investigates the process of identifying design patterns in international collaborative learning environments. In this context, design patterns are referred to as structured descriptions of best practice with pre-defined sections such as problem, solution and consequences. We pay special attention to how the scope of a design pattern is…

  1. Training the Brain or Tending a Garden? Students' Metaphors of Learning Predict Self-Reported Learning Patterns

    Science.gov (United States)

    Wegner, Elisabeth; Nückles, Matthias

    2015-01-01

    Conceptions of learning are seen as an important factor in shaping students' patterns of learning. However, conceptions are often implicit and difficult to assess. Metaphors have been proposed as a method to assess conceptions, because metaphors are closely linked to the conceptual system. Therefore, in our study we assessed which conceptions of…

  2. Attribute Extraction of Chinese Online Encyclopedia Based on Weakly Supervised Learning%基于弱监督学习的中文百科数据属性抽取

    Institute of Scientific and Technical Information of China (English)

    贾真; 杨燕; 何大可

    2014-01-01

    提出基于弱监督学习的属性抽取方法,利用知识库中已有结构化的属性信息自动获取训练语料,有效解决了训练语料不足问题。针对训练语料存在的噪声问题,提出基于关键词过滤的训练语料优化方法。提出n元模式特征提取方法,该特征能够缓解传统n-gram特征稀疏性问题。实验数据源来自互动百科,从互动百科信息盒中抽取结构化属性信息构建知识库,从百科条目文本中自动获取训练数据和测试数据。实验结果表明,关键词过滤能有效提高训练语料的质量,与传统n-gram特征相比,n元模式特征能够提高属性抽取的性能。%An attribute extraction method based on weakly supervised learning is proposed in the paper. The training corpus is automatically acquired from natural language texts by using structured attribute information from knowledgebase. To solve the problem that noise exists in the training corpus, an optimization method based on keywords filtering is proposed.N-pattern features extraction method is proposed which can relieve to some extent the data sparsity problem of traditionaln-gram features. Experiment data are downloaded from Hudong Baike. Structured attribute information is extracted from infoboxes of Hudong Baike and used to construct knowledgebase. Training data and testing data are acquired from encyclopedia entry texts. Experiment results show that the method of keywords filtering can effectively improve the quality of training corpus, and achieve better performance of attribute extraction by usingn-pattern features, compared with traditionaln-gram features.

  3. 脑机接口中基于MRP的半监督判决空间模式法%Semi-Supervised Discriminative Spatial Patterns Based on MRP for Brain-Computer Interfaces

    Institute of Scientific and Technical Information of China (English)

    吕俊

    2011-01-01

    In the study of brain-computer interface,if the number of training samples is small,the features of movement related potentials can not be well extracted by discriminative spatial pattern algorithm.Thus in this paper,semi-supervised self-training scheme i%在脑-机接口研究中,如果训练样本少,判决空间模式法不能很好地提取运动相关电位特征。为此,文中在半监督框架下,采用自训练方法,引入分类置信度高的无标记样本,迭代学习MRP的空间判决模式。实验结果验证了所提算法的有效性。

  4. On psychoanalytic supervision as signature pedagogy.

    Science.gov (United States)

    Watkins, C Edward

    2014-04-01

    What is signature pedagogy in psychoanalytic education? This paper examines that question, considering why psychoanalytic supervision best deserves that designation. In focusing on supervision as signature pedagogy, I accentuate its role in building psychoanalytic habits of mind, habits of hand, and habits of heart, and transforming theory and self-knowledge into practical product. Other facets of supervision as signature pedagogy addressed in this paper include its features of engagement, uncertainty, formation, and pervasiveness, as well as levels of surface, deep, and implicit structure. Epistemological, ontological, and axiological in nature, psychoanalytic supervision engages trainees in learning to do, think, and value what psychoanalytic practitioners in the field do, think, and value: It is, most fundamentally, professional preparation for competent, "good work." In this paper, effort is made to shine a light on and celebrate the pivotal role of supervision in "making" or developing budding psychoanalysts and psychoanalytic psychotherapists. Now over a century old, psychoanalytic supervision remains unparalleled in (1) connecting and integrating conceptualization and practice, (2) transforming psychoanalytic theory and self-knowledge into an informed analyzing instrument, and (3) teaching, transmitting, and perpetuating the traditions, practice, and culture of psychoanalytic treatment.

  5. Discussion on Funds Supervision Pattern of Freeway Construction%浅探高速公路建设资金监管模式

    Institute of Scientific and Technical Information of China (English)

    张凌

    2012-01-01

    In recent years, the freeway in China has a rapid development, and the freeway network is gradually formed. Because of the long construction period and huge investment of freeway project, some times, the construction unit may divert the funds of freeway project construction for other ways, which may cause large wage arrears for migrant rural workers, delay duration, and affect the project quality and progress. Therefore, it is very necessary and important to strengthen the supervision of project funds for construction unit.%近年来,我国高速公路快速发展,高速公路网逐步形成.高速公路项目建设期长,投资巨大,施工单位挪用高速公路项目建设资金、用于他途时有发生,容易造成拖欠农民工工资、延误工期、影响工程质量和进度.因此,加强对施工单位的项目建设资金监管,是非常必要和重要的.

  6. Supporting Placement Supervision in Clinical Exercise Physiology

    Science.gov (United States)

    Sealey, Rebecca M.; Raymond, Jacqueline; Groeller, Herb; Rooney, Kieron; Crabb, Meagan; Watt, Kerrianne

    2015-01-01

    The continued engagement of the professional workforce as supervisors is critical for the sustainability and growth of work-integrated learning activities in university degrees. This study investigated factors that influence the willingness and ability of clinicians to continue to supervise clinical exercise physiology work-integrated learning…

  7. Remote Video Supervision in Adapted Physical Education

    Science.gov (United States)

    Kelly, Luke; Bishop, Jason

    2013-01-01

    Supervision for beginning adapted physical education (APE) teachers and inservice general physical education teachers who are learning to work with students with disabilities poses a number of challenges. The purpose of this article is to describe a project aimed at developing a remote video system that could be used by a university supervisor to…

  8. On Training Targets for Supervised Speech Separation

    Science.gov (United States)

    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 use of binary targets. In this study, we evaluate and compare separation results by using different training targets, including the IBM, the target binary mask, the ideal ratio mask (IRM), the short-time Fourier transform spectral magnitude and its corresponding mask (FFT-MASK), and the Gammatone frequency power spectrum. Our results in various test conditions reveal that the two ratio mask targets, the IRM and the FFT-MASK, outperform the other targets in terms of objective intelligibility and quality metrics. In addition, we find that masking based targets, in general, are significantly better than spectral envelope based targets. We also present comparisons with recent methods in non-negative matrix factorization and speech enhancement, which show clear performance advantages of supervised speech separation. PMID:25599083

  9. Clinical Supervision in Denmark

    DEFF Research Database (Denmark)

    Jacobsen, Claus Haugaard

    Data fra den danske undersøgelse af psykoterapeuters faglige udvikling indsamlet ved hjælp af DPCCQ. Oplægget fokuserer på supervision (modtaget, givet, uddannelse i) blandt danske psykoterapeutiske arbejdende psykologer....

