WorldWideScience

Sample records for generative semi-supervised learning

  1. 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

  2. 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...

  3. 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.

  4. 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.

  5. 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

  6. 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...

  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. 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.

  10. 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...

  11. 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.

  12. A Hybrid Generative/Discriminative Classifier Design for Semi-supervised Learing

    Science.gov (United States)

    Fujino, Akinori; Ueda, Naonori; Saito, Kazumi

    Semi-supervised classifier design that simultaneously utilizes both a small number of labeled samples and a large number of unlabeled samples is a major research issue in machine learning. Existing semi-supervised learning methods for probabilistic classifiers belong to either generative or discriminative approaches. This paper focuses on a semi-supervised probabilistic classifier design for multiclass and single-labeled classification problems and first presents a hybrid approach to take advantage of the generative and discriminative approaches. Our formulation considers a generative model trained on labeled samples and a newly introduced bias correction model, whose belongs to the same model family as the generative model, but whose parameters are different from the generative model. A hybrid classifier is constructed by combining both the generative and bias correction models based on the maximum entropy principle, where the combination weights of these models are determined so that the class labels of labeled samples are as correctly predicted as possible. We apply the hybrid approach to text classification problems by employing naive Bayes as the generative and bias correction models. In our experimental results on three English and one Japanese text data sets, we confirmed that the hybrid classifier significantly outperformed conventional probabilistic generative and discriminative classifiers when the classification performance of the generative classifier was comparable to the discriminative classifier.

  13. 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.

  14. 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.

  15. 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.

  16. 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

  17. 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.

  18. 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.

  19. 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

  20. 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.

  1. 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.

  2. 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 ...

  3. 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...

  4. 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...

  5. 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.

  6. 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.

  7. 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.

  8. 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

  9. 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.

  10. 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

  11. 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

  12. 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

  13. 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...

  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. 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.

  16. 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.

  17. 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.

  18. 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...

  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...... 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...

  20. 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...

  1. 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

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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).

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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

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

    Science.gov (United States)

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

    2015-01-01

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

  13. 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

  14. 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

  15. 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

  16. 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.

  17. Application of graph-based semi-supervised learning for development of cyber COP and network intrusion detection

    Science.gov (United States)

    Levchuk, Georgiy; Colonna-Romano, John; Eslami, Mohammed

    2017-05-01

    The United States increasingly relies on cyber-physical systems to conduct military and commercial operations. Attacks on these systems have increased dramatically around the globe. The attackers constantly change their methods, making state-of-the-art commercial and military intrusion detection systems ineffective. In this paper, we present a model to identify functional behavior of network devices from netflow traces. Our model includes two innovations. First, we define novel features for a host IP using detection of application graph patterns in IP's host graph constructed from 5-min aggregated packet flows. Second, we present the first application, to the best of our knowledge, of Graph Semi-Supervised Learning (GSSL) to the space of IP behavior classification. Using a cyber-attack dataset collected from NetFlow packet traces, we show that GSSL trained with only 20% of the data achieves higher attack detection rates than Support Vector Machines (SVM) and Naïve Bayes (NB) classifiers trained with 80% of data points. We also show how to improve detection quality by filtering out web browsing data, and conclude with discussion of future research directions.

  18. 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

  19. 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

  20. 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.

  1. 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.

  2. 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.

  3. 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

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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。

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. Enhanced manifold regularization for semi-supervised classification.

    Science.gov (United States)

    Gan, Haitao; Luo, Zhizeng; Fan, Yingle; Sang, Nong

    2016-06-01

    Manifold regularization (MR) has become one of the most widely used approaches in the semi-supervised learning field. It has shown superiority by exploiting the local manifold structure of both labeled and unlabeled data. The manifold structure is modeled by constructing a Laplacian graph and then incorporated in learning through a smoothness regularization term. Hence the labels of labeled and unlabeled data vary smoothly along the geodesics on the manifold. However, MR has ignored the discriminative ability of the labeled and unlabeled data. To address the problem, we propose an enhanced MR framework for semi-supervised classification in which the local discriminative information of the labeled and unlabeled data is explicitly exploited. To make full use of labeled data, we firstly employ a semi-supervised clustering method to discover the underlying data space structure of the whole dataset. Then we construct a local discrimination graph to model the discriminative information of labeled and unlabeled data according to the discovered intrinsic structure. Therefore, the data points that may be from different clusters, though similar on the manifold, are enforced far away from each other. Finally, the discrimination graph is incorporated into the MR framework. In particular, we utilize semi-supervised fuzzy c-means and Laplacian regularized Kernel minimum squared error for semi-supervised clustering and classification, respectively. Experimental results on several benchmark datasets and face recognition demonstrate the effectiveness of our proposed method.

  17. Semi-supervised clustering methods

    Science.gov (United States)

    Bair, Eric

    2013-01-01

    Cluster analysis methods seek to partition a data set into homogeneous subgroups. It is useful in a wide variety of applications, including document processing and modern genetics. Conventional clustering methods are unsupervised, meaning that there is no outcome variable nor is anything known about the relationship between the observations in the data set. In many situations, however, information about the clusters is available in addition to the values of the features. For example, the cluster labels of some observations may be known, or certain observations may be known to belong to the same cluster. In other cases, one may wish to identify clusters that are associated with a particular outcome variable. This review describes several clustering algorithms (known as “semi-supervised clustering” methods) that can be applied in these situations. The majority of these methods are modifications of the popular k-means clustering method, and several of them will be described in detail. A brief description of some other semi-supervised clustering algorithms is also provided. PMID:24729830

  18. Improved semi-supervised online boosting for object tracking

    Science.gov (United States)

    Li, Yicui; Qi, Lin; Tan, Shukun

    2016-10-01

    The advantage of an online semi-supervised boosting method which takes object tracking problem as a classification problem, is training a binary classifier from labeled and unlabeled examples. Appropriate object features are selected based on real time changes in the object. However, the online semi-supervised boosting method faces one key problem: The traditional self-training using the classification results to update the classifier itself, often leads to drifting or tracking failure, due to the accumulated error during each update of the tracker. To overcome the disadvantages of semi-supervised online boosting based on object tracking methods, the contribution of this paper is an improved online semi-supervised boosting method, in which the learning process is guided by positive (P) and negative (N) constraints, termed P-N constraints, which restrict the labeling of the unlabeled samples. First, we train the classification by an online semi-supervised boosting. Then, this classification is used to process the next frame. Finally, the classification is analyzed by the P-N constraints, which are used to verify if the labels of unlabeled data assigned by the classifier are in line with the assumptions made about positive and negative samples. The proposed algorithm can effectively improve the discriminative ability of the classifier and significantly alleviate the drifting problem in tracking applications. In the experiments, we demonstrate real-time tracking of our tracker on several challenging test sequences where our tracker outperforms other related on-line tracking methods and achieves promising tracking performance.

  19. 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...

  20. 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

  1. Active constraints selection based semi-supervised dimensionality in ensemble subspaces

    Institute of Scientific and Technical Information of China (English)

    Jie Zeng; Wei Nie; Yong Zhang

    2015-01-01

    Semi-supervised dimensionality reduction (SSDR) has attracted an increasing amount of attention in this big-data era. Many algorithms have been developed with a smal number of pairwise constraints to achieve performances comparable to those of ful y supervised methods. However, one chal enging problem with semi-supervised approaches is the appropriate choice of the constraint set, including the cardinality and the composition of the constraint set, which to a large extent, affects the performance of the resulting algorithm. In this work, we address the problem by incorporating ensemble subspace and active learning into dimen-sionality reduction and propose a new algorithm, termed as global and local scatter based SSDR with active pairwise constraints selection in ensemble subspaces (SSGL-ESA). Unlike traditional methods that select the supervised information in one subspace, we pick up pairwise constraints in ensemble subspace, where a novel active learning algorithm is designed with both exploration and filtering to generate informative pairwise constraints. The auto-matic constraint selection approach proposed in this paper can be generalized to be used with al constraint-based semi-supervised learning algorithms. Comparative experiments are conducted on two face database and the results validate the effectiveness of the proposed method.

  2. Incremental multi-class semi-supervised clustering regularized by Kalman filtering.

    Science.gov (United States)

    Mehrkanoon, Siamak; Agudelo, Oscar Mauricio; Suykens, Johan A K

    2015-11-01

    This paper introduces an on-line semi-supervised learning algorithm formulated as a regularized kernel spectral clustering (KSC) approach. We consider the case where new data arrive sequentially but only a small fraction of it is labeled. The available labeled data act as prototypes and help to improve the performance of the algorithm to estimate the labels of the unlabeled data points. We adopt a recently proposed multi-class semi-supervised KSC based algorithm (MSS-KSC) and make it applicable for on-line data clustering. Given a few user-labeled data points the initial model is learned and then the class membership of the remaining data points in the current and subsequent time instants are estimated and propagated in an on-line fashion. The update of the memberships is carried out mainly using the out-of-sample extension property of the model. Initially the algorithm is tested on computer-generated data sets, then we show that video segmentation can be cast as a semi-supervised learning problem. Furthermore we show how the tracking capabilities of the Kalman filter can be used to provide the labels of objects in motion and thus regularizing the solution obtained by the MSS-KSC algorithm. In the experiments, we demonstrate the performance of the proposed method on synthetic data sets and real-life videos where the clusters evolve in a smooth fashion over time.

  3. Active semi-supervised community detection based on must-link and cannot-link constraints.

    Directory of Open Access Journals (Sweden)

    Jianjun Cheng

    Full Text Available Community structure detection is of great importance because it can help in discovering the relationship between the function and the topology structure of a network. Many community detection algorithms have been proposed, but how to incorporate the prior knowledge in the detection process remains a challenging problem. In this paper, we propose a semi-supervised community detection algorithm, which makes full utilization of the must-link and cannot-link constraints to guide the process of community detection and thereby extracts high-quality community structures from networks. To acquire the high-quality must-link and cannot-link constraints, we also propose a semi-supervised component generation algorithm based on active learning, which actively selects nodes with maximum utility for the proposed semi-supervised community detection algorithm step by step, and then generates the must-link and cannot-link constraints by accessing a noiseless oracle. Extensive experiments were carried out, and the experimental results show that the introduction of active learning into the problem of community detection makes a success. Our proposed method can extract high-quality community structures from networks, and significantly outperforms other comparison methods.

  4. 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.

  5. A semi-supervised method for predicting transcription factor-gene interactions in Escherichia coli.

    Directory of Open Access Journals (Sweden)

    Jason Ernst

    2008-03-01

    Full Text Available While Escherichia coli has one of the most comprehensive datasets of experimentally verified transcriptional regulatory interactions of any organism, it is still far from complete. This presents a problem when trying to combine gene expression and regulatory interactions to model transcriptional regulatory networks. Using the available regulatory interactions to predict new interactions may lead to better coverage and more accurate models. Here, we develop SEREND (SEmi-supervised REgulatory Network Discoverer, a semi-supervised learning method that uses a curated database of verified transcriptional factor-gene interactions, DNA sequence binding motifs, and a compendium of gene expression data in order to make thousands of new predictions about transcription factor-gene interactions, including whether the transcription factor activates or represses the gene. Using genome-wide binding datasets for several transcription factors, we demonstrate that our semi-supervised classification strategy improves the prediction of targets for a given transcription factor. To further demonstrate the utility of our inferred interactions, we generated a new microarray gene expression dataset for the aerobic to anaerobic shift response in E. coli. We used our inferred interactions with the verified interactions to reconstruct a dynamic regulatory network for this response. The network reconstructed when using our inferred interactions was better able to correctly identify known regulators and suggested additional activators and repressors as having important roles during the aerobic-anaerobic shift interface.

  6. Semi-supervised detection of intracranial pressure alarms using waveform dynamics.

    Science.gov (United States)

    Scalzo, Fabien; Hu, Xiao

    2013-04-01

    Patient monitoring systems in intensive care units (ICU) are usually set to trigger alarms when abnormal values are detected. Alarms are generated by threshold-crossing rules that lead to high false alarm rates. This is a recognized issue that causes alarm fatigue, waste of human resources, and increased patient risks. Recently developed smart alarm models require alarms to be validated by experts during the training phase. The manual annotation process involved is time-consuming and virtually impossible to achieve for the thousands of alarms recorded in the ICU every week. To tackle this problem, we investigate in this study if the use of semi-supervised learning methods, that can naturally integrate unlabeled data samples in the model, can be used to improve the accuracy of the alarm detection. As a proof of concept, the detection system is evaluated on intracranial pressure (ICP) signal alarms. Specific morphological and trending features are extracted from the ICP signal waveform to capture the dynamic of the signal prior to alarms. This study is based on a comprehensive dataset of 4791 manually labeled alarms recorded from 108 neurosurgical patients. A comparative analysis is provided between kernel spectral regression (SR-KDA) and support vector machine (SVM) both modified for the semi-supervised setting. Results obtained during the experimental evaluations indicate that the two models can significantly reduce false alarms using unlabeled samples; especially in the presence of a restrained number of labeled examples. At a true alarm recognition rate of 99%, the false alarm reduction rates improved from 9% (supervised) to 27% (semi-supervised) for SR-KDA, and from 3% (supervised) to 16% (semi-supervised) for SVM.