  10. Supervision af psykoterapi

    DEFF Research Database (Denmark)

    SUPERVISION AF PSYKOTERAPI indtager en central position i uddannelsen og udviklingen af psykoterapeuter. Trods flere lighedspunkter med psykoterapi, undervisning og konsultation er psykoterapisupervision et selvstændigt virksomhedsområde. Supervisor må foruden at være en trænet psykoterapeut kende...... supervisionens rammer og indplacering i forhold til organisation og samfund. En række kapitler drejer sig om supervisors opgaver, roller og kontrolfunktion, supervision set fra supervisandens perspektiv samt betragtninger over relationer og processer i supervision. Der drøftes fordele og ulemper ved de...... forskellige måder, hvorpå en sag kan fremlægges. Bogens første del afsluttes med refleksioner over de etiske aspekter ved psykoterapisupervision. Bogens anden del handler om de særlige forhold, der gør sig gældende ved supervision af en række specialiserede behandlingsformer eller af psykoterapi med bestemte...

  11. Psykoterapi og supervision

    DEFF Research Database (Denmark)

    Jacobsen, Claus Haugaard

    2014-01-01

    Kapitlet beskriver supervisionen funktioner i forhold til psykoterapi. Supervision af psykoterapi henviser i almindelighed til, at en psykoterapeut konsulterer en ofte mere erfaren kollega (supervisor) med henblik på drøftelse af et konkret igangværende psykoterapeutisk behandlingsforløb. Formålet...... er at fremme denne fagpersons (psykoterapeutens) faglige udvikling samt sikre kvaliteten af behandlingen.kan defineres som i. Der redegøres for, hvorfor supervision er vigtig del af psykoterapeutens profession samt vises, hvorledes supervision foruden den faglige udvikling også er vigtigt redskab i...... psykoterapiens kvalitetssikring. Efter at have drøftet nogle etiske forhold ved supervision, fremlægges endelig nogle få forskningsresultater vedr. psykoterapisupervision af danske psykologer....

  12. Supervision and group dynamics

    DEFF Research Database (Denmark)

    Hansen, Søren; Jensen, Lars Peter

    2004-01-01

    as well as at Aalborg University. The first visible result has been participating supervisors telling us that the course has inspired them to try supervising group dynamics in the future. This paper will explore some aspects of supervising group dynamics as well as, how to develop the Aalborg model...... An important aspect of the problem based and project organized study at Aalborg University is the supervision of the project groups. At the basic education (first year) it is stated in the curriculum that part of the supervisors' job is to deal with group dynamics. This is due to the experience...... that many students are having difficulties with practical issues such as collaboration, communication, and project management. Most supervisors either ignore this demand, because they do not find it important or they find it frustrating, because they do not know, how to supervise group dynamics...

  13. Image Binarization Using Multi-Layer Perceptron: A Semi-Supervised Approach

    Directory of Open Access Journals (Sweden)

    Amlan Raychaudhuri

    2012-04-01

    Full Text Available In this paper, we have discussed the Image Binarization technique using Multilayer Perceptron (MLP. The purpose of Image Binarization is to extract the lightness (brightness, density as a feature amount from the Image. It converts a gray-scale image of up to 256 gray levels to a black and white image. We use Backpropagation algorithm for training MLP. It is a supervised learning technique. Here Kmeans clustering algorithm has been used for clustering a 256 × 256 gray-level image. The dataset obtained by this is fed to the MLP and processed in a Semi-Supervised way where some training samples are taken as Known patterns (for training and others as Unknown patterns. Finally through this approach a Binarized image is produced.

  14. Entry-Level Technical Skills that Agricultural Industry Experts Expected Students to Learn through Their Supervised Agricultural Experiences: A Modified Delphi Study

    Science.gov (United States)

    Ramsey, Jon W.; Edwards, M. Craig

    2011-01-01

    The National Research Council's (NRC) Report (1988), Understanding Agriculture: New Directions for Education, called on secondary agricultural education to shift its scope and purpose, including students' supervised agricultural experiences (SAEs). The NRC asserted that this shift should create opportunities for students to acquire supervised…

  15. Living with diabetes—Development of learning patterns over a 3-year period

    Directory of Open Access Journals (Sweden)

    Åsa Kneck

    2014-07-01

    Full Text Available Background: Learning involves acquiring new knowledge and skills, and changing our ways of thinking, acting, and feeling. Learning in relation to living with diabetes is a lifelong process where there is limited knowledge of how it is experienced and established over time. It was considered important to explore how learning was developed over time for persons living with diabetes. Aim: The aim of the study was to identify patterns in learning when living with diabetes, from recently being diagnosed, and over a 3-year period. Materials and methods: A longitudinal qualitative descriptive design was used. Thirteen participants, with both type I and type II diabetes, were interviewed at three different occasions during a 3-year period. Qualitative content analysis was used in different steps in order to distinguish patterns. Findings: Five main patterns of learning were identified. Two of the patterns (I and II were characterized by gradually becoming comfortable living with diabetes, whereas for one pattern (IV living with diabetes became gradually more difficult. For pattern V living with diabetes was making only a limited impact on life, whereas for Pattern III there was a constant management of obstacles related to illness. The different patterns in the present study showed common and different ways of learning and using different learning strategies at different timespans. Conclusion: The present study showed that duration of illness is not of importance for how far a person has come in his own learning process. A person-centered care is needed to meet the different and changing needs of persons living with diabetes in relation to learning to live with a lifelong illness.

  16. Learning a new bimanual coordination pattern: interlimb interactions, attentional focus, and transfer.

    Science.gov (United States)

    de Boer, Betteco J; Peper, C Lieke E; Beek, Peter J

    2013-01-01

    Because bimanual coordinative stability is governed by interlimb coupling, we examined how learning a new pattern (90°) was reflected in changes in the underlying interlimb interactions. Three interlimb interaction sources were distinguished: integrated timing of feedforward control signals, error corrections based on perceived relative phase, and phase entrainment by contralateral afference. By comparing 4 tasks that involve these interactions to a different extent, changes in the stabilizing contributions of these coupling sources could be studied. Furthermore, we studied how the learning process and changes in the underlying interactions were influenced by attentional focus (internal vs. external), and we examined retention of the learned pattern and transfer to the mirror-symmetrical pattern (270°). Results showed that stability and accuracy of the new pattern increased significantly with learning, due to improved integrated timing and error correction. Integrated timing improved first, possibly providing a reference frame for the error corrections that subsequently became more effective. Despite some qualitative differences in the learning process, neither performance of the learned pattern nor the underlying interlimb interactions was influenced by attentional focus. Whereas the learned pattern improved directly after practice, transfer followed later, suggesting that a more general representation was formed at a slower rate after practice.

  17. Supervised Discrete Hashing With Relaxation.

    Science.gov (United States)

    Gui, Jie; Liu, Tongliang; Sun, Zhenan; Tao, Dacheng; Tan, Tieniu

    2016-12-29

    Data-dependent hashing has recently attracted attention due to being able to support efficient retrieval and storage of high-dimensional data, such as documents, images, and videos. In this paper, we propose a novel learning-based hashing method called ''supervised discrete hashing with relaxation'' (SDHR) based on ''supervised discrete hashing'' (SDH). SDH uses ordinary least squares regression and traditional zero-one matrix encoding of class label information as the regression target (code words), thus fixing the regression target. In SDHR, the regression target is instead optimized. The optimized regression target matrix satisfies a large margin constraint for correct classification of each example. Compared with SDH, which uses the traditional zero-one matrix, SDHR utilizes the learned regression target matrix and, therefore, more accurately measures the classification error of the regression model and is more flexible. As expected, SDHR generally outperforms SDH. Experimental results on two large-scale image data sets (CIFAR-10 and MNIST) and a large-scale and challenging face data set (FRGC) demonstrate the effectiveness and efficiency of SDHR.