  7. 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...

  8. 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...

  9. 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.

  10. 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.

  11. Semi-supervised SVM for individual tree crown species classification

    Science.gov (United States)

    Dalponte, Michele; Ene, Liviu Theodor; Marconcini, Mattia; Gobakken, Terje; Næsset, Erik

    2015-12-01

    In this paper a novel semi-supervised SVM classifier is presented, specifically developed for tree species classification at individual tree crown (ITC) level. In ITC tree species classification, all the pixels belonging to an ITC should have the same label. This assumption is used in the learning of the proposed semi-supervised SVM classifier (ITC-S3VM). This method exploits the information contained in the unlabeled ITC samples in order to improve the classification accuracy of a standard SVM. The ITC-S3VM method can be easily implemented using freely available software libraries. The datasets used in this study include hyperspectral imagery and laser scanning data acquired over two boreal forest areas characterized by the presence of three information classes (Pine, Spruce, and Broadleaves). The experimental results quantify the effectiveness of the proposed approach, which provides classification accuracies significantly higher (from 2% to above 27%) than those obtained by the standard supervised SVM and by a state-of-the-art semi-supervised SVM (S3VM). Particularly, by reducing the number of training samples (i.e. from 100% to 25%, and from 100% to 5% for the two datasets, respectively) the proposed method still exhibits results comparable to the ones of a supervised SVM trained with the full available training set. This property of the method makes it particularly suitable for practical forest inventory applications in which collection of in situ information can be very expensive both in terms of cost and time.

  12. Quintic spline smooth semi-supervised support vector classification machine

    Institute of Scientific and Technical Information of China (English)

    Xiaodan Zhang; Jinggai Ma; Aihua Li; Ang Li

    2015-01-01

    A semi-supervised vector machine is a relatively new learning method using both labeled and unlabeled data in classifi-cation. Since the objective function of the model for an unstrained semi-supervised vector machine is not smooth, many fast opti-mization algorithms cannot be applied to solve the model. In order to overcome the difficulty of dealing with non-smooth objective functions, new methods that can solve the semi-supervised vector machine with desired classification accuracy are in great demand. A quintic spline function with three-times differentiability at the ori-gin is constructed by a general three-moment method, which can be used to approximate the symmetric hinge loss function. The approximate accuracy of the quintic spline function is estimated. Moreover, a quintic spline smooth semi-support vector machine is obtained and the convergence accuracy of the smooth model to the non-smooth one is analyzed. Three experiments are performed to test the efficiency of the model. The experimental results show that the new model outperforms other smooth models, in terms of classification performance. Furthermore, the new model is not sensitive to the increasing number of the labeled samples, which means that the new model is more efficient.

  13. 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。

  14. 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

  15. Semi-Supervised Additive Logistic Regression: A Gradient Descent Solution

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    This paper describes a semi-supervised regularized method for additive logistic regression. The graph regularization term of the combined functions is added to the original cost functional used in AdaBoost. This term constrains the learned function to be smooth on a graph. Then the gradient solution is computed with the advantage that the regularization parameter can be adaptively selected. Finally, the function step-size of each iteration can be computed using Newton-Raphson iteration. Experiments on benchmark data sets show that the algorithm gives better results than existing methods.

  16. Semi Supervised Weighted K-Means Clustering for Multi Class Data Classification

    Directory of Open Access Journals (Sweden)

    Vijaya Geeta Dharmavaram

    2013-01-01

    Full Text Available Supervised Learning techniques require large number of labeled examples to train a classifier model. Research on Semi Supervised Learning is motivated by the availability of unlabeled examples in abundance even in domains with limited number of labeled examples. In such domains semi supervised classifier uses the results of clustering for classifier development since clustering does not rely only on labeled examples as it groups the objects based on their similarities. In this paper, the authors propose a new algorithm for semi supervised classification namely Semi Supervised Weighted K-Means (SSWKM. In this algorithm, the authors suggest the usage of weighted Euclidean distance metric designed as per the purpose of clustering for estimating the proximity between a pair of points and used it for building semi supervised classifier. The authors propose a new approach for estimating the weights of features by appropriately adopting the results of multiple discriminant analysis. The proposed method was then tested on benchmark datasets from UCI repository with varied percentage of labeled examples and found to be consistent and promising.

  17. Semi-supervised least squares support vector machine algorithm: application to offshore oil reservoir

    Science.gov (United States)

    Luo, Wei-Ping; Li, Hong-Qi; Shi, Ning

    2016-06-01

    At the early stages of deep-water oil exploration and development, fewer and further apart wells are drilled than in onshore oilfields. Supervised least squares support vector machine algorithms are used to predict the reservoir parameters but the prediction accuracy is low. We combined the least squares support vector machine (LSSVM) algorithm with semi-supervised learning and established a semi-supervised regression model, which we call the semi-supervised least squares support vector machine (SLSSVM) model. The iterative matrix inversion is also introduced to improve the training ability and training time of the model. We use the UCI data to test the generalization of a semi-supervised and a supervised LSSVM models. The test results suggest that the generalization performance of the LSSVM model greatly improves and with decreasing training samples the generalization performance is better. Moreover, for small-sample models, the SLSSVM method has higher precision than the semi-supervised K-nearest neighbor (SKNN) method. The new semisupervised LSSVM algorithm was used to predict the distribution of porosity and sandstone in the Jingzhou study area.

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

    CERN Document Server

    Hisano, Ryohei

    2016-01-01

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

  19. Target Localization in Wireless Sensor Networks Using Online Semi-Supervised Support Vector Regression

    Directory of Open Access Journals (Sweden)

    Jaehyun Yoo

    2015-05-01

    Full Text Available Machine learning has been successfully used for target localization in wireless sensor networks (WSNs due to its accurate and robust estimation against highly nonlinear and noisy sensor measurement. For efficient and adaptive learning, this paper introduces online semi-supervised support vector regression (OSS-SVR. The first advantage of the proposed algorithm is that, based on semi-supervised learning framework, it can reduce the requirement on the amount of the labeled training data, maintaining accurate estimation. Second, with an extension to online learning, the proposed OSS-SVR automatically tracks changes of the system to be learned, such as varied noise characteristics. We compare the proposed algorithm with semi-supervised manifold learning, an online Gaussian process and online semi-supervised colocalization. The algorithms are evaluated for estimating the unknown location of a mobile robot in a WSN. The experimental results show that the proposed algorithm is more accurate under the smaller amount of labeled training data and is robust to varying noise. Moreover, the suggested algorithm performs fast computation, maintaining the best localization performance in comparison with the other methods.

  20. A study of relation extraction of undefined relation type based on semi-supervised learning framework%未定义类型的关系抽取的半监督学习框架研究

    Institute of Scientific and Technical Information of China (English)

    程显毅; 朱倩

    2012-01-01

    This study aims to design a relation extraction system with undefined relation type. However, without specific areas and machine-readable knowledge as a guide, it is difficult to achieve expected precision and recall in relation extraction for natural language texts. This paper describes a framework of extraction entity-attribute-value relationship based on semi-supervised machine learning. In semi-supervised learning tasks, seeds are obtained from the Wikipedia information table. We first identify some strong counter-example with a linear classifier, then re-train the classifier with the existing counter-example data, and finally find more counter-examples in remainingunannotated data. After semi-supervised learning, we can obtain a set of candidate relationship instances. Then we discuss the verification problem of the relationship categories. For the noise mode, we propose a standard evaluating relationship model confidence level. If modes have conflict, control match order algorithm will be presented(i, e. the principle of high confidence mode priority matching). After two algorithms, the relation type may be still with diversities, then the algorithm of condensed hierarchical clustering will be presented in this paper, which expresses Wikipedia as a vector, and give a computing mode of similar relational and complete relation type clustering. In the Wikipedia XML data sets experiments are conducted , and results show that according to Wikipedia, we can dynamically determine relation type, reduce the dependence on the predefined types, and improve the portability of relation recognition system.%设计未定义类型关系抽取系统是目前研究的热点.但在没有特定领域的、机器可读的知识作为指导的情况下,面向自然语言文本的关系抽取很难取得令人满意的精确度和召回率,约束可以有效辅助语义关系的抽取.本文描述了一个提取“实体一属性一值”关系的半监督的机器学

  1. Semi-supervised clustering using soft-constraint affinity propagation

    CERN Document Server

    Leone, Michele; Weigt, Martin

    2007-01-01

    Semi-supervised clustering aims at dividing partially labeled data into groups, assigning labels to previously unlabeled points. It uses both the geometrical organization of the data set and the available labels assigned to few points, giving additional information compared to unsupervised clustering methods. In this letter, we present a novel, computationally efficient and statistically robust semi-supervised clustering algorithm based on soft-constraint affinity propagation. The method is successfully tested on artificial and biological benchmark data.

  2. SEMI-SUPERVISED RADIO TRANSMITTER CLASSIFICATION BASED ON ELASTIC SPARSITY REGULARIZED SVM

    Institute of Scientific and Technical Information of China (English)

    Hu Guyu; Gong Yong; Chen Yande; Pan Zhisong; Deng Zhantao

    2012-01-01

    Non-collaborative radio transmitter recognition is a significant but challenging issue,sinceit is hard or costly to obtain labeled training data samples.In order to make effective use of the unlabeled samples which can be obtained much easier,a novel semi-supervised classification method named Elastic Sparsity Regularized Support Vector Machine (ESRSVM) is proposed for radio transmitter classification.ESRSVM first constructs an elastic-net graph over data samples to capture the robust and natural discriminating information and then incorporate the information into the manifold learning framework by an elastic sparsity regularization term.Experimental results on 10 GMSK modulated Automatic Identification System radios and 15 FM walkie-talkie radios show that ESRSVM achieves obviously better performance than KNN and SVM,which use only labeled samples for classification,and also outperforms semi-supervised classifier LapSVM based on manifold regularization.

  3. Semi-supervised Laplacian Eigenmap%半监督拉普拉斯特征映射算法

    Institute of Scientific and Technical Information of China (English)

    刘海红; 周聪辉

    2012-01-01

    How incorporate manifold learning and semi-supervised machine learning to extend the manifold learning algorithm. One way is to use the prior information in the form of on-manifold coordinates of certain data samples to compute the low-dimension coordinates of the other data samples. Combined Laplacian Eigenmap (LE) with semi-supervised machine learning, a semi-supervised Laplacian Eigenmap (SSLE) is presented. Simulation and real examples show that SSLE is more effective in clasaifi-cation and recognition field.%为了使流形学习方法具有半监督的特点,利用流形上某些已知低维信息的数据去学习推测出其它数据的低维信息,扩大流形学习算法的应用范围,把拉普拉斯特征映射算法(Laplacian Eigenmap,LE)与半监督的机器学习相结合,提出一种半监督的拉普拉斯特征映射算法(semi-supervised Laplacian Eigenmap,SSLE),这种半监督的流形学习算法在分类识别等问题上,具有很好的效果.模拟实验和实际例子都表明了SSLE算法的有效性.

  4. A Novel Semi-Supervised Methodology for Extracting Tumor Type-Specific MRS Sources in Human Brain Data

    Science.gov (United States)

    Ortega-Martorell, Sandra; Ruiz, Héctor; Vellido, Alfredo; Olier, Iván; Romero, Enrique; Julià-Sapé, Margarida; Martín, José D.; Jarman, Ian H.; Arús, Carles; Lisboa, Paulo J. G.