  18. Counselor Supervision: A Consumer's Guide.

    Science.gov (United States)

    Yager, Geoffrey G.; Littrell, John M.

    This guide attempts to solve problems caused when a certain designated "brand" of supervision is forced on the counselor trainee with neither choice nor checklist of important criteria. As a tentative start on a guide to supervision the paper offers the following: a definition of supervision; a summary of the various types of supervision; a…

  19. Towards a Pattern Language for Learning Management Systems

    NARCIS (Netherlands)

    Avgeriou, Paris; Papasalouros, Andreas; Retalis, Symeon; Skordalakis, Manolis

    2003-01-01

    Learning Management Systems are sophisticated web-based applications that are being engineered today in increasing numbers by numerous institutions and companies that want to get involved in e-learning either for providing services to third parties, or for educating and training their own people. Ev

  20. Tuning Primary Learning Style for Children with Secondary Behavioral Patterns

    Science.gov (United States)

    Mosharraf, Maedeh

    2016-01-01

    Personalization is one of the most expected features in the current educational systems. User modeling is supposed to be the first stage of this process, which may incorporate learning style as an important part of the model. Learning style, which is a non-stable characteristic in the case of children, differentiates students in learning…

  1. Tuning Primary Learning Style for Children with Secondary Behavioral Patterns

    Science.gov (United States)

    Mosharraf, Maedeh

    2016-01-01

    Personalization is one of the most expected features in the current educational systems. User modeling is supposed to be the first stage of this process, which may incorporate learning style as an important part of the model. Learning style, which is a non-stable characteristic in the case of children, differentiates students in learning…

  2. African Journal of Science and Technology (AJST) SUPERVISED ...

    African Journals Online (AJOL)

    NORBERT OPIYO AKECH

    ABSTRACT: TThis paper proposes a new method for supervised color image classification by the ... learning quantisation vector (LVQ), is constructed and compared to the K-means clustering ..... colored scanned maps, Machine Vision and.

  3. Stimulation of the human motor cortex alters generalization patterns of motor learning

    OpenAIRE

    Orban de Xivry, Jean-Jacques; Marko, Mollie K; Pekny, Sarah E.; Pastor, Damien; Izawa, Jun; Celnik, Pablo; Shadmehr, Reza

    2011-01-01

    It has been hypothesized that the generalization patterns that accompany learning carry the signatures of the neural systems that are engaged in that learning. Reach adaptation in force fields has generalization patterns that suggest primary engagement of a neural system that encodes movements in the intrinsic coordinates of joints and muscles, and lesser engagement of a neural system that encodes movements in the extrinsic coordinates of the task. Among the cortical motor areas, the intrinsi...

  4. Instruction in learning a temporal pattern on an anticipation-coincidence task.

    Science.gov (United States)

    Albinet, C; Fezzani, K

    2003-08-01

    Using a computer-simulated anticipation-coincidence task, the main aim of the study was to examine the effect of the type of instruction on learning a temporal pattern. For this task, participants must learn to anticipate the appropriate time to launch a projectile to hit a moving target. The experiment involved three instructional conditions. In the Explicit-rule Discover Instruction Condition participants were informed that target speed could change from trial to trial and that change is controlled by a regular pattern. Their task was then to search, to identify, and to use such pattern to enhance their anticipation. In the Explicit-Informative Instruction Condition, participants were, however, allowed before practice to examine attentively the regular pattern. Participants were also explicitly urged to use the pattern they observed to ensure a better interception of the target. Finally, in the Implicit Instruction Condition, participants were only informed that their task was to hit, or at least, to place the projectile as near as possible to the target. No additional information was provied about the target's behaviour. Analysis indicated that learning the temporal pattern was more important in Implicit than in Explicit-rule Discover Instruction Condion. However, the Explicit-Informative Instruction Condition produced unambiguouslly the highest learning. Overall, the study highlights the role of information over guidance in the understanding of the effect of the instructions on learning. Finally, we discussed the implications of these results on the comprehension of the variability of the effects of the instruction on learning.

  5. Theory of Multiple Intelligences at Teacher Supervision

    Directory of Open Access Journals (Sweden)

    İzzet Döş

    2012-07-01

    Full Text Available This study aims to determine views of teachers and supervisors related to the multiple intelligences in students’ learning that they took into consideration in the evaluation of teachers during lesson supervision. The study was conducted with 5 supervisors who work at Kahramanmaraş provincial directorate of national education and 10 teachers who work at primary schools in the centre of Kahramanmaraş in 2011-2012 year. Data was gathered with the help of interview form consisting of five open-ended questions. In the analysis of the data content analysis which is one of the qualitative research methods. According to the results of the analysis, it has been found that usage of multiple intelligences theory in the evaluation students’ learning during supervision enabled them to evaluate students’ learning in a more detailed way. It also made it possible for the supervisors to examine supervision evaluations at different levels. It was also mentioned that supervisions made according to multiple intelligence theory has some limitations.

  6. A Cross-Cultural Comparison of Student Learning Patterns in Higher Education

    Science.gov (United States)

    Marambe, Kosala N.; Vermunt, Jan D.; Boshuizen, Henny P. A.

    2012-01-01

    The aim of this study was to compare student learning patterns in higher education across different cultures. A meta-analysis was performed on three large-scale studies that had used the same research instrument: the Inventory of learning Styles (ILS). The studies were conducted in the two Asian countries Sri Lanka and Indonesia and the European…

  7. Allowing Learners to Choose: Self-Controlled Practice Schedules for Learning Multiple Movement Patterns

    Science.gov (United States)

    Wu, Will F. W.; Magill, Richard A.

    2011-01-01

    For this study, we investigated the effects of self-controlled practice on learning multiple motor skills. Thirty participants were randomly assigned to self-control or yoked conditions. Participants learned a three-keystroke pattern with three different relative time structures. Those in the self-control group chose one of three relative time…

  8. Exploring Elementary-School Students' Engagement Patterns in a Game-Based Learning Environment

    Science.gov (United States)

    Hsieh, Ya-Hui; Lin, Yi-Chun; Hou, Huei-Tse

    2015-01-01

    Unlike most research, which has primarily examined the players' interest in or attitude toward game-based learning through questionnaires, the purpose of this empirical study is to explore students' engagement patterns by qualitative observation and sequential analysis to visualize and better understand their game-based learning process. We…

  9. Identifying Learning Patterns of Children at Risk for Specific Reading Disability

    Science.gov (United States)

    Barbot, Baptiste; Krivulskaya, Suzanna; Hein, Sascha; Reich, Jodi; Thuma, Philip E.; Grigorenko, Elena L.

    2016-01-01

    Differences in learning patterns of vocabulary acquisition in children at risk (+SRD) and not at risk (-SRD) for Specific Reading Disability (SRD) were examined using a microdevelopmental paradigm applied to the multi-trial Foreign Language Learning Task (FLLT; Baddeley et al., 1995). The FLLT was administered to 905 children from rural…

  10. Two Group Development Patterns of Virtual Learning Teams: Linear Progression and Adaptive Progression

    Science.gov (United States)

    Yoon, Seung Won

    2006-01-01

    This study examined member behaviors, distribution of performed behaviors, and development-shaping forces in order to identify group development patterns of virtual learning teams. Participants of this study were 7 newly formed virtual learning teams working on a final group project in a 12-week online graduate-level course. Examining the group…

  11. Mobile gaming patterns and their impact on learning outcomes: A literature review

    NARCIS (Netherlands)

    Schmitz, Birgit; Klemke, Roland; Specht, Marcus

    2012-01-01

    Schmitz, B., Klemke, R., & Specht, M. (2012). Mobile gaming patterns and their impact on learning outcomes: A literature review. In A. Ravenscroft, S. Lindstaedt, C. D. Kloos, & D. Hérnandez-Leo (Eds.), Proceedings of 7th European Conference on Technology Enhanced Learning (EC-TEL 2012) (pp. 419-424

  12. Exploring Elementary-School Students' Engagement Patterns in a Game-Based Learning Environment

    Science.gov (United States)

    Hsieh, Ya-Hui; Lin, Yi-Chun; Hou, Huei-Tse

    2015-01-01

    Unlike most research, which has primarily examined the players' interest in or attitude toward game-based learning through questionnaires, the purpose of this empirical study is to explore students' engagement patterns by qualitative observation and sequential analysis to visualize and better understand their game-based learning process. We…

  13. 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...

  14. 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...

  15. [Administrative reform thinking on the regulations on the supervision and administration of medical devices].

    Science.gov (United States)

    Yue, Wei

    2014-09-01

    This paper learned and interpreted the regulations on the supervision and administration of medical devices, carded the thoughts of administrative reform, then put forward the "ten principles", including full supervision, classification supervision, risk classification, safety-effective-save, to encourage innovation, simplified license, scientific-standard, sincerity & self-discipline, clear responsibility, severe punishment, and discussed these priciples.

  16. Pattern and process in the evolution of learning.

    Science.gov (United States)

    Papini, Mauricio R

    2002-01-01

    A century after E. L. Thorndike's (1898) dissertation on the comparative psychology of learning, the field seems ready for a reassessment of its metatheoretical foundations. The stability of learning phenotypes across species is shown to be similar to that of other biological characters, both genotypic (e.g., Hox genes) and phenotypic (e.g., vertebrate brain structure). Moreover, an analysis of some current lines of comparative research indicates that researchers use similar strategies when approaching problems from either an ecological view (emphasizing adaptive significance) or a general-process view (emphasizing commonality across species). An integration of learning and evolution requires the development of criteria for recognizing and studying the divergence, homology, and homoplasy of learning mechanisms, much as it is done in other branches of biological research.

  17. Collage, a Collaborative Learning Design Editor Based on Patterns

    NARCIS (Netherlands)

    Hernández-Leo, Davinia; Villasclaras-Fernández, Eloy; Jorrín-Abellán, Iván; Asensio-Pérez, Juan; Dimitriadis, Yannis; Ruiz-Requies, Inés; Rubia-Avi, Bartolomé

    2006-01-01

    CSCL (Computer-Supported Collaborative Learning) constitutes a significant field that has drawn the attention of many researchers and practitioners (Dillenbourg, 2002). This domain is characterized by the coexistence of very different expectations, requirements, knowledge and interests posed by both

  18. Collage, a Collaborative Learning Design Editor Based on Patterns

    NARCIS (Netherlands)

    Hernández-Leo, Davinia; Villasclaras-Fernández, Eloy; Jorrín-Abellán, Iván; Asensio-Pérez, Juan; Dimitriadis, Yannis; Ruiz-Requies, Inés; Rubia-Avi, Bartolomé

    2006-01-01

    CSCL (Computer-Supported Collaborative Learning) constitutes a significant field that has drawn the attention of many researchers and practitioners (Dillenbourg, 2002). This domain is characterized by the coexistence of very different expectations, requirements, knowledge and interests posed by both

  19. Knowledge Management through the Equilibrium Pattern Model for Learning

    Science.gov (United States)

    Sarirete, Akila; Noble, Elizabeth; Chikh, Azeddine

    Contemporary students are characterized by having very applied learning styles and methods of acquiring knowledge. This behavior is consistent with the constructivist models where students are co-partners in the learning process. In the present work the authors developed a new model of learning based on the constructivist theory coupled with the cognitive development theory of Piaget. The model considers the level of learning based on several stages and the move from one stage to another requires learners' challenge. At each time a new concept is introduced creates a disequilibrium that needs to be worked out to return back to its equilibrium stage. This process of "disequilibrium/equilibrium" has been analyzed and validated using a course in computer networking as part of Cisco Networking Academy Program at Effat College, a women college in Saudi Arabia. The model provides a theoretical foundation for teaching especially in a complex knowledge domain such as engineering and can be used in a knowledge economy.

  20. On Behavior Patterns of College Students'Application to English Learning%大学生行为模式在英语学习中的应用

    Institute of Scientific and Technical Information of China (English)

    赵科研

    2012-01-01

    Studying the distinguish between quantitative and qualitative approaches to English learning, this article discusses the principal learning theories and their application to English learning of specific behavior patterns.

  1. Self-Supervised Dynamical Systems

    Science.gov (United States)

    Zak, Michail

    2003-01-01

    Some progress has been made in a continuing effort to develop mathematical models of the behaviors of multi-agent systems known in biology, economics, and sociology (e.g., systems ranging from single or a few biomolecules to many interacting higher organisms). Living systems can be characterized by nonlinear evolution of probability distributions over different possible choices of the next steps in their motions. One of the main challenges in mathematical modeling of living systems is to distinguish between random walks of purely physical origin (for instance, Brownian motions) and those of biological origin. Following a line of reasoning from prior research, it has been assumed, in the present development, that a biological random walk can be represented by a nonlinear mathematical model that represents coupled mental and motor dynamics incorporating the psychological concept of reflection or self-image. The nonlinear dynamics impart the lifelike ability to behave in ways and to exhibit patterns that depart from thermodynamic equilibrium. Reflection or self-image has traditionally been recognized as a basic element of intelligence. The nonlinear mathematical models of the present development are denoted self-supervised dynamical systems. They include (1) equations of classical dynamics, including random components caused by uncertainties in initial conditions and by Langevin forces, coupled with (2) the corresponding Liouville or Fokker-Planck equations that describe the evolutions of probability densities that represent the uncertainties. The coupling is effected by fictitious information-based forces, denoted supervising forces, composed of probability densities and functionals thereof. The equations of classical mechanics represent motor dynamics that is, dynamics in the traditional sense, signifying Newton s equations of motion. The evolution of the probability densities represents mental dynamics or self-image. Then the interaction between the physical and

  2. Ghosts, Stars, and Learning Online: Analysis of Interaction Patterns in Student Online Discussions

    Science.gov (United States)

    Samuels-Peretz, Debbie

    2014-01-01

    Discussions are commonly used in online teaching and have been shown to foster student learning and collaboration. This case study uses content analysis to explore the interaction patterns of student online discussions during a semester-long teacher preparation course using concepts from sociometry. Findings suggest that interaction patterns were…

  3. Resistance to group clinical supervision

    DEFF Research Database (Denmark)

    Buus, Niels; Delgado, Cynthia; Traynor, Michael

    2017-01-01

    This present study is a report of an interview study exploring personal views on participating in group clinical supervision among mental health nursing staff members who do not participate in supervision. There is a paucity of empirical research on resistance to supervision, which has traditiona......This present study is a report of an interview study exploring personal views on participating in group clinical supervision among mental health nursing staff members who do not participate in supervision. There is a paucity of empirical research on resistance to supervision, which has...... traditionally been theorized as a supervisee's maladaptive coping with anxiety in the supervision process. The aim of the present study was to examine resistance to group clinical supervision by interviewing nurses who did not participate in supervision. In 2015, we conducted semistructured interviews with 24...