    2013-01-01

    Background The clinical investigation of human brain tumors often starts with a non-invasive imaging study, providing information about the tumor extent and location, but little insight into the biochemistry of the analyzed tissue. Magnetic Resonance Spectroscopy can complement imaging by supplying a metabolic fingerprint of the tissue. This study analyzes single-voxel magnetic resonance spectra, which represent signal information in the frequency domain. Given that a single voxel may contain a heterogeneous mix of tissues, signal source identification is a relevant challenge for the problem of tumor type classification from the spectroscopic signal. Methodology/Principal Findings Non-negative matrix factorization techniques have recently shown their potential for the identification of meaningful sources from brain tissue spectroscopy data. In this study, we use a convex variant of these methods that is capable of handling negatively-valued data and generating sources that can be interpreted as tumor class prototypes. A novel approach to convex non-negative matrix factorization is proposed, in which prior knowledge about class information is utilized in model optimization. Class-specific information is integrated into this semi-supervised process by setting the metric of a latent variable space where the matrix factorization is carried out. The reported experimental study comprises 196 cases from different tumor types drawn from two international, multi-center databases. The results indicate that the proposed approach outperforms a purely unsupervised process by achieving near perfect correlation of the extracted sources with the mean spectra of the tumor types. It also improves tissue type classification. Conclusions/Significance We show that source extraction by unsupervised matrix factorization benefits from the integration of the available class information, so operating in a semi-supervised learning manner, for discriminative source identification and brain

  5. A novel semi-supervised methodology for extracting tumor type-specific MRS sources in human brain data.

    Directory of Open Access Journals (Sweden)

    Sandra Ortega-Martorell

    Full Text Available BACKGROUND: The clinical investigation of human brain tumors often starts with a non-invasive imaging study, providing information about the tumor extent and location, but little insight into the biochemistry of the analyzed tissue. Magnetic Resonance Spectroscopy can complement imaging by supplying a metabolic fingerprint of the tissue. This study analyzes single-voxel magnetic resonance spectra, which represent signal information in the frequency domain. Given that a single voxel may contain a heterogeneous mix of tissues, signal source identification is a relevant challenge for the problem of tumor type classification from the spectroscopic signal. METHODOLOGY/PRINCIPAL FINDINGS: Non-negative matrix factorization techniques have recently shown their potential for the identification of meaningful sources from brain tissue spectroscopy data. In this study, we use a convex variant of these methods that is capable of handling negatively-valued data and generating sources that can be interpreted as tumor class prototypes. A novel approach to convex non-negative matrix factorization is proposed, in which prior knowledge about class information is utilized in model optimization. Class-specific information is integrated into this semi-supervised process by setting the metric of a latent variable space where the matrix factorization is carried out. The reported experimental study comprises 196 cases from different tumor types drawn from two international, multi-center databases. The results indicate that the proposed approach outperforms a purely unsupervised process by achieving near perfect correlation of the extracted sources with the mean spectra of the tumor types. It also improves tissue type classification. CONCLUSIONS/SIGNIFICANCE: We show that source extraction by unsupervised matrix factorization benefits from the integration of the available class information, so operating in a semi-supervised learning manner, for discriminative source

  6. Semi-supervised consensus clustering for gene expression data analysis

    OpenAIRE

    Wang, Yunli; Pan, Youlian

    2014-01-01

    Background Simple clustering methods such as hierarchical clustering and k-means are widely used for gene expression data analysis; but they are unable to deal with noise and high dimensionality associated with the microarray gene expression data. Consensus clustering appears to improve the robustness and quality of clustering results. Incorporating prior knowledge in clustering process (semi-supervised clustering) has been shown to improve the consistency between the data partitioning and do...

  7. A Novel Classification Algorithm Based on Incremental Semi-Supervised Support Vector Machine

    Science.gov (United States)

    Gao, Fei; Mei, Jingyuan; Sun, Jinping; Wang, Jun; Yang, Erfu; Hussain, Amir

    2015-01-01

    For current computational intelligence techniques, a major challenge is how to learn new concepts in changing environment. Traditional learning schemes could not adequately address this problem due to a lack of dynamic data selection mechanism. In this paper, inspired by human learning process, a novel classification algorithm based on incremental semi-supervised support vector machine (SVM) is proposed. Through the analysis of prediction confidence of samples and data distribution in a changing environment, a “soft-start” approach, a data selection mechanism and a data cleaning mechanism are designed, which complete the construction of our incremental semi-supervised learning system. Noticeably, with the ingenious design procedure of our proposed algorithm, the computation complexity is reduced effectively. In addition, for the possible appearance of some new labeled samples in the learning process, a detailed analysis is also carried out. The results show that our algorithm does not rely on the model of sample distribution, has an extremely low rate of introducing wrong semi-labeled samples and can effectively make use of the unlabeled samples to enrich the knowledge system of classifier and improve the accuracy rate. Moreover, our method also has outstanding generalization performance and the ability to overcome the concept drift in a changing environment. PMID:26275294

  8. A Novel Classification Algorithm Based on Incremental Semi-Supervised Support Vector Machine.

    Directory of Open Access Journals (Sweden)

    Fei Gao

    Full Text Available For current computational intelligence techniques, a major challenge is how to learn new concepts in changing environment. Traditional learning schemes could not adequately address this problem due to a lack of dynamic data selection mechanism. In this paper, inspired by human learning process, a novel classification algorithm based on incremental semi-supervised support vector machine (SVM is proposed. Through the analysis of prediction confidence of samples and data distribution in a changing environment, a "soft-start" approach, a data selection mechanism and a data cleaning mechanism are designed, which complete the construction of our incremental semi-supervised learning system. Noticeably, with the ingenious design procedure of our proposed algorithm, the computation complexity is reduced effectively. In addition, for the possible appearance of some new labeled samples in the learning process, a detailed analysis is also carried out. The results show that our algorithm does not rely on the model of sample distribution, has an extremely low rate of introducing wrong semi-labeled samples and can effectively make use of the unlabeled samples to enrich the knowledge system of classifier and improve the accuracy rate. Moreover, our method also has outstanding generalization performance and the ability to overcome the concept drift in a changing environment.

  9. Spectral Methods for Linear and Non-Linear Semi-Supervised Dimensionality Reduction

    CERN Document Server

    Chatpatanasiri, Ratthachat

    2008-01-01

    We present a general framework of spectral methods for semi-supervised dimensionality reduction. Applying an approach called manifold regularization, our framework naturally generalizes existent supervised frameworks. Furthermore, by our two semi-supervised versions of the representer theorem, our framework can be kernelized as well. Using our framework, we give three examples of semi-supervised algorithms which are extended from three recent supervised algorithms, namely, ``discriminant neighborhood embedding'', ``marginal Fisher analysis'' and ``local Fisher discriminant analysis''. We also give three more semi-supervised examples of the kernel versions of these algorithms. Numerical results of the six semi-supervised algorithms compared to their supervised versions are presented.

  10. Semi-Supervised Transductive Hot Spot Predictor Working on Multiple Assumptions

    KAUST Repository

    Wang, Jim Jing-Yan

    2014-05-23

    Protein-protein interactions are critically dependent on just a few residues (“hot spots”) at the interfaces. Hot spots make a dominant contribution to the binding free energy and if mutated they can disrupt the interaction. As mutagenesis studies require significant experimental efforts, there exists a need for accurate and reliable computational hot spot prediction methods. Compared to the supervised hot spot prediction algorithms, the semi-supervised prediction methods can take into consideration both the labeled and unlabeled residues in the dataset during the prediction procedure. The transductive support vector machine has been utilized for this task and demonstrated a better prediction performance. To the best of our knowledge, however, none of the transductive semi-supervised algorithms takes all the three semisupervised assumptions, i.e., smoothness, cluster and manifold assumptions, together into account during learning. In this paper, we propose a novel semi-supervised method for hot spot residue prediction, by considering all the three semisupervised assumptions using nonlinear models. Our algorithm, IterPropMCS, works in an iterative manner. In each iteration, the algorithm first propagates the labels of the labeled residues to the unlabeled ones, along the shortest path between them on a graph, assuming that they lie on a nonlinear manifold. Then it selects the most confident residues as the labeled ones for the next iteration, according to the cluster and smoothness criteria, which is implemented by a nonlinear density estimator. Experiments on a benchmark dataset, using protein structure-based features, demonstrate that our approach is effective in predicting hot spots and compares favorably to other available methods. The results also show that our method outperforms the state-of-the-art transductive learning methods.

  11. A multi-label, semi-supervised classification approach applied to personality prediction in social media.

    Science.gov (United States)

    Lima, Ana Carolina E S; de Castro, Leandro Nunes

    2014-10-01

    Social media allow web users to create and share content pertaining to different subjects, exposing their activities, opinions, feelings and thoughts. In this context, online social media has attracted the interest of data scientists seeking to understand behaviours and trends, whilst collecting statistics for social sites. One potential application for these data is personality prediction, which aims to understand a user's behaviour within social media. Traditional personality prediction relies on users' profiles, their status updates, the messages they post, etc. Here, a personality prediction system for social media data is introduced that differs from most approaches in the literature, in that it works with groups of texts, instead of single texts, and does not take users' profiles into account. Also, the proposed approach extracts meta-attributes from texts and does not work directly with the content of the messages. The set of possible personality traits is taken from the Big Five model and allows the problem to be characterised as a multi-label classification task. The problem is then transformed into a set of five binary classification problems and solved by means of a semi-supervised learning approach, due to the difficulty in annotating the massive amounts of data generated in social media. In our implementation, the proposed system was trained with three well-known machine-learning algorithms, namely a Naïve Bayes classifier, a Support Vector Machine, and a Multilayer Perceptron neural network. The system was applied to predict the personality of Tweets taken from three datasets available in the literature, and resulted in an approximately 83% accurate prediction, with some of the personality traits presenting better individual classification rates than others.

  12. Document Clustering Based on Semi-Supervised Term Clustering

    Directory of Open Access Journals (Sweden)

    Hamid Mahmoodi

    2012-05-01

    Full Text Available The study is conducted to propose a multi-step feature (term selection process and in semi-supervised fashion, provide initial centers for term clusters. Then utilize the fuzzy c-means (FCM clustering algorithm for clustering terms. Finally assign each of documents to closest associated term clusters. While most text clustering algorithms directly use documents for clustering, we propose to first group the terms using FCM algorithm and then cluster documents based on terms clusters. We evaluate effectiveness of our technique on several standard text collections and compare our results with the some classical text clustering algorithms.

  13. 基于多步降维和半监督学习的蛋白质质谱特征提取算法%Feature Extraction of Protein Mass Spectrometry Data Based on Multi-step Dimensionality Reduction and Semi-supervised Learning

    Institute of Scientific and Technical Information of China (English)

    游晓璐; 祝磊; 曹凯敏; 韩斌

    2013-01-01

    目的 提出一种基于半监督学习的多步降维特征提取方法.方法 算法首先运用t-test对样本特征进行筛选,初步降低特征维度;然后进行离散小波变换,对小波系数进行相对熵排序,筛选出新的特征子集;接着进行主成分分析,提取主成分;最后运用半监督学习算法BB-LLGC进行标签传递,充分提取有标记和无标记样本的判别信息.结果 在公共卵巢癌数据集OC-WCX2b和公共前列腺癌数据集PC-H4上获得了99.13%和97.20%分类准确率.在浙江省肿瘤医院临床乳腺癌数据集BC-WCX2a上获得了92.78%的分类准确率和100%的敏感性.结论 多步降维的特征提取方法可以有效降低SELDI质谱数据的特征维度,结合半监督学习算法BB-LLGC,可以获得较好的分类效果.%Objective To propose a multi-step feature extraction algorithm based on semi-supervised learning for extraction of the features from the mass spectrometry data.Methods First,t-test was used to screen the mass spectrometry data and the dimension of features was preliminary reduced.Then,the multi-resolution wavelet decomposition was used to extract the detail features,and entropy ranking was used to screen the features.Next,principal component analysis was applied to extract the principal components.Finally,Semi-supervised learning BB-LLGC was used to carry outlabel tranfer and extract the differentiating information for labeled and unlabeled samples.Results Classification accuracy using this algorithm for the public ovarian cancer data OC-WCX2b and the public prostate cancer data PC-H4 could reach up to 99.13% and 97.20% respectively.In the clinical breast cancer data BC-WCX2a by Zhejiang Cancer Hospital,the classification accuracy was 92.78% and the sensitivity was 100%.Conclusion The multi-step feature extraction method can efficiently reduce the features dimension of SELDI and the combination with the semi-supervised learning of BBLLGC can significantly

  14. A Survey of Semi-supervised Text Categonzation%半监督文本分类综述

    Institute of Scientific and Technical Information of China (English)

    牛罡; 罗爱宝; 商琳

    2011-01-01

    Text categorization is a regular problem in people daily work and an interesting research area of machine learning.Semi-supervised learning algorithms, which consider both labeled and unlabeled data, can improve learning effectiveness significantly.This paper gives the definition and characteristic of text categorization and introduces the traditional supervised learning algorithms and evaluation indicators.Then it analyzes the characteristic and basic theory of semi-supervised text categorization, and discusses some algorithms on semi-supervised text categorization,such as Bayesian method and regularization method.%文本分类是人们日常工作中经常遇到的问题,也是机器学习的重要研究内容.半监督学习算法同时考虑有标记和无标记数据,能显著提升学习效果.给出了文本分类的定义和特点,介绍了传统的监督学习分类算法和评价指标,对半监督文本分类的特点和基础理论进行了分析,并具体介绍了一些半监督文本分类算法,如贝叶斯方法和正则化方法.