  4. 基于半监督学习的Web页面内容分类技术研究%Study on Web page content classification technology based on semi-supervised learning

    Institute of Scientific and Technical Information of China (English)

    赵夫群

    2016-01-01

    For the key issues that how to use labeled and unlabeled data to conduct Web classification,a classifier of com-bining generative model with discriminative model is explored. The maximum likelihood estimation is adopted in the unlabeled training set to construct a semi-supervised classifier with high classification performance. The Dirichlet-polynomial mixed distri-bution is used to model the text,and then a hybrid model which is suitable for the semi-supervised learning is proposed. Since the EM algorithm for the semi-supervised learning has fast convergence rate and is easy to fall into local optimum,two intelli-gent optimization methods of simulated annealing algorithm and genetic algorithm are introduced,analyzed and processed. A new intelligent semi-supervised classification algorithm was generated by combing the two algorithms,and the feasibility of the algorithm was verified.%针对如何使用标记和未标记数据进行Web分类这一关键性问题,探索一种生成模型和判别模型相互结合的分类器,在无标记训练集中采用最大似然估计,构造一种具有良好分类性能的半监督分类器.利用狄利克雷-多项式混合分布对文本进行建模,提出了适用于半监督学习的混合模型.针对半监督学习的EM算法收敛速度过快,容易陷入局部最优的难题,引入两种智能优化的方法——模拟退火算法和遗传算法进行分析和处理,结合这两种算法形成一种新型智能的半监督分类算法,并且验证了该算法的可行性.

  5. Learning causes reorganization of neuronal firing patterns to represent related experiences within a hippocampal schema.

    Science.gov (United States)

    McKenzie, Sam; Robinson, Nick T M; Herrera, Lauren; Churchill, Jordana C; Eichenbaum, Howard

    2013-06-19

    According to schema theory as proposed by Piaget and Bartlett, learning involves the assimilation of new memories into networks of preexisting knowledge, as well as alteration of the original networks to accommodate the new information. Recent evidence has shown that rats form a schema of goal locations and that the hippocampus plays an essential role in adding new memories to the spatial schema. Here we examined the nature of hippocampal contributions to schema updating by monitoring firing patterns of multiple CA1 neurons as rats learned new goal locations in an environment in which there already were multiple goals. Before new learning, many neurons that fired on arrival at one goal location also fired at other goals, whereas ensemble activity patterns also distinguished different goal events, thus constituting a neural representation that linked distinct goals within a spatial schema. During new learning, some neurons began to fire as animals approached the new goals. These were primarily the same neurons that fired at original goals, the activity patterns at new goals were similar to those associated with the original goals, and new learning also produced changes in the preexisting goal-related firing patterns. After learning, activity patterns associated with the new and original goals gradually diverged, such that initial generalization was followed by a prolonged period in which new memories became distinguished within the ensemble representation. These findings support the view that consolidation involves assimilation of new memories into preexisting neural networks that accommodate relationships among new and existing memories.

  6. Ghosts, Stars, and Learning Online: Analysis of Interaction Patterns in Student Online Discussions

    Directory of Open Access Journals (Sweden)

    Debbie Samuels-Peretz

    2014-07-01

    Full Text Available Discussions are commonly used in online teaching and have been shown to foster student learning and collaboration. This case study uses content analysis to explore the interaction patterns of student online discussions during a semester-long teacher preparation course using concepts from sociometry. Findings suggest that interaction patterns were influenced by the content of student posts. Online discussions in this case were found to be an equitable form of collaborative learning, enabling each student to have a voice. There were, however, indications that gendered ways of knowing may play a role in the content of interaction, if not in the patterns themselves.

  7. Analysing Content and Patterns of Interaction for Improving the Learning Design of Networked Learning Environments

    Science.gov (United States)

    Haya, Pablo A.; Daems, Oliver; Malzahn, Nils; Castellanos, Jorge; Hoppe, Heinz Ulrich

    2015-01-01

    Learning Analytics constitutes a key tool for supporting Learning Design and teacher-led inquiry into student learning. In this paper, we demonstrate how a Social Learning Analytics toolkit can combine social network analysis and content analysis for supporting a global and formal teacher inquiry. This toolkit not only supports teachers in…

  8. Analysing Content and Patterns of Interaction for Improving the Learning Design of Networked Learning Environments

    Science.gov (United States)

    Haya, Pablo A.; Daems, Oliver; Malzahn, Nils; Castellanos, Jorge; Hoppe, Heinz Ulrich

    2015-01-01

    Learning Analytics constitutes a key tool for supporting Learning Design and teacher-led inquiry into student learning. In this paper, we demonstrate how a Social Learning Analytics toolkit can combine social network analysis and content analysis for supporting a global and formal teacher inquiry. This toolkit not only supports teachers in…

  9. PatternCoder: A Programming Support Tool for Learning Binary Class Associations and Design Patterns

    Science.gov (United States)

    Paterson, J. H.; Cheng, K. F.; Haddow, J.

    2009-01-01

    PatternCoder is a software tool to aid student understanding of class associations. It has a wizard-based interface which allows students to select an appropriate binary class association or design pattern for a given problem. Java code is then generated which allows students to explore the way in which the class associations are implemented in a…

  10. PatternCoder: A Programming Support Tool for Learning Binary Class Associations and Design Patterns

    Science.gov (United States)

    Paterson, J. H.; Cheng, K. F.; Haddow, J.

    2009-01-01

    PatternCoder is a software tool to aid student understanding of class associations. It has a wizard-based interface which allows students to select an appropriate binary class association or design pattern for a given problem. Java code is then generated which allows students to explore the way in which the class associations are implemented in a…

  11. Telehone consultations: At the emergency service, Copenhagen County: Analysis of doctor-patient communication patterns

    DEFF Research Database (Denmark)

    Larsen, Jan-Helge; Risør, Ole

    1997-01-01

    Communications patterns, emergency service, out-of-hours service, telehpone consultations, video-supervision......Communications patterns, emergency service, out-of-hours service, telehpone consultations, video-supervision...

  12. Content Design Patterns for Game-Based Learning

    Science.gov (United States)

    Maciuszek, Dennis; Ladhoff, Sebastian; Martens, Alke

    2011-01-01

    To address the lack of documented best practices in the development of digital educational games, the authors have previously proposed a reference software architecture. One of its components is the rule system specifying learning and gameplay content. It contains quest, player character, non-player character, environment, and item rules.…

  13. Phases and Patterns of Group Development in Virtual Learning Teams

    Science.gov (United States)

    Yoon, Seung Won; Johnson, Scott D.

    2008-01-01

    With the advancement of Internet communication technologies, distributed work groups have great potential for remote collaboration and use of collective knowledge. Adopting the Complex Adaptive System (CAS) perspective (McGrath, Arrow, & Berdhal, "Personal Soc Psychol Rev" 4 (2000) 95), which views virtual learning teams as an adaptive and…

  14. Machine Learning Method for Pattern Recognition in Volcano Seismic Spectra

    Science.gov (United States)

    Radic, V.; Unglert, K.; Jellinek, M.

    2016-12-01

    Variations in the spectral content of volcano seismicity related to changes in volcanic activity are commonly identified manually in spectrograms. However, long time series of monitoring data at volcano observatories require tools to facilitate automated and rapid processing. Techniques such as Self-Organizing Maps (SOM), Principal Component Analysis (PCA) and clustering methods can help to quickly and automatically identify important patterns related to impending eruptions. In this study we develop and evaluate an algorithm applied on a set of synthetic volcano seismic spectra as well as observed spectra from Kılauea Volcano, Hawai`i. Our goal is to retrieve a set of known spectral patterns that are associated with dominant phases of volcanic tremor before, during, and after periods of volcanic unrest. The algorithm is based on training a SOM on the spectra and then identifying local maxima and minima on the SOM 'topography'. The topography is derived from the first two PCA modes so that the maxima represent the SOM patterns that carry most of the variance in the spectra. Patterns identified in this way reproduce the known set of spectra. Our results show that, regardless of the level of white noise in the spectra, the algorithm can accurately reproduce the characteristic spectral patterns and their occurrence in time. The ability to rapidly classify spectra of volcano seismic data without prior knowledge of the character of the seismicity at a given volcanic system holds great potential for real time or near-real time applications, and thus ultimately for eruption forecasting.

  15. Exploring Learners' Sequential Behavioral Patterns, Flow Experience, and Learning Performance in an Anti-Phishing Educational Game

    Science.gov (United States)

    Sun, Jerry Chih-Yuan; Kuo, Cian-Yu; Hou, Huei-Tse; Lin, Yu-Yan

    2017-01-01

    The purposes of this study were to provide a game-based anti-phishing lesson to 110 elementary school students in Taiwan, explore their learning behavioral patterns, and investigate the effects of the flow states on their learning behavioral patterns and learning achievement. The study recorded behaviour logs, and applied a pre- and post-test on…

  16. Exploring Learners' Sequential Behavioral Patterns, Flow Experience, and Learning Performance in an Anti-Phishing Educational Game

    Science.gov (United States)

    Sun, Jerry Chih-Yuan; Kuo, Cian-Yu; Hou, Huei-Tse; Lin, Yu-Yan

    2017-01-01

    The purposes of this study were to provide a game-based anti-phishing lesson to 110 elementary school students in Taiwan, explore their learning behavioral patterns, and investigate the effects of the flow states on their learning behavioral patterns and learning achievement. The study recorded behaviour logs, and applied a pre- and post-test on…

  17. Reflecting reflection in supervision

    DEFF Research Database (Denmark)

    Lystbæk, Christian Tang

    Reflection has moved from the margins to the mainstream in supervision. Notions of reflection have become well established since the late 1980s. These notions have provided useful framing devices to help conceptualize some important processes in guidance and counseling. However, some applications...

  18. Clinical Supervision in Denmark

    DEFF Research Database (Denmark)

    Jacobsen, Claus Haugaard

    2011-01-01

    on giving and receiving clinical supervision as reported by therapists in Denmark. Method: Currently, the Danish sample consists of 350 clinical psychologist doing psychotherapy who completed DPCCQ. Data are currently being prepared for statistical analysis. Results: This paper will focus primarily...

  19. Kontraktetablering i supervision

    DEFF Research Database (Denmark)

    Mortensen, Karen Vibeke; Jacobsen, Claus Haugaard

    2007-01-01

    Kapitlet behandler kontraktetablering i supervision, et element, der ofte er blevet negligeret eller endog helt forbigået ved indledningen af supervisionsforløb. Sikre aftaler om emner som tid, sted, procedurer for fremlæggelse, fortrolighed, ansvarsfordeling og evaluering skaber imidlertid tryghed...

  20. Etiske betragtninger ved supervision

    DEFF Research Database (Denmark)

    Jacobsen, Claus Haugaard; Agerskov, Kirsten

    2007-01-01

    Kapitlet præsenterer nogle etiske betragtninger ved supervision. Mens der længe har eksisteret etiske retningslinjer for psykoterapeutisk arbejde, har der overraskende nok manglet tilsvarende vejledninger på supervisionsområdet. Det betyder imidlertid ikke, at de ikke er relevante. I kapitlet gøres...

  1. Finding patterns and learning words: Infant phonotactic knowledge is associated with vocabulary size.

    Science.gov (United States)

    Graf Estes, Katharine; Gluck, Stephanie Chen-Wu; Grimm, Kevin J

    2016-06-01

    Native language statistical regularities about allowable phoneme combinations (i.e., phonotactic patterns) may provide learners with cues to support word learning. The current research investigated the association between infants' native language phonotactic knowledge and their word learning progress, as measured by vocabulary size. In the experiment, 19-month-old infants listened to a corpus of nonce words that contained novel phonotactic patterns. All words began with "illegal" consonant clusters that cannot occur in native (English) words. The rationale for the task was that infants with fragile phonotactic knowledge should exhibit stronger learning of the novel illegal phonotactic patterns than infants with robust phonotactic knowledge. We found that infants with smaller vocabularies showed stronger phonotactic learning than infants with larger vocabularies even after accounting for general cognition. We propose that learning about native language structure may promote vocabulary development by providing a foundation for word learning; infants with smaller vocabularies may have weaker support from phonotactics than infants with larger vocabularies. Furthermore, stored vocabulary knowledge may promote the detection of phonotactic patterns even during infancy.

  2. 基于改进图半监督学习的个人信用评估方法%Personal Credit Scoring Method Using Improved Graph Based Semi-Supervised Learning

    Institute of Scientific and Technical Information of China (English)

    张燕; 张晨光; 张夏欢

    2012-01-01

    Labeled instances are expensive to collect for personal credit scoring. However, unlabeled data are often relatively easy to obtain. Aiming at this problem and the ubiquitous asymmetry of credit datasets, this paper proposes a personal credit scoring model based on improved graph based semi-supervised learning method. Because the model adopts semi-supervised technology, it can learn from abundant unlabeled instances to avoid the decreasing of generalization ability which is induced by the relative lack of labeled data. Furthermore, by improving graph based semi-supervised learning technology with normalization and modification of decision boundary on its iterative results, the scoring model effectively reduces the bad impact of asymmetric dataset. Experiments on three UCI credit approval datasets show that the new scoring model can provide significantly better results than support vector machines and the unimproved method.%针对个人信用评估中未标号数据获取容易而已标号数据获取相对困难,以及普遍存在的数据不对称问题,提出了基于改进图半监督学习技术的个人信用评估模型.该模型采用了半监督学习技术,一方面能从大量的未标号数据中学习,避免了个人信用评估中已标号数据相对缺乏造成的泛化能力下降问题;另一方面,通过改进图半监督学习技术,对图半监督迭代结果进行归一化及修改决策边界,有效减小了数据不对称的影响.在UCI的三个信用审核数据集上的评测结果表明,该模型具有明显优于支持向量机和改进前方法的评估效果.

  3. Co-Training Semi-Supervised Active Learning Algorithm with Noise Filter%具有噪声过滤功能的协同训练半监督主动学习算法

    Institute of Scientific and Technical Information of China (English)

    詹永照; 陈亚必

    2009-01-01

    针对基于半监督学习的分类器利用未标记样本训练会引入噪声而使得分类性能下降的情形,文中提出一种具有噪声过滤功能的协同训练半监督主动学习算法.该算法以3个模糊深隐马尔可夫模型进行协同半监督学习,在适当的时候主动引入一些人机交互来补充类别标记,避免判决类别不相同时的拒判和初始时判决一致即认为正确的误判情形.同时加入噪声过滤机制,用以过滤南机器自动标记的可能是噪声的样本.将该算法应用于人脸表情识别.实验结果表明,该算法能有效提高未标记样本的利用率并降低半监督学习而引入的噪声,提高表情识别的准确率.%The classification performance of the classifier based on semi-supervised learning is weakened when the noise samples are introduced. An algorithm called co-training semi-supervised active learning with noise filter is presented to overcome this disadvantage. In this algorithm, three fuzzy buried Markov models are used to perform semi-supervised learning cooperatively. Some human-computer interactions are actively introduced into labelling the unlabeled sample at certain time in order to avoid the rejective judgment when the classifiers do not agree with each other and the inaccurate judgment when the initial weak classifiers all agree. Meanwhile, the noise filter is used to filter the possible noise samples which are labeled automatically by the computer. The proposed algorithm is applied to facial expression recognition. The experimental results show that the algorithm can effectively improve the utilization of unlabeled samples, reduce the introduction of noise samples and raise the accuracy of expression recognition.