  15. Improving head and body pose estimation through semi-supervised manifold alignment

    KAUST Repository

    Heili, Alexandre

    2014-10-27

    In this paper, we explore the use of a semi-supervised manifold alignment method for domain adaptation in the context of human body and head pose estimation in videos. We build upon an existing state-of-the-art system that leverages on external labelled datasets for the body and head features, and on the unlabelled test data with weak velocity labels to do a coupled estimation of the body and head pose. While this previous approach showed promising results, the learning of the underlying manifold structure of the features in the train and target data and the need to align them were not explored despite the fact that the pose features between two datasets may vary according to the scene, e.g. due to different camera point of view or perspective. In this paper, we propose to use a semi-supervised manifold alignment method to bring the train and target samples closer within the resulting embedded space. To this end, we consider an adaptation set from the target data and rely on (weak) labels, given for example by the velocity direction whenever they are reliable. These labels, along with the training labels are used to bias the manifold distance within each manifold and to establish correspondences for alignment.

  16. A multimedia retrieval framework based on semi-supervised ranking and relevance feedback.

    Science.gov (United States)

    Yang, Yi; Nie, Feiping; Xu, Dong; Luo, Jiebo; Zhuang, Yueting; Pan, Yunhe

    2012-04-01

    We present a new framework for multimedia content analysis and retrieval which consists of two independent algorithms. First, we propose a new semi-supervised algorithm called ranking with Local Regression and Global Alignment (LRGA) to learn a robust Laplacian matrix for data ranking. In LRGA, for each data point, a local linear regression model is used to predict the ranking scores of its neighboring points. A unified objective function is then proposed to globally align the local models from all the data points so that an optimal ranking score can be assigned to each data point. Second, we propose a semi-supervised long-term Relevance Feedback (RF) algorithm to refine the multimedia data representation. The proposed long-term RF algorithm utilizes both the multimedia data distribution in multimedia feature space and the history RF information provided by users. A trace ratio optimization problem is then formulated and solved by an efficient algorithm. The algorithms have been applied to several content-based multimedia retrieval applications, including cross-media retrieval, image retrieval, and 3D motion/pose data retrieval. Comprehensive experiments on four data sets have demonstrated its advantages in precision, robustness, scalability, and computational efficiency.

  17. Deep Web Search Interface Identification: A Semi-Supervised Ensemble Approach

    Directory of Open Access Journals (Sweden)

    Hong Wang

    2014-12-01

    Full Text Available To surface the Deep Web, one crucial task is to predict whether a given web page has a search interface (searchable HyperText Markup Language (HTML form or not. Previous studies have focused on supervised classification with labeled examples. However, labeled data are scarce, hard to get and requires tediousmanual work, while unlabeled HTML forms are abundant and easy to obtain. In this research, we consider the plausibility of using both labeled and unlabeled data to train better models to identify search interfaces more effectively. We present a semi-supervised co-training ensemble learning approach using both neural networks and decision trees to deal with the search interface identification problem. We show that the proposed model outperforms previous methods using only labeled data. We also show that adding unlabeled data improves the effectiveness of the proposed model.

  18. 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.

  19. Graph Regularized Semi-Supervised Learning on Heterogeneous Information Networks%异构信息网络上基于图正则化的半监督学习

    Institute of Scientific and Technical Information of China (English)

    刘钰峰; 李仁发

    2015-01-01

    Heterogeneous information networks ,composed of multiple types of objects and links ,are ubiquitous in real life .Therefore ,effective analysis of large‐scale heterogeneous information networks poses an interesting but critical challenge . Learning from labeled and unlabeled data via semi‐supervised classification can lead to good knowledge extraction of the hidden network structure . How ever ,although semi‐supervised learning on homogeneous netw orks has been studied for decades , classification on heterogeneous networks has not been explored until recently . In this paper , we consider the semi‐supervised classification problem on heterogeneous information networks with an arbitrary schema consisting of a number of object and link types .By applying graph regularization to preserve consistency over each relation graph corresponding to each type of links separately , we develop a classifying function which is sufficiently smooth with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points .We propose an iterative framework on heterogeneous information network in which the information of labeled data can be spread to the adjacent nodes by iterative method until the steady state .We infer the class memberships of unlabeled data from those of labeled ones according to their proximities in the network .Experiments on the real DBLP data set clearly show that our approach outperforms the classic semi‐supervised Learning methods .%现实世界中存在着大量包含多种类型的对象和联系的异构信息网络,从中挖掘信息获取知识已成为当前的研究热点之一.基于图正则化的半监督学习在近年来得到了广泛的研究,然而,现有的半监督学习算法大都只能应用于同构网络.基于同构节点和异构节点的一致性假设,提出了任意结构的异构信息网络上的半监督学习的正则化分类函数,并得到分类函数的闭

  20. Semi-supervised weighted kernel clustering based on gravitational search for fault diagnosis.

    Science.gov (United States)

    Li, Chaoshun; Zhou, Jianzhong

    2014-09-01

    Supervised learning method, like support vector machine (SVM), has been widely applied in diagnosing known faults, however this kind of method fails to work correctly when new or unknown fault occurs. Traditional unsupervised kernel clustering can be used for unknown fault diagnosis, but it could not make use of the historical classification information to improve diagnosis accuracy. In this paper, a semi-supervised kernel clustering model is designed to diagnose known and unknown faults. At first, a novel semi-supervised weighted kernel clustering algorithm based on gravitational search (SWKC-GS) is proposed for clustering of dataset composed of labeled and unlabeled fault samples. The clustering model of SWKC-GS is defined based on wrong classification rate of labeled samples and fuzzy clustering index on the whole dataset. Gravitational search algorithm (GSA) is used to solve the clustering model, while centers of clusters, feature weights and parameter of kernel function are selected as optimization variables. And then, new fault samples are identified and diagnosed by calculating the weighted kernel distance between them and the fault cluster centers. If the fault samples are unknown, they will be added in historical dataset and the SWKC-GS is used to partition the mixed dataset and update the clustering results for diagnosing new fault. In experiments, the proposed method has been applied in fault diagnosis for rotatory bearing, while SWKC-GS has been compared not only with traditional clustering methods, but also with SVM and neural network, for known fault diagnosis. In addition, the proposed method has also been applied in unknown fault diagnosis. The results have shown effectiveness of the proposed method in achieving expected diagnosis accuracy for both known and unknown faults of rotatory bearing.

  1. Semi-Supervised Half-Quadratic Nonnegative Matrix Factorization for Face Recognition

    KAUST Repository

    Alghamdi, Masheal M.

    2014-05-01

    Face recognition is a challenging problem in computer vision. Difficulties such as slight differences between similar faces of different people, changes in facial expressions, light and illumination condition, and pose variations add extra complications to the face recognition research. Many algorithms are devoted to solving the face recognition problem, among which the family of nonnegative matrix factorization (NMF) algorithms has been widely used as a compact data representation method. Different versions of NMF have been proposed. Wang et al. proposed the graph-based semi-supervised nonnegative learning (S2N2L) algorithm that uses labeled data in constructing intrinsic and penalty graph to enforce separability of labeled data, which leads to a greater discriminating power. Moreover the geometrical structure of labeled and unlabeled data is preserved through using the smoothness assumption by creating a similarity graph that conserves the neighboring information for all labeled and unlabeled data. However, S2N2L is sensitive to light changes, illumination, and partial occlusion. In this thesis, we propose a Semi-Supervised Half-Quadratic NMF (SSHQNMF) algorithm that combines the benefits of S2N2L and the robust NMF by the half- quadratic minimization (HQNMF) algorithm.Our algorithm improves upon the S2N2L algorithm by replacing the Frobenius norm with a robust M-Estimator loss function. A multiplicative update solution for our SSHQNMF algorithmis driven using the half- 4 quadratic (HQ) theory. Extensive experiments on ORL, Yale-A and a subset of the PIE data sets for nine M-estimator loss functions for both SSHQNMF and HQNMF algorithms are investigated, and compared with several state-of-the-art supervised and unsupervised algorithms, along with the original S2N2L algorithm in the context of classification, clustering, and robustness against partial occlusion. The proposed algorithm outperformed the other algorithms. Furthermore, SSHQNMF with Maximum Correntropy

  2. Efficient dynamic graph construction for inductive semi-supervised learning.

    Science.gov (United States)

    Dornaika, F; Dahbi, R; Bosaghzadeh, A; Ruichek, Y

    2017-10-01

    Most of graph construction techniques assume a transductive setting in which the whole data collection is available at construction time. Addressing graph construction for inductive setting, in which data are coming sequentially, has received much less attention. For inductive settings, constructing the graph from scratch can be very time consuming. This paper introduces a generic framework that is able to make any graph construction method incremental. This framework yields an efficient and dynamic graph construction method that adds new samples (labeled or unlabeled) to a previously constructed graph. As a case study, we use the recently proposed Two Phase Weighted Regularized Least Square (TPWRLS) graph construction method. The paper has two main contributions. First, we use the TPWRLS coding scheme to represent new sample(s) with respect to an existing database. The representative coefficients are then used to update the graph affinity matrix. The proposed method not only appends the new samples to the graph but also updates the whole graph structure by discovering which nodes are affected by the introduction of new samples and by updating their edge weights. The second contribution of the article is the application of the proposed framework to the problem of graph-based label propagation using multiple observations for vision-based recognition tasks. Experiments on several image databases show that, without any significant loss in the accuracy of the final classification, the proposed dynamic graph construction is more efficient than the batch graph construction. Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. Semi-Supervised Projective Non-Negative Matrix Factorization for Cancer Classification.

    Directory of Open Access Journals (Sweden)

    Xiang Zhang

    Full Text Available Advances in DNA microarray technologies have made gene expression profiles a significant candidate in identifying different types of cancers. Traditional learning-based cancer identification methods utilize labeled samples to train a classifier, but they are inconvenient for practical application because labels are quite expensive in the clinical cancer research community. This paper proposes a semi-supervised projective non-negative matrix factorization method (Semi-PNMF to learn an effective classifier from both labeled and unlabeled samples, thus boosting subsequent cancer classification performance. In particular, Semi-PNMF jointly learns a non-negative subspace from concatenated labeled and unlabeled samples and indicates classes by the positions of the maximum entries of their coefficients. Because Semi-PNMF incorporates statistical information from the large volume of unlabeled samples in the learned subspace, it can learn more representative subspaces and boost classification performance. We developed a multiplicative update rule (MUR to optimize Semi-PNMF and proved its convergence. The experimental results of cancer classification for two multiclass cancer gene expression profile datasets show that Semi-PNMF outperforms the representative methods.

  4. MULTI-LABEL ASRS DATASET CLASSIFICATION USING SEMI-SUPERVISED SUBSPACE CLUSTERING

    Data.gov (United States)

    National Aeronautics and Space Administration — MULTI-LABEL ASRS DATASET CLASSIFICATION USING SEMI-SUPERVISED SUBSPACE CLUSTERING MOHAMMAD SALIM AHMED, LATIFUR KHAN, NIKUNJ OZA, AND MANDAVA RAJESWARI Abstract....

  5. TV-SVM: Total Variation Support Vector Machine for Semi-Supervised Data Classification

    OpenAIRE

    Bresson, Xavier; Zhang, Ruiliang

    2012-01-01

    We introduce semi-supervised data classification algorithms based on total variation (TV), Reproducing Kernel Hilbert Space (RKHS), support vector machine (SVM), Cheeger cut, labeled and unlabeled data points. We design binary and multi-class semi-supervised classification algorithms. We compare the TV-based classification algorithms with the related Laplacian-based algorithms, and show that TV classification perform significantly better when the number of labeled data is small.

  6. 基于半监督学习的变种群规模区间适应值交互式遗传算法%Interval-fitness interactive genetic algorithms with varying population size based on semi-supervised learning

    Institute of Scientific and Technical Information of China (English)

    孙晓燕; 任洁; 巩敦卫

    2011-01-01

    In order to alleviate user fatigue and improve the performances of interactive genetic algorithms (IGAs) in exploration, we present the interval-fitness interactive genetic algorithms with varying population size based on a co-training semi-supervised learning(CSSL). According to the clustering results of a large population, we develop the strategy for selecting unlabeled samples and labeled samples. Based on the approximation precision of two co-training learners, an efficient strategy for selecting high reliable unlabeled samples for labeling is given. Then, the CSSL mechanism is employed to train two radial basis function(RBF) neural networks in order to establish the surrogate model with high precision and good generalization ability. In the subsequent evolution, the surrogate model is used to estimate the fitness of an individual; in turn, the surrogate model is updated based on its estimation error. The proposed algorithm is analyzed and applied to a fashion evolutionary design system. The experimental results show its efficacy.%为了减轻用户疲劳并增强算法的搜索性能,本文在变种群规模交互式遗传算法的基础上引入协同训练半监督学习方法,提出基于半监督学习的变种群规模区间适应值交互式遗传算法.根据对大规模种群的聚类结果,给出标记样本和未标记样本的获取方法;结合半监督协同学习器逼近误差的改变,提出高可信度未标记样本的选择策略;采用半监督协同学习机制训练两个径向基函数(RBF)神经网络,构造精度高泛化能力强的代理模型;在进化过程中,利用代理模型估计大种群规模进化个体适应值,并根据估计偏差更新代理模型.算法的理论分析及其在服装进化设计系统中的应用结果说明了算法的有效性.

  7. Detection of facilities in satellite imagery using semi-supervised image classification and auxiliary contextual observables

    Energy Technology Data Exchange (ETDEWEB)

    Harvey, Neal R [Los Alamos National Laboratory; Ruggiero, Christy E [Los Alamos National Laboratory; Pawley, Norma H [Los Alamos National Laboratory; Brumby, Steven P [Los Alamos National Laboratory; Macdonald, Brian [Los Alamos National Laboratory; Balick, Lee [Los Alamos National Laboratory; Oyer, Alden [Los Alamos National Laboratory

    2009-01-01

    Detecting complex targets, such as facilities, in commercially available satellite imagery is a difficult problem that human analysts try to solve by applying world knowledge. Often there are known observables that can be extracted by pixel-level feature detectors that can assist in the facility detection process. Individually, each of these observables is not sufficient for an accurate and reliable detection, but in combination, these auxiliary observables may provide sufficient context for detection by a machine learning algorithm. We describe an approach for automatic detection of facilities that uses an automated feature extraction algorithm to extract auxiliary observables, and a semi-supervised assisted target recognition algorithm to then identify facilities of interest. We illustrate the approach using an example of finding schools in Quickbird image data of Albuquerque, New Mexico. We use Los Alamos National Laboratory's Genie Pro automated feature extraction algorithm to find a set of auxiliary features that should be useful in the search for schools, such as parking lots, large buildings, sports fields and residential areas and then combine these features using Genie Pro's assisted target recognition algorithm to learn a classifier that finds schools in the image data.

  8. Machinery running state identification based on discriminant semi-supervised local tangent space alignment for feature fusion and extraction

    Science.gov (United States)

    Su, Zuqiang; Xiao, Hong; Zhang, Yi; Tang, Baoping; Jiang, Yonghua

    2017-04-01

    Extraction of sensitive features is a challenging but key task in data-driven machinery running state identification. Aimed at solving this problem, a method for machinery running state identification that applies discriminant semi-supervised local tangent space alignment (DSS-LTSA) for feature fusion and extraction is proposed. Firstly, in order to extract more distinct features, the vibration signals are decomposed by wavelet packet decomposition WPD, and a mixed-domain feature set consisted of statistical features, autoregressive (AR) model coefficients, instantaneous amplitude Shannon entropy and WPD energy spectrum is extracted to comprehensively characterize the properties of machinery running state(s). Then, the mixed-dimension feature set is inputted into DSS-LTSA for feature fusion and extraction to eliminate redundant information and interference noise. The proposed DSS-LTSA can extract intrinsic structure information of both labeled and unlabeled state samples, and as a result the over-fitting problem of supervised manifold learning and blindness problem of unsupervised manifold learning are overcome. Simultaneously, class discrimination information is integrated within the dimension reduction process in a semi-supervised manner to improve sensitivity of the extracted fusion features. Lastly, the extracted fusion features are inputted into a pattern recognition algorithm to achieve the running state identification. The effectiveness of the proposed method is verified by a running state identification case in a gearbox, and the results confirm the improved accuracy of the running state identification.

  9. Semi-supervised classification of emotional pictures based on feature combination

    Science.gov (United States)

    Li, Shuo; Zhang, Yu-Jin

    2011-02-01

    Can the abundant emotions reflected in pictures be classified automatically by computer? Only the visual features extracted from images are considered in the previous researches, which have the constrained capability to reveal various emotions. In addition, the training database utilized by previous methods is the subset of International Affective Picture System (IAPS) that has a relatively small scale, which exerts negative effects on the discrimination of emotion classifiers. To solve the above problems, this paper proposes a novel and practical emotional picture classification approach, using semi-supervised learning scheme with both visual feature and keyword tag information. Besides the IAPS with both emotion labels and keyword tags as part of the training dataset, nearly 2000 pictures with only keyword tags that are downloaded from the website Flickr form an auxiliary training dataset. The visual feature of the latent emotional semantic factors is extracted by probabilistic Latent Semantic Analysis (pLSA) model, while the text feature is described by binary vectors on the tag vocabulary. A first Linear Programming Boost (LPBoost) classifier which is trained on the samples from IAPS combines the above two features, and aims to label the other training samples from the internet. Then the second SVM classifier which is trained on all training images using only visual feature, focuses on the test images. In the experiment, the categorization performance of our approach is better than the latest methods.

  10. Semi-supervised vibration-based classification and condition monitoring of compressors

    Science.gov (United States)

    Potočnik, Primož; Govekar, Edvard

    2017-09-01

    Semi-supervised vibration-based classification and condition monitoring of the reciprocating compressors installed in refrigeration appliances is proposed in this paper. The method addresses the problem of industrial condition monitoring where prior class definitions are often not available or difficult to obtain from local experts. The proposed method combines feature extraction, principal component analysis, and statistical analysis for the extraction of initial class representatives, and compares the capability of various classification methods, including discriminant analysis (DA), neural networks (NN), support vector machines (SVM), and extreme learning machines (ELM). The use of the method is demonstrated on a case study which was based on industrially acquired vibration measurements of reciprocating compressors during the production of refrigeration appliances. The paper presents a comparative qualitative analysis of the applied classifiers, confirming the good performance of several nonlinear classifiers. If the model parameters are properly selected, then very good classification performance can be obtained from NN trained by Bayesian regularization, SVM and ELM classifiers. The method can be effectively applied for the industrial condition monitoring of compressors.

  11. Modeling electroencephalography waveforms with semi-supervised deep belief nets: fast classification and anomaly measurement

    Science.gov (United States)

    Wulsin, D. F.; Gupta, J. R.; Mani, R.; Blanco, J. A.; Litt, B.

    2011-06-01

    Clinical electroencephalography (EEG) records vast amounts of human complex data yet is still reviewed primarily by human readers. Deep belief nets (DBNs) are a relatively new type of multi-layer neural network commonly tested on two-dimensional image data but are rarely applied to times-series data such as EEG. We apply DBNs in a semi-supervised paradigm to model EEG waveforms for classification and anomaly detection. DBN performance was comparable to standard classifiers on our EEG dataset, and classification time was found to be 1.7-103.7 times faster than the other high-performing classifiers. We demonstrate how the unsupervised step of DBN learning produces an autoencoder that can naturally be used in anomaly measurement. We compare the use of raw, unprocessed data—a rarity in automated physiological waveform analysis—with hand-chosen features and find that raw data produce comparable classification and better anomaly measurement performance. These results indicate that DBNs and raw data inputs may be more effective for online automated EEG waveform recognition than other common techniques.

  12. Semi-supervised hyperspectral classification from a small number of training samples using a co-training approach

    Science.gov (United States)

    Romaszewski, Michał; Głomb, Przemysław; Cholewa, Michał

    2016-11-01

    We present a novel semi-supervised algorithm for classification of hyperspectral data from remote sensors. Our method is inspired by the Tracking-Learning-Detection (TLD) framework, originally applied for tracking objects in a video stream. TLD introduced the co-training approach called P-N learning, making use of two independent 'experts' (or learners) that scored samples in different feature spaces. In a similar fashion, we formulated the hyperspectral classification task as a co-training problem, that can be solved with the P-N learning scheme. Our method uses both spatial and spectral features of data, extending a small set of initial labelled samples during the process of region growing. We show that this approach is stable and achieves very good accuracy even for small training sets. We analyse the algorithm's performance on several publicly available hyperspectral data sets.

  13. A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images

    Directory of Open Access Journals (Sweden)

    Bin Hou

    2016-08-01

    Full Text Available Characterizations of up to date information of the Earth’s surface are an important application providing insights to urban planning, resources monitoring and environmental studies. A large number of change detection (CD methods have been developed to solve them by utilizing remote sensing (RS images. The advent of high resolution (HR remote sensing images further provides challenges to traditional CD methods and opportunities to object-based CD methods. While several kinds of geospatial objects are recognized, this manuscript mainly focuses on buildings. Specifically, we propose a novel automatic approach combining pixel-based strategies with object-based ones for detecting building changes with HR remote sensing images. A multiresolution contextual morphological transformation called extended morphological attribute profiles (EMAPs allows the extraction of geometrical features related to the structures within the scene at different scales. Pixel-based post-classification is executed on EMAPs using hierarchical fuzzy clustering. Subsequently, the hierarchical fuzzy frequency vector histograms are formed based on the image-objects acquired by simple linear iterative clustering (SLIC segmentation. Then, saliency and morphological building index (MBI extracted on difference images are used to generate a pseudo training set. Ultimately, object-based semi-supervised classification is implemented on this training set by applying random forest (RF. Most of the important changes are detected by the proposed method in our experiments. This study was checked for effectiveness using visual evaluation and numerical evaluation.

  14. A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images.

    Science.gov (United States)

    Hou, Bin; Wang, Yunhong; Liu, Qingjie

    2016-08-27

    Characterizations of up to date information of the Earth's surface are an important application providing insights to urban planning, resources monitoring and environmental studies. A large number of change detection (CD) methods have been developed to solve them by utilizing remote sensing (RS) images. The advent of high resolution (HR) remote sensing images further provides challenges to traditional CD methods and opportunities to object-based CD methods. While several kinds of geospatial objects are recognized, this manuscript mainly focuses on buildings. Specifically, we propose a novel automatic approach combining pixel-based strategies with object-based ones for detecting building changes with HR remote sensing images. A multiresolution contextual morphological transformation called extended morphological attribute profiles (EMAPs) allows the extraction of geometrical features related to the structures within the scene at different scales. Pixel-based post-classification is executed on EMAPs using hierarchical fuzzy clustering. Subsequently, the hierarchical fuzzy frequency vector histograms are formed based on the image-objects acquired by simple linear iterative clustering (SLIC) segmentation. Then, saliency and morphological building index (MBI) extracted on difference images are used to generate a pseudo training set. Ultimately, object-based semi-supervised classification is implemented on this training set by applying random forest (RF). Most of the important changes are detected by the proposed method in our experiments. This study was checked for effectiveness using visual evaluation and numerical evaluation.

  15. Semi-supervised segmentation of multispectral remote sensing image based on spectral clustering

    Science.gov (United States)

    Zhang, Xiangrong; Wang, Ting; Jiao, Licheng; Yang, Chun

    2009-10-01

    In this paper, a new multi-spectral remote sensing image segmentation method based on multi-parameter semi-supervised spectral clustering (STS3C) is proposed. Two types of instance-level constraints: must-link and cannot-link are incorporated into spectral cluster to construct semi-supervised spectral clustering in which the self-tuning parameter is applied to avoid the selection of the scaling parameter. Further, when STS3C is applied to multi-spectral remote sensing image segmentation, the uniform sampling technique combined with nearest neighbor rule is used to reduce the computation complexity. Segmentation results show that STS3C outperforms the semi-supervised spectral clustering with fixed parameter and the well-known clustering methods including k-means and FCM in multi-spectral remote sensing image segmentation.

  16. SAR image segmentation with entropy ranking based adaptive semi-supervised spectral clustering

    Science.gov (United States)

    Zhang, Xiangrong; Yang, Jie; Hou, Biao; Jiao, Licheng

    2010-10-01

    Spectral clustering has become one of the most popular modern clustering algorithms in recent years. In this paper, a new algorithm named entropy ranking based adaptive semi-supervised spectral clustering for SAR image segmentation is proposed. We focus not only on finding a suitable scaling parameter but also determining automatically the cluster number with the entropy ranking theory. Also, two kinds of constrains must-link and cannot-link based semi-supervised spectral clustering is applied to gain better segmentation results. Experimental results on SAR images show that the proposed method outperforms other spectral clustering algorithms.

  17. Semi-supervised adaptation in ssvep-based brain-computer interface using tri-training

    DEFF Research Database (Denmark)

    Bender, Thomas; Kjaer, Troels W.; Thomsen, Carsten E.;

    2013-01-01

    This paper presents a novel and computationally simple tri-training based semi-supervised steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). It is implemented with autocorrelation-based features and a Naïve-Bayes classifier (NBC). The system uses nine characters...