  4. Learning System of Web Navigation Patterns through Hypertext Probabilistic Grammars

    Science.gov (United States)

    Cortes Vasquez, Augusto

    2015-01-01

    One issue of real interest in the area of web data mining is to capture users' activities during connection and extract behavior patterns that help define their preferences in order to improve the design of future pages adapting websites interfaces to individual users. This research is intended to provide, first of all, a presentation of the…

  5. Participatory Pattern Workshops: A Methodology for Open Learning Design Inquiry

    Science.gov (United States)

    Mor, Yishay; Warburton, Steven; Winters, Niall

    2012-01-01

    In order to promote pedagogically informed use of technology, educators need to develop an active, inquisitive, design-oriented mindset. Design Patterns have been demonstrated as powerful mediators of theory-praxis conversations yet widespread adoption by the practitioner community remains a challenge. Over several years, the authors and their…

  6. Levels and Patterns of Participation and Social Interaction in an Online Learning Community for Learning to Teach

    Science.gov (United States)

    Tsai, I-Chun

    2011-01-01

    This study investigates how pre-service and in-service teachers participate in an online community for learning to teach. Members' levels and patterns of participation and social interaction were examined via social network analysis of activity logs and content analysis of interviews. The results of the analyses show that (a) members' levels and…

  7. A Semi-supervised Kernel Learning Method Based on Label Propagation%一种基于标签传播的半监督核学习算法

    Institute of Scientific and Technical Information of China (English)

    袁优; 张钢

    2013-01-01

    A good kernel function can improve the performance of machine learning models. However,it is not easy to properly determine a kernel since it is closely related to application background and relies on domain knowledge and experience. Kernel learning is a machine learning method which seeks an optimal kernel function with a given training data set. It often seeks an optimal weighted combination of a pre-defined set of base kernel functions. Considering the cost of acquiring labeled training samples,we propose a semi-supervised kernel learning method based on label propagation,which makes use of labeled and unlabeled samples simutaneously to perform kernel learning,and applies label propagation method,a popular method in semi-supervised learning,combined with harmonic function to obtain a unified distribution of the whole data set. The proposed metod is evaluated on the UCI benchmark data set and the results show its effectiveness.%一个好的核函数能提升机器学习模型的有效性,但核函数的选择并不容易,其与问题背景密切相关,且依赖于领域知识和经验。核学习是一种通过训练数据集寻找最优核函数的机器学习方法,能通过有监督学习的方式寻找到一组基核函数的最优加权组合。考虑到训练数据集获取标签的代价,提出一种基于标签传播的半监督核学习方法,该方法能够同时利用有标签数据和无标签数据进行核学习,通过半监督学习中被广泛使用的标签传播方法结合和谐函数获得数据集统一的标签分布。在UCI数据集上对提出的算法进行性能评估,结果表明该方法是有效的。

  8. The potential of the inventory of learning styles to study students' learning patterns in three types of medical curricula.

    Science.gov (United States)

    Van der Veken, J; Valcke, M; Muijtjens, A; De Maeseneer, J; Derese, A

    2008-01-01

    Introducing innovative curricular designs can be evaluating by scrutinizing the learning patterns students use. Studying the potential of Vermunt's Inventory of Learning Styles (ILS) in detecting differences in student learning patterns in different medical curricula. Cross-sectional between-subjects comparison of ILS-scores in third-year medical students in a conventional, an integrated contextual and a PBL-curriculum using one-way post hoc ANOVA. Response rate was 85%: 197 conventional, 130 integrated contextual and 301 PBL students. The results show a differential impact from the three curricula. In relation to processing strategies, the students in the problem-based curriculum showed less rote learning and rehearsing, greater variety in sources of knowledge used and less ability to express study content in a personal manner than did the students in the conventional curriculum. The students of the integrated contextual curriculum showed more structuring of subject matter by integrating different aspects into a whole. In relation to regulation strategies, the students in the problem-based curriculum showed significantly more self-regulation of learning content and the students in the integrated contextual curriculum showed lower levels of regulation. As to learning orientations, the students in the problem-based curriculum showed less ambivalence and the students of the conventional curriculum were less vocationally oriented. The study provides empirical support for expected effects of traditional and innovative curricula which thus far were not well supported by empirical studies.

  9. Patterns of Field Learning Activities and Their Relation to Learning Outcome

    Science.gov (United States)

    Lee, Mingun; Fortune, Anne E.

    2013-01-01

    Field practicum is an active learning process. This study explores the different learning stages or processes students experience during their field practicum. First-year master's of social work students in field practica were asked how much they had engaged in educational learning activities such as observation, working independently,…

  10. Engineering Students Learning Preferences in UNITEN: Comparative Study and Patterns of Learning Styles

    Science.gov (United States)

    Lee, Chen Kang; Sidhu, Manjit Singh

    2015-01-01

    Engineering educators have been increasingly taking the learning style theories into serious consideration as part of their efforts to enhance the teaching and learning in engineering. This paper presents a research study to investigate the learning style preference of the mechanical engineering students in Universiti Tenaga Nasional (UNITEN),…

  11. Engineering Students Learning Preferences in UNITEN: Comparative Study and Patterns of Learning Styles

    Science.gov (United States)

    Lee, Chen Kang; Sidhu, Manjit Singh

    2015-01-01

    Engineering educators have been increasingly taking the learning style theories into serious consideration as part of their efforts to enhance the teaching and learning in engineering. This paper presents a research study to investigate the learning style preference of the mechanical engineering students in Universiti Tenaga Nasional (UNITEN),…

  12. A qualitative study of the learning processes in young physicians treating suicidal patients: from insecurity to personal pattern knowledge and self-confidence

    Directory of Open Access Journals (Sweden)

    Talseth Anne-Grethe

    2007-07-01

    Full Text Available Abstract Background Little empirical work has been done in studying learning processes among newly educated physicians in the mental health field. The aim of the study was to shed light on the meaning of newly educated physicians' lived experiences of learning processes related to treating suicidal patients. Methods Thirteen newly educated physicians narrated their learning experiences while treating suicidal patients in their own practice. The interview texts were transcribed and interpreted using a phenomenological-hermeneutical method inspired by Ricoeur's philosophy. Results There was one main theme, four themes and eleven sub themes. The main theme was: Being in a transitional learning process. The themes and sub themes were: Preparing for practice (Getting tools and training skills, Becoming aware of one's own attitudes; Gaining experience from treating patients (Treating and following up patients over time, Storing memories and recognizing similarities and differences in patients; Participating in the professional community (Being an apprentice, Relating clinical stories and receiving feedback, Sharing emotions from clinical experiences, Receiving support from peers; and Developing personal competence (Having unarticulated awareness, Having emotional knowledge, Achieving self-confidence. The informants gave a detailed account of the learning process; from recognising similarities and differences in patients they have treated, to accumulating pattern knowledge, which then contributed to their personal feelings of competence and confidence. They described their personal competence with cognitive and emotional elements consisting of both articulated and less articulated knowledge. The findings are interpreted in relation to different learning theories that focus on both individual factors and the interaction with the learning environment. Conclusion This study provides additional information about learning experiences of young physicians

  13. Modelling Global Pattern Formations for Collaborative Learning Environments

    DEFF Research Database (Denmark)

    Grappiolo, Corrado; Cheong, Yun-Gyung; Khaled, Rilla;

    2012-01-01

    We present our research towards the design of a computational framework capable of modelling the formation and evolution of global patterns (i.e. group structures) in a population of social individuals. The framework is intended to be used in collaborative environments, e.g. social serious games...... and computer simulations of artificial societies. The theoretical basis of our research, together with current state of the art and future work, are briefly introduced....

  14. The Principal Pattern of Educational Supervision in the International Perspective Selection and Reconstruction%国际视阈下中国教育督导机构模式的选择与重构

    Institute of Scientific and Technical Information of China (English)

    杨頔; 吕林

    2015-01-01

    Education supervision mechanism is the core of the system of educational supervision,however our country education supervision mechanism has not been set up with the mode of educational supervision system,re-sulting in chaos and inefficient in local practice. Education supervision institutions do not have a unified mode in the world,but China has“Duzheng”characteristics,the reference of legislation technology and system theory education supervision institutions in the world from the perspective of the international mode,our country education supervision main body should be clear to the government at the corresponding level directly under the authority of the independent mechanism,which can greatly enhance the education supervision mechanism in order to reconstruct the education su-pervision mechanism mode.%教育督导机构是教育督导的核心制度,然而我国教育督导机构模式并未随教育督导制度的建立而明确,以导致长期以来地方实践的混乱与低效。教育督导机构在全世界没有一个统一的模式,但中国有“督政”的特色,在国际视阈下借鉴各国教育督导机构模式的立法技术与制度理论,我国教育督导主体应当明确为本级政府直属的独立机构,可以极大提升教育督导机构的权威性,以此重构教育督导机构模式,推进中国特色教育督导的制度建设。

  15. Social networks in supervision

    DEFF Research Database (Denmark)

    Lystbæk, Christian Tang

    and practice have focused on conceptual frameworks and practical techniques of promoting reflection through conversation in general and questioning in particular. However, in recent years, supervision research has started to focus on the social and technological aspects of supervision. This calls...... is constituted by the relationality of the actors, not by the actors themselves. In other words, no one acts in a vacuum but rather always under the influence of a wide range of surrounding and interconnected factors. Actors are actors because they are in a networked relationship. Thus, focusing on social...... and space. That involves mobilised an denrolled actos, both animate and inanimate (e.g. books, computers, etc. Actor-network theory defines a symmetry between animate and inanimate, i.e. subjects and objects, because ”human powers increasingly derive from the complex interconnections if human with material...

  16. A pedagogical design pattern framework for sharing experiences and enhancing communities of practice within online and blended learning

    DEFF Research Database (Denmark)

    May, Michael; Neutszky-Wulff, Chresteria; Rosthøj, Susanne

    2016-01-01

    for teachers at the University of Copenhagen a new and simpler pedagogical design pattern framework was developed for interfaculty sharing of experiences and enhancing communities of practice in relation to online and blended learning across the university. The framework of pedagogical design patterns were...... applied to describe the learning design in four online and blended learning courses within different academic disciplines: Classical Greek, Biostatistics, Environmental Management in Europe, and Climate Change Impacts, Adaptation and Mitigation. Future perspectives for using the framework for developing...... new E-learning patterns for online and blended learning courses are discussed....

  17. Lack of strategy holding: a new pattern of learning deficit in cortical dementias.

    Science.gov (United States)

    Benedet, María J; Lauro-Grotto, Rosapia; Giotti, Chiara

    2009-09-01

    The aim of this study was to demonstrate, by means of systematic research and qualitative data analysis, the presence, among a group of patients with fronto-temporal lobar degeneration of a subgroup that, at variance with the standard pattern, is able to devise and implement learning strategies, but appear impaired at carrying them on from a trial to the next. In order to provide evidence of the existence of a group of patients showing this type of learning disability, that we refer to as lack of strategy holding, we performed a stepwise hierarchical cluster analysis of a set of variables whose scores were selected from the subject's performance at the Test de Aprendizaje Verbal España-Complutense. Results substantiate the segregation of three groups of subjects characterized by the following patterns of performance: normal elderly individuals, who show a quite preserved ability to discover a semantic strategy along the learning trials and to carry it from a trial to the next, patients presenting with a deficit in implementing semantic learning strategies and possibly use of serial and/or phonological strategies to perform the task, and to patients who, although able to generate and implement appropriate learning strategies, appear unable to carry them over the learning trials. The presence of this new pattern raises a few questions that seem worth trying to address.

  18. Rethinking Hearing Aid Fitting by Learning From Behavioral Patterns

    DEFF Research Database (Denmark)

    Johansen, Benjamin; Petersen, Michael Kai; Pontoppidan, Niels Henrik

    2017-01-01

    The recent introduction of Internet connected hearing instruments offers a paradigm shift in hearing instrument fitting. Potentially this makes it possible for devices to adapt their settings to a changing context, inferred from user interactions. In a pilot study we enabled hearing instrument...... users to remotely enhance auditory focus and attenuate background noise to improve speech intelligibility. N=5, participants changed program settings and adjusted volume on their hearing instruments using their smartphones. We found that individual behavioral patterns affected the usage of the devices...

  19. Assimilation and accommodation patterns in ventral occipitotemporal cortex in learning a second writing system.

    Science.gov (United States)

    Nelson, Jessica R; Liu, Ying; Fiez, Julie; Perfetti, Charles A

    2009-03-01

    Using fMRI, we compared the patterns of fusiform activity produced by viewing English and Chinese for readers who were either English speakers learning Chinese or Chinese-English bilinguals. The pattern of fusiform activity depended on both the writing system and the reader's native language. Native Chinese speakers fluent in English recruited bilateral fusiform areas when viewing both Chinese and English. English speakers learning Chinese, however, used heavily left-lateralized fusiform regions when viewing English, but recruited an additional right fusiform region for viewing Chinese. Thus, English learners of Chinese show an accommodation pattern, in which the reading network accommodates the new writing system by adding neural resources that support its specific graphic requirements. Chinese speakers show an assimilation pattern, in which the reading network established for L1 includes procedures sufficient for the graphic demands of L2 without major change.

  20. Neural Pattern Similarity in the Left IFG and Fusiform Is Associated with Novel Word Learning

    Directory of Open Access Journals (Sweden)

    Jing Qu

    2017-08-01

    Full Text Available Previous studies have revealed that greater neural pattern similarity across repetitions is associated with better subsequent memory. In this study, we used an artificial language training paradigm and representational similarity analysis to examine whether neural pattern similarity across repetitions before training was associated with post-training behavioral performance. Twenty-four native Chinese speakers were trained to learn a logographic artificial language for 12 days and behavioral performance was recorded using the word naming and picture naming tasks. Participants were scanned while performing a passive viewing task before training, after 4-day training and after 12-day training. Results showed that pattern similarity in the left pars opercularis (PO and fusiform gyrus (FG before training was negatively associated with reaction time (RT in both word naming and picture naming tasks after training. These results suggest that neural pattern similarity is an effective neurofunctional predictor of novel word learning in addition to word memory.