  18. Auxiliary Deep Generative Models

    OpenAIRE

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

    2016-01-01

    Deep generative models parameterized by neural networks have recently achieved state-of-the-art performance in unsupervised and semi-supervised learning. We extend deep generative models with auxiliary variables which improves the variational approximation. The auxiliary variables leave the generative model unchanged but make the variational distribution more expressive. Inspired by the structure of the auxiliary variable we also propose a model with two stochastic layers and skip connections...

  19. A Semi-supervised Heat Kernel Pagerank MBO Algorithm for Data Classification

    Science.gov (United States)

    2016-07-01

    closed-form expression for the class of each node is derived. Moreover, the authors of [50] describe a semi-supervised method for classifying data using...manifold smoothing and image denoising. In addition to image processing, methods in- volving spectral graph theory [17,56], based on a graphical setting...pagerank and Section 3 presents a model using heat kernel pagerank directly as a classifier . Section 4 formulates the new algorithm as well as provides

  20. A semi-supervised approach uncovers thousands of intragenic enhancers differentially activated in human cells

    OpenAIRE

    2015-01-01

    Background Transcriptional enhancers are generally known to regulate gene transcription from afar. Their activation involves a series of changes in chromatin marks and recruitment of protein factors. These enhancers may also occur inside genes, but how many may be active in human cells and their effects on the regulation of the host gene remains unclear. Results We describe a novel semi-supervised method based on the relative enrichment of chromatin signals between 2 conditions to predict act...

  1. Vinayaka : A Semi-Supervised Projected Clustering Method Using Differential Evolution

    OpenAIRE

    Satish Gajawada; Durga Toshniwal

    2012-01-01

    Differential Evolution (DE) is an algorithm for evolutionary optimization. Clustering problems have beensolved by using DE based clustering methods but these methods may fail to find clusters hidden insubspaces of high dimensional datasets. Subspace and projected clustering methods have been proposed inliterature to find subspace clusters that are present in subspaces of dataset. In this paper we proposeVINAYAKA, a semi-supervised projected clustering method based on DE. In this method DE opt...

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

    OpenAIRE

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

    2012-01-01

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

  3. Auxiliary Deep Generative Models

    DEFF Research Database (Denmark)

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

    2016-01-01

    Deep generative models parameterized by neural networks have recently achieved state-of-the-art performance in unsupervised and semi-supervised learning. We extend deep generative models with auxiliary variables which improves the variational approximation. The auxiliary variables leave...... the generative model unchanged but make the variational distribution more expressive. Inspired by the structure of the auxiliary variable we also propose a model with two stochastic layers and skip connections. Our findings suggest that more expressive and properly specified deep generative models converge...... faster with better results. We show state-of-the-art performance within semi-supervised learning on MNIST (0.96%), SVHN (16.61%) and NORB (9.40%) datasets....

  4. Auxiliary Deep Generative Models

    DEFF Research Database (Denmark)

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

    2016-01-01

    Deep generative models parameterized by neural networks have recently achieved state-of-the-art performance in unsupervised and semi-supervised learning. We extend deep generative models with auxiliary variables which improves the variational approximation. The auxiliary variables leave...... the generative model unchanged but make the variational distribution more expressive. Inspired by the structure of the auxiliary variable we also propose a model with two stochastic layers and skip connections. Our findings suggest that more expressive and properly specified deep generative models converge...... faster with better results. We show state-of-the-art performance within semi-supervised learning on MNIST (0.96%), SVHN (16.61%) and NORB (9.40%) datasets....

  5. Semi-Supervised Classification based on Gaussian Mixture Model for remote imagery

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    Semi-Supervised Classification (SSC),which makes use of both labeled and unlabeled data to determine classification borders in feature space,has great advantages in extracting classification information from mass data.In this paper,a novel SSC method based on Gaussian Mixture Model (GMM) is proposed,in which each class’s feature space is described by one GMM.Experiments show the proposed method can achieve high classification accuracy with small amount of labeled data.However,for the same accuracy,supervised classification methods such as Support Vector Machine,Object Oriented Classification,etc.should be provided with much more labeled data.

  6. Semi-supervised adaptation in ssvep-based brain-computer interface using tri-training

    DEFF Research Database (Denmark)

    Bender, Thomas; Kjaer, Troels W.; Thomsen, Carsten E.

    2013-01-01

    This paper presents a novel and computationally simple tri-training based semi-supervised steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). It is implemented with autocorrelation-based features and a Naïve-Bayes classifier (NBC). The system uses nine characters...... presented on a 100 Hz CRT-monitor, three scalp electrodes for signal acquisition, a gUSB-amp for preamplification and two PCs for data-processing and stimulus control respectively. Preliminary test results of the system on nine healthy subjects, with and without tri-training, indicates that the accuracy...... improves as a result of tri-training....

  7. Summarizing Relational Data Using Semi-Supervised Genetic Algorithm-Based Clustering Techniques

    Directory of Open Access Journals (Sweden)

    Rayner Alfred

    2010-01-01

    Full Text Available Problem statement: In solving a classification problem in relational data mining, traditional methods, for example, the C4.5 and its variants, usually require data transformations from datasets stored in multiple tables into a single table. Unfortunately, we may loss some information when we join tables with a high degree of one-to-many association. Therefore, data transformation becomes a tedious trial-and-error work and the classification result is often not very promising especially when the number of tables and the degree of one-to-many association are large. Approach: We proposed a genetic semi-supervised clustering technique as a means of aggregating data stored in multiple tables to facilitate the task of solving a classification problem in relational database. This algorithm is suitable for classification of datasets with a high degree of one-to-many associations. It can be used in two ways. One is user-controlled clustering, where the user may control the result of clustering by varying the compactness of the spherical cluster. The other is automatic clustering, where a non-overlap clustering strategy is applied. In this study, we use the latter method to dynamically cluster multiple instances, as a means of aggregating them and illustrate the effectiveness of this method using the semi-supervised genetic algorithm-based clustering technique. Results: It was shown in the experimental results that using the reciprocal of Davies-Bouldin Index for cluster dispersion and the reciprocal of Gini Index for cluster purity, as the fitness function in the Genetic Algorithm (GA, finds solutions with much greater accuracy. The results obtained in this study showed that automatic clustering (seeding, by optimizing the cluster dispersion or cluster purity alone using GA, provides one with good results compared to the traditional k-means clustering. However, the best result can be achieved by optimizing the combination values of both the cluster

  8. Semi-supervised classification of remote sensing image based on probabilistic topic model%利用概率主题模型的遥感影像半监督分类

    Institute of Scientific and Technical Information of China (English)

    易文斌; 冒亚明; 慎利

    2013-01-01

    Land cover is the center of the interaction of the natural environment and human activities and the acquisition of land cover information are obtained through the classification of remote sensing images, so the image classification is one of the most basic issues of remote sensing image analysis. Based on the image clustering analysis of high-resolution remote sensing image through the probabilistic topic model, the generated model which is a typical method in the semi-supervised learning is analyzed and a classification method based on probabilistic topic model and semi-supervised learning(SS-LDA)is formed in the paper. The process of SS-LDA model used in the text recognition applications is relearned and a basic image classification process of high-resolution remote sensing image is constructed. Comparing to traditional unsupervised classification and supervised classi-fication algorithm, the SS-LDA algorithm will get more accuracy of image classification results through experiments.%  土地覆盖是自然环境与人类活动相互作用的中心,而土地覆盖信息主要是通过遥感影像分类来获取,因此影像分类是遥感影像分析的最基本问题之一。在参考基于概率主题模型的高分辨率遥感影像聚类分析的基础上,通过半监督学习最典型的生成模型方法引出了基于概率主题模型的半监督分类(SS-LDA)算法。借鉴SS-LDA模型在文本识别应用的流程,构建了基于SS-LDA算法的高分辨率遥感影像分类的基本流程。通过实验证明,相对于传统的非监督分类与监督分类算法,SS-LDA算法能够获取较高精度的影像分类结果。

  9. Sparse dimensionality reduction of hyperspectral image based on semi-supervised local Fisher discriminant analysis

    Science.gov (United States)

    Shao, Zhenfeng; Zhang, Lei

    2014-09-01

    This paper presents a novel sparse dimensionality reduction method of hyperspectral image based on semi-supervised local Fisher discriminant analysis (SELF). The proposed method is designed to be especially effective for dealing with the out-of-sample extrapolation to realize advantageous complementarities between SELF and sparsity preserving projections (SPP). Compared to SELF and SPP, the method proposed herein offers highly discriminative ability and produces an explicit nonlinear feature mapping for the out-of-sample extrapolation. This is due to the fact that the proposed method can get an explicit feature mapping for dimensionality reduction and improve the classification performance of classifiers by performing dimensionality reduction. Experimental analysis on the sparsity and efficacy of low dimensional outputs shows that, sparse dimensionality reduction based on SELF can yield good classification results and interpretability in the field of hyperspectral remote sensing.

  10. HCsnip: An R Package for Semi-supervised Snipping of the Hierarchical Clustering Tree.

    Science.gov (United States)

    Obulkasim, Askar; van de Wiel, Mark A

    2015-01-01

    Hierarchical clustering (HC) is one of the most frequently used methods in computational biology in the analysis of high-dimensional genomics data. Given a data set, HC outputs a binary tree leaves of which are the data points and internal nodes represent clusters of various sizes. Normally, a fixed-height cut on the HC tree is chosen, and each contiguous branch of data points below that height is considered as a separate cluster. However, the fixed-height branch cut may not be ideal in situations where one expects a complicated tree structure with nested clusters. Furthermore, due to lack of utilization of related background information in selecting the cutoff, induced clusters are often difficult to interpret. This paper describes a novel procedure that aims to automatically extract meaningful clusters from the HC tree in a semi-supervised way. The procedure is implemented in the R package HCsnip available from Bioconductor. Rather than cutting the HC tree at a fixed-height, HCsnip probes the various way of snipping, possibly at variable heights, to tease out hidden clusters ensconced deep down in the tree. The cluster extraction process utilizes, along with the data set from which the HC tree is derived, commonly available background information. Consequently, the extracted clusters are highly reproducible and robust against various sources of variations that "haunted" high-dimensional genomics data. Since the clustering process is guided by the background information, clusters are easy to interpret. Unlike existing packages, no constraint is placed on the data type on which clustering is desired. Particularly, the package accepts patient follow-up data for guiding the cluster extraction process. To our knowledge, HCsnip is the first package that is able to decomposes the HC tree into clusters with piecewise snipping under the guidance of patient time-to-event information. Our implementation of the semi-supervised HC tree snipping framework is generic, and can

  11. Sparse Markov chain-based semi-supervised multi-instance multi-label method for protein function prediction.

    Science.gov (United States)

    Han, Chao; Chen, Jian; Wu, Qingyao; Mu, Shuai; Min, Huaqing

    2015-10-01

    Automated assignment of protein function has received considerable attention in recent years for genome-wide study. With the rapid accumulation of genome sequencing data produced by high-throughput experimental techniques, the process of manually predicting functional properties of proteins has become increasingly cumbersome. Such large genomics data sets can only be annotated computationally. However, automated assignment of functions to unknown protein is challenging due to its inherent difficulty and complexity. Previous studies have revealed that solving problems involving complicated objects with multiple semantic meanings using the multi-instance multi-label (MIML) framework is effective. For the protein function prediction problems, each protein object in nature may associate with distinct structural units (instances) and multiple functional properties (class labels) where each unit is described by an instance and each functional property is considered as a class label. Thus, it is convenient and natural to tackle the protein function prediction problem by using the MIML framework. In this paper, we propose a sparse Markov chain-based semi-supervised MIML method, called Sparse-Markov. A sparse transductive probability graph is constructed to encode the affinity information of the data based on ensemble of Hausdorff distance metrics. Our goal is to exploit the affinity between protein objects in the sparse transductive probability graph to seek a sparse steady state probability of the Markov chain model to do protein function prediction, such that two proteins are given similar functional labels if they are close to each other in terms of an ensemble Hausdorff distance in the graph. Experimental results on seven real-world organism data sets covering three biological domains show that our proposed Sparse-Markov method is able to achieve better performance than four state-of-the-art MIML learning algorithms.

  12. Semi-supervised segmentation of ultrasound images based on patch representation and continuous min cut.

    Directory of Open Access Journals (Sweden)

    Anca Ciurte

    Full Text Available Ultrasound segmentation is a challenging problem due to the inherent speckle and some artifacts like shadows, attenuation and signal dropout. Existing methods need to include strong priors like shape priors or analytical intensity models to succeed in the segmentation. However, such priors tend to limit these methods to a specific target or imaging settings, and they are not always applicable to pathological cases. This work introduces a semi-supervised segmentation framework for ultrasound imaging that alleviates the limitation of fully automatic segmentation, that is, it is applicable to any kind of target and imaging settings. Our methodology uses a graph of image patches to represent the ultrasound image and user-assisted initialization with labels, which acts as soft priors. The segmentation problem is formulated as a continuous minimum cut problem and solved with an efficient optimization algorithm. We validate our segmentation framework on clinical ultrasound imaging (prostate, fetus, and tumors of the liver and eye. We obtain high similarity agreement with the ground truth provided by medical expert delineations in all applications (94% DICE values in average and the proposed algorithm performs favorably with the literature.

  13. Semi-supervised segmentation of ultrasound images based on patch representation and continuous min cut.

    Science.gov (United States)

    Ciurte, Anca; Bresson, Xavier; Cuisenaire, Olivier; Houhou, Nawal; Nedevschi, Sergiu; Thiran, Jean-Philippe; Cuadra, Meritxell Bach

    2014-01-01

    Ultrasound segmentation is a challenging problem due to the inherent speckle and some artifacts like shadows, attenuation and signal dropout. Existing methods need to include strong priors like shape priors or analytical intensity models to succeed in the segmentation. However, such priors tend to limit these methods to a specific target or imaging settings, and they are not always applicable to pathological cases. This work introduces a semi-supervised segmentation framework for ultrasound imaging that alleviates the limitation of fully automatic segmentation, that is, it is applicable to any kind of target and imaging settings. Our methodology uses a graph of image patches to represent the ultrasound image and user-assisted initialization with labels, which acts as soft priors. The segmentation problem is formulated as a continuous minimum cut problem and solved with an efficient optimization algorithm. We validate our segmentation framework on clinical ultrasound imaging (prostate, fetus, and tumors of the liver and eye). We obtain high similarity agreement with the ground truth provided by medical expert delineations in all applications (94% DICE values in average) and the proposed algorithm performs favorably with the literature.

  14. spa: Semi-Supervised Semi-Parametric Graph-Based Estimation in R

    Directory of Open Access Journals (Sweden)

    Mark Culp

    2011-04-01

    Full Text Available In this paper, we present an R package that combines feature-based (X data and graph-based (G data for prediction of the response Y . In this particular case, Y is observed for a subset of the observations (labeled and missing for the remainder (unlabeled. We examine an approach for fitting Y = Xβ + f(G where β is a coefficient vector and f is a function over the vertices of the graph. The procedure is semi-supervised in nature (trained on the labeled and unlabeled sets, requiring iterative algorithms for fitting this estimate. The package provides several key functions for fitting and evaluating an estimator of this type. The package is illustrated on a text analysis data set, where the observations are text documents (papers, the response is the category of paper (either applied or theoretical statistics, the X information is the name of the journal in which the paper resides, and the graph is a co-citation network, with each vertex an observation and each edge the number of times that the two papers cite a common paper. An application involving classification of protein location using a protein interaction graph and an application involving classification on a manifold with part of the feature data converted to a graph are also presented.

  15. Indoor Localization Using Semi-Supervised Manifold Alignment with Dimension Expansion

    Directory of Open Access Journals (Sweden)

    Qiao Zhang

    2016-11-01

    Full Text Available Location estimation plays a crucial role in Location-Based Services (LBSs with satisfactory user experience. The Wireless Local Area Network (WLAN localization approach is preferred as a cost-efficient solution to indoor localization on account of the widely-deployed WLAN infrastructures. In this paper, we propose a new WLAN Received Signal Strength (RSS-based indoor localization approach using the semi-supervised manifold alignment with dimension expansion. In concrete terms, we first construct an innovative objective function based on the augmented physical coordinates and the corresponding WLAN RSS measurements. Second, the closed-form solution to the objective function is derived out according to the Lagrange multiplier equation, which results in the manifold in physical coordinate space. Third, the target location is estimated by matching the transformed newly-collected RSS against the manifold. The localization performance with noise perturbation is analyzed upon the constructed objective function, and meanwhile, the closed-form solution to the objective function with respect to multiple types of measurements is also derived out for the sake of leveraging all of the potential measurements for indoor localization. The extensive testing results show that the proposed approach performs well in localization accuracy even at low calibration load, and its performance can be further improved by using multiple types of measurements for localization.

  16. Auxiliary Deep Generative Models

    DEFF Research Database (Denmark)

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

    2016-01-01

    Deep generative models parameterized by neural networks have recently achieved state-of-the-art performance in unsupervised and semi-supervised learning. We extend deep generative models with auxiliary variables which improves the variational approximation. The auxiliary variables leave...... the generative model unchanged but make the variational distribution more expressive. Inspired by the structure of the auxiliary variable we also propose a model with two stochastic layers and skip connections. Our findings suggest that more expressive and properly specified deep generative models converge...

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

    Directory of Open Access Journals (Sweden)

    Pan Shao

    2016-03-01

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

  18. Learning with partially labeled and interdependent data

    CERN Document Server

    Amini, Massih-Reza

    2015-01-01

    This book develops two key machine learning principles: the semi-supervised paradigm and learning with interdependent data. It reveals new applications, primarily web related, that transgress the classical machine learning framework through learning with interdependent data. The book traces how the semi-supervised paradigm and the learning to rank paradigm emerged from new web applications, leading to a massive production of heterogeneous textual data. It explains how semi-supervised learning techniques are widely used, but only allow a limited analysis of the information content and thus d

  19. Learning to Generate Networks

    CERN Document Server

    Atwood, James

    2014-01-01

    The recent explosion in social network data has stimulated interest in probabilistic models of networks. Such models are appealing because they are empirically grounded; in contrast to more traditional network models, their parameters are estimated from data, and the models are evaluated on how well they represent the data. The exponential random graph model (ERGM, or, alternatively $p^*$) is currently the dominant framework for probabilistic network modeling. Despite their popularity, ERGMs suffer from a very serious flaw: near degeneracy. Briefly, an ERGM fit to a network or set of networks often ends up generating networks that look nothing at all like the training data. It is deeply troubling that the most likely model will generate instances that look nothing like data, and this calls the validity of models into question. In this work, we seek to address the general problem of learning to generate networks that do look like data. This is a large, challenging problem. To gain an understanding, we decompos...

  20. Workplace Learning and Generation X.

    Science.gov (United States)

    Bova, Breda; Kroth, Michael

    2001-01-01

    A survey of the learning preferences of 197 Generation X workers found that they value incidental and action learning. They recognized the need for formal training, but suggested improvements. They preferred learning by doing, visual stimuli, and self-directed learning. (Contains 26 references.) (SK)

  1. Semi-Supervised Clustering for High-Dimensional and Sparse Features

    Science.gov (United States)

    Yan, Su

    2010-01-01

    Clustering is one of the most common data mining tasks, used frequently for data organization and analysis in various application domains. Traditional machine learning approaches to clustering are fully automated and unsupervised where class labels are unknown a priori. In real application domains, however, some "weak" form of side…

  2. A Semi-Supervised WLAN Indoor Localization Method Based on ℓ1-Graph Algorithm

    Institute of Scientific and Technical Information of China (English)

    Liye Zhang; Lin Ma; Yubin Xu

    2015-01-01

    For indoor location estimation based on received signal strength ( RSS ) in wireless local area networks ( WLAN) , in order to reduce the influence of noise on the positioning accuracy, a large number of RSS should be collected in offline phase. Therefore, collecting training data with positioning information is time consuming which becomes the bottleneck of WLAN indoor localization. In this paper, the traditional semi⁃supervised learning method based on k⁃NN andε⁃NN graph for reducing collection workload of offline phase are analyzed, and the result shows that the k⁃NN or ε⁃NN graph are sensitive to data noise, which limit the performance of semi⁃supervised learning WLAN indoor localization system. Aiming at the above problem, it proposes a ℓ1⁃graph⁃algorithm⁃based semi⁃supervised learning ( LG⁃SSL) indoor localization method in which the graph is built by ℓ1⁃norm algorithm. In our system, it firstly labels the unlabeled data using LG⁃SSL and labeled data to build the Radio Map in offline training phase, and then uses LG⁃SSL to estimate user’ s location in online phase. Extensive experimental results show that, benefit from the robustness to noise and sparsity ofℓ1⁃graph, LG⁃SSL exhibits superior performance by effectively reducing the collection workload in offline phase and improving localization accuracy in online phase.

  3. Semi-supervised binary classification algorithm based on global and local regularization%结合全局和局部正则化的半监督二分类算法

    Institute of Scientific and Technical Information of China (English)

    吕佳

    2012-01-01

    As for semi-supervised classification problem, it is difficult to obtain a good classification function for the entire input space if global learning is used alone, while if local learning is utilized alone, a good classification function on some specified regions of the input space can be got. Accordingly, a new semi-supervised binary classification algorithm based on a mixed local and global regularization was presented in this paper. The algorithm integrated the benefits of global regularizer and local regularizes Global regularizer was built to smooth the class labels of the data so as to lessen insufficient training of local regularizer, and based upon the neighboring region, local regularizer was constructed to make class label of each data have the desired property, thus the objective function of semi-supervised binary classification problem was constructed. Comparative semi-supervised binary classification experiments on some benchmark datasets validate that the average classification accuracy and the standard error of the proposed algorithm are obviously superior to other algorithms.%针对在半监督分类问题中单独使用全局学习容易出现的在整个输入空间中较难获得一个优良的决策函数的问题,以及单独使用局部学习可在特定的局部区域内习得较好的决策函数的特点,提出了一种结合全局和局部正则化的半监督二分类算法.该算法综合全局正则项和局部正则项的优点,基于先验知识构建的全局正则项能平滑样本的类标号以避免局部正则项学习不充分的问题,通过基于局部邻域内样本信息构建的局部正则项使得每个样本的类标号具有理想的特性,从而构造出半监督二分类问题的目标函数.通过在标准二类数据集上的实验,结果表明所提出的算法其平均分类正确率和标准误差均优于基于拉普拉斯正则项方法、基于正则化拉普拉斯正则项方法和基于局部学习正则项方法.

  4. A semi-supervised support vector machine approach for parameter setting in motor imagery-based brain computer interfaces

    Science.gov (United States)

    Long, Jinyi; Yu, Zhuliang

    2010-01-01

    Parameter setting plays an important role for improving the performance of a brain computer interface (BCI). Currently, parameters (e.g. channels and frequency band) are often manually selected. It is time-consuming and not easy to obtain an optimal combination of parameters for a BCI. In this paper, motor imagery-based BCIs are considered, in which channels and frequency band are key parameters. First, a semi-supervised support vector machine algorithm is proposed for automatically selecting a set of channels with given frequency band. Next, this algorithm is extended for joint channel-frequency selection. In this approach, both training data with labels and test data without labels are used for training a classifier. Hence it can be used in small training data case. Finally, our algorithms are applied to a BCI competition data set. Our data analysis results show that these algorithms are effective for selection of frequency band and channels when the training data set is small. PMID:21886673

  5. Semi-Supervised Single- and Multi-Domain Regression with Multi-Domain Training

    CERN Document Server

    Michaeli, Tomer; Sapiro, Guillermo

    2012-01-01

    We address the problems of multi-domain and single-domain regression based on distinct and unpaired labeled training sets for each of the domains and a large unlabeled training set from all domains. We formulate these problems as a Bayesian estimation with partial knowledge of statistical relations. We propose a worst-case design strategy and study the resulting estimators. Our analysis explicitly accounts for the cardinality of the labeled sets and includes the special cases in which one of the labeled sets is very large or, in the other extreme, completely missing. We demonstrate our estimators in the context of removing expressions from facial images and in the context of audio-visual word recognition, and provide comparisons to several recently proposed multi-modal learning algorithms.

  6. Semi-Supervised Clustering Fingerprint Positioning Algorithm Based on Distance Constraints

    Institute of Scientific and Technical Information of China (English)

    Ying Xia; Zhongzhao Zhang; Lin Ma; Yao Wang

    2015-01-01

    With the rapid development of WLAN ( Wireless Local Area Network ) technology, an important target of indoor positioning systems is to improve the positioning accuracy while reducing the online computation. In this paper, it proposes a novel fingerprint positioning algorithm known as semi⁃supervised affinity propagation clustering based on distance function constraints. We show that by employing affinity propagation techniques, it is able to use a fractional labeled data to adjust similarity matrix of signal space to cluster reference points with high accuracy. The semi⁃supervised APC uses a combination of machine learning, clustering analysis and fingerprinting algorithm. By collecting data and testing our algorithm in a realistic indoor WLAN environment, the experimental results indicate that the proposed algorithm can improve positioning accuracy while reduce the online localization computation, as compared with the widely used K nearest neighbor and maximum likelihood estimation algorithms.

  7. Generation Z, Meet Cooperative Learning

    Science.gov (United States)

    Igel, Charles; Urquhart, Vicki

    2012-01-01

    Today's Generation Z teens need to develop teamwork and social learning skills to be successful in the 21st century workplace. Teachers can help students develop these skills and enhance academic achievement by implementing cooperative learning strategies. Three key principles for successful cooperative learning are discussed. (Contains 1 figure.)

  8. Generation Z, Meet Cooperative Learning

    Science.gov (United States)

    Igel, Charles; Urquhart, Vicki

    2012-01-01

    Today's Generation Z teens need to develop teamwork and social learning skills to be successful in the 21st century workplace. Teachers can help students develop these skills and enhance academic achievement by implementing cooperative learning strategies. Three key principles for successful cooperative learning are discussed. (Contains 1 figure.)

  9. A Semi-Supervised Approach for Refining Transcriptional Signatures of Drug Response and Repositioning Predictions.

    Directory of Open Access Journals (Sweden)

    Francesco Iorio

    Full Text Available We present a novel strategy to identify drug-repositioning opportunities. The starting point of our method is the generation of a signature summarising the consensual transcriptional response of multiple human cell lines to a compound of interest (namely the seed compound. This signature can be derived from data in existing databases, such as the connectivity-map, and it is used at first instance to query a network interlinking all the connectivity-map compounds, based on the similarity of their transcriptional responses. This provides a drug neighbourhood, composed of compounds predicted to share some effects with the seed one. The original signature is then refined by systematically reducing its overlap with the transcriptional responses induced by drugs in this neighbourhood that are known to share a secondary effect with the seed compound. Finally, the drug network is queried again with the resulting refined signatures and the whole process is carried on for a number of iterations. Drugs in the final refined neighbourhood are then predicted to exert the principal mode of action of the seed compound. We illustrate our approach using paclitaxel (a microtubule stabilising agent as seed compound. Our method predicts that glipizide and splitomicin perturb microtubule function in human cells: a result that could not be obtained through standard signature matching methods. In agreement, we find that glipizide and splitomicin reduce interphase microtubule growth rates and transiently increase the percentage of mitotic cells-consistent with our prediction. Finally, we validated the refined signatures of paclitaxel response by mining a large drug screening dataset, showing that human cancer cell lines whose basal transcriptional profile is anti-correlated to them are significantly more sensitive to paclitaxel and docetaxel.

  10. Generative Learning: Adults Learning within Ambiguity

    Science.gov (United States)

    Nicolaides, Aliki

    2015-01-01

    This study explored the extent to which ambiguity can serve as a catalyst for adult learning. The purpose of this study is to understand learning that is generated when encountering ambiguity agitated by the complexity of liquid modernity. "Ambiguity," in this study, describes an encounter with an appearance of reality that is at first…

  11. Learning as a Generative Process

    Science.gov (United States)

    Wittrock, M. C.

    2010-01-01

    A cognitive model of human learning with understanding is introduced. Empirical research supporting the model, which is called the generative model, is summarized. The model is used to suggest a way to integrate some of the research in cognitive development, human learning, human abilities, information processing, and aptitude-treatment…

  12. HAMA-Based Semi-Supervised Hashing Algorithm%基于HAMA的半监督哈希方法

    Institute of Scientific and Technical Information of China (English)

    刘扬; 朱明

    2014-01-01

    In the massive data retrieval applications, hashing-based approximate nearest(ANN) search has become popular due to its computational and memory efficiency for online search. Semi-supervised hashing (SSH) framework that minimizes empirical error over the labeled set and an information theoretic regularizer over both labeled and unlabeled sets. But the training of hashing function of this framework is so slow due to the large-scale complex training process. HAMA is a Hadoop top-level parallel framework based on Bulk Synchronous Parallel mode (BSP). In this paper, we analyze calculation of adjusted covariance matrix in the training process of SSH, split it into two parts:unsupervised data variance part and supervised pairwise labeled data part, and explore its parallelization. And experiments show the performance and scalability over general commercial hardware and network environment.%在海量数据检索应用中,基于哈希算法的最近邻搜索算法有着很高的计算和内存效率。而半监督哈希算法,结合了无监督哈希算法的正规化信息以及监督算法跨越语义鸿沟的优点,从而取得了良好的结果。但其线下的哈希函数训练过程则非常之缓慢,要对全部数据集进行复杂的训练过程。 HAMA是在Hadoop平台基础上,按照分布式计算BSP模型构建的并行计算框架。本文尝试在HAMA框架基础上,将半监督哈希算法的训练过程中的调整相关矩阵计算过程分解为无监督的相关矩阵部分与监督性的调整部分,分别进行并行计算处理。这使得使得其可以水平扩展在较大规模的商业计算集群上,使得其可以应用于实际应用。实验表明,这种分布式算法,有效提高算法的性能,并且可以进一步应用在大规模的计算集群上。

  13. Generational diversity: teaching and learning approaches.

    Science.gov (United States)

    Johnson, Susan A; Romanello, Mary L

    2005-01-01

    Nursing students represent multiple generations--Baby Boomers, Generation X, and now the Millennials. Each generation has its own set of values, ideas, ethics, beliefs, and learning styles. The authors describe the context, characteristics, and learning styles of each generation and provide suggestions for enhanced teaching and learning across multiple generations. Using generational diversity as a teaching tool in the classroom is also discussed.

  14. Non-intrusive Hazardous Pilot Cognitive State Assessment via Semi-Supervised Deep Learning: CSA-Deep Project

    Data.gov (United States)

    National Aeronautics and Space Administration — In aviation history, many crew-related errors are caused by crew members being in hazardous cognitive states, such as overstress, disengagement, high fatigue, and...

  15. Semisupervised learning for a hybrid generative/discriminative classifier based on the maximum entropy principle.

    Science.gov (United States)

    Fujino, Akinori; Ueda, Naonori; Saito, Kazumi

    2008-03-01

    This paper presents a method for designing semi-supervised classifiers trained on labeled and unlabeled samples. We focus on probabilistic semi-supervised classifier design for multi-class and single-labeled classification problems, and propose a hybrid approach that takes advantage of generative and discriminative approaches. In our approach, we first consider a generative model trained by using labeled samples and introduce a bias correction model, where these models belong to the same model family, but have different parameters. Then, we construct a hybrid classifier by combining these models based on the maximum entropy principle. To enable us to apply our hybrid approach to text classification problems, we employed naive Bayes models as the generative and bias correction models. Our experimental results for four text data sets confirmed that the generalization ability of our hybrid classifier was much improved by using a large number of unlabeled samples for training when there were too few labeled samples to obtain good performance. We also confirmed that our hybrid approach significantly outperformed generative and discriminative approaches when the performance of the generative and discriminative approaches was comparable. Moreover, we examined the performance of our hybrid classifier when the labeled and unlabeled data distributions were different.

  16. 自适应半监督边界费舍尔分析%Adaptive Semi-supervised Marginal Fisher Analysis

    Institute of Scientific and Technical Information of China (English)

    姜伟; 杨炳儒; 隋海峰

    2011-01-01

    基于图的半监督算法已经成功地应用于人脸识剐中,算法不仅考虑带标签数据而且利用一致性的假设.传统的算法一致性约束是定义在原特征空间中,但是在原特征空间中定义的一致性不是最好的.提出了自适应半监督边界费舍尔分析算法,它将一致性约束定义在原特征空间和期望低维特征空间中.在CMU PIE和YALE-B数据库上进行了实验,结果表明自适应半监督边界费舍尔分析算法在人脸识别率上有显著的提高.%Graph based semi-supervised methods have successfully used in face recognition. These algorithms not only consider the label information, but also utilize a consistency assumption. Conventional algorithms assumed that the eonsistency constraint is defined on the original feature spac. However, the original feature space is not the best for defining consistency. We proposed adaptive semi-supervised marginal fisher analysis (ASMFA) by which the consistency constraint is defined in the original feature space and the expected low-dimensional feature space. Experimental results on the CMU PIE and YALE-B databases demonstrate that ASMFA brings signification improvement in face recognition accuracy.

  17. A Generative Model of Mathematics Learning

    Science.gov (United States)

    Wittrock, M. C.

    1974-01-01

    The learning of mathematics is presented as a cognitive process rather than as a behavioristic one. A generative model of mathematics learning is described. Learning with understanding can occur with discovery or reception treatments. Relevant empirical research is discussed and implications for teaching mathematics as a generative process are…

  18. Generative Learning Management: A Hypothetical Model

    Science.gov (United States)

    Osterberg, Peter

    2004-01-01

    It is proposed that to reach a state of generative learning, an organization requires a "generative learning manager": a person who understands the importance of development and directing of knowledge. The purpose of this study was, therefore, both to explain mechanisms like knowledge distribution, goal setting and symbolic convergence from a…

  19. A Hybrid Constrained Semi-Supervised Clustering Algorithm%一种混合约束的半监督聚类算法

    Institute of Scientific and Technical Information of China (English)

    李雪梅; 王立宏; 宋宜斌

    2011-01-01

    提出一种混合约束的半监督聚类算法(HCC),综合考虑标号点和成对点约束信息的作用,使两种先验信息在聚类的过程中能以不同的方式发挥作用.给出理论推导、具体算法步骤、实验及分析.实验表明在HCC算法中,标号点对提高聚类结果的作用要比成对点约束信息的作用更明显,算法得到的CRI、聚类数、运行时间等多项指标都比对比算法好.%A hybrid constrained semi-supervised clustering algorithm (HCC) is proposed based on consistency algorithm. To get a better clustering result, both labeled data and pairwise constraints are considered in clustering to make use of two types of prior knowledge supplementary to each other. The theoretical derivation and the algorithm are presented in detail. Experimental results show that labeled data outperform pairwise constraints in promoting the quality of clustering. Additionally, for many indices, such as CRI, number of clusters and running time, HCC is better than comparative algorithms.

  20. Face Detection Method Based on Semi-supervised Clustering%基于半监督聚类的人脸检测方法

    Institute of Scientific and Technical Information of China (English)

    王燕; 蒋正午

    2012-01-01

    The paper proposes a method of face detection combined color of skin with continuous AdaBoost algorithm. In order to establish skin color model, this paper takes advantage of semi-supervised strategy to guide skin color clustering, and it also proposes a new algorithm SKDK in the process of clustering, skin color model can be established by the probability statistics distribution characteristics of each pixel cluster. On this basis, mathematical morphology of knowledge is used to handle image and find face candidate, which is the input of continuous AdaBoost classifier for final face detection. Experimental results prove that face detection ability of the method is superior to that directly using continuous AdaBoost method for face detection especially in multi-face situation.%将肤色与连续AdaBoost算法相结合进行人脸检测,并引入半监督策略指导肤色聚类从而建立肤色模型.在肤色聚类过程中,提出一种基于半监督的SKDK算法引导肤色聚类,依据各个像素簇的概率统计分布特性得到肤色模型.在此基础上利用数学形态学等知识对图像进行处理,得到人脸候选区域,将其作为连续AdaBoost分类器的输入进行人脸检测.实验结果表明,在多人脸的场景下,该方法的检测效果优于直接使用连续AdaBoost方法进行人脸检测的检测效果.

  1. 基于链接关系的半监督特征选择算法%Linked Social Media Data Based Semi-Supervised Feature Selection Method

    Institute of Scientific and Technical Information of China (English)

    王亦兵; 潘志松; 吴君青; 贾波; 胡谷雨

    2014-01-01

    社会媒体网络产生的海量、高维无标记数据给数据处理工作带来巨大挑战,同时数据样本间构成的链接图信息在现有模式识别算法中难以有效利用。基于此,文中充分挖掘社会媒体网络数据链接关系图,结合部分监督信息提出一种基于链接关系的半监督特征选择算法( SSLFS)。该算法利用谱分析和稀疏约束,使得选出的特征子集保持原数据的局部流形和稀疏特性。在社会媒体数据集Flickr上的实验结果表明,SSLFS相比其他特征选择方法得到的特征子集在分类性能上有较显著提高。%Mountains of high-dimensional, unlabeled data are produced by the social media network, which brings tremendous challenges to the data processing. Meanwhile, the linked graph information between data samples can not be effectively used in the existing pattern recognition algorithms. A semi-supervised feature selection method ( SSLFS) based on linked relations is proposed combined with a little supervised information after mining the linked graph of social media network. Through spectral analysis and sparsity constraint, SSLFS selects feature subsets which maintain the characteristics of local manifold and sparsity. The experimental results on the Flickr dataset show that the subset obtained by SSLFS is more effective when applied to classification compared with those by other methods.

  2. 基于半监督模糊聚类的入侵检测%Semi-supervised fuzzy clustering algorithm for intrusion detection

    Institute of Scientific and Technical Information of China (English)

    杜红乐; 樊景博

    2016-01-01

    针对网络行为数据中带标签数据收集困难及网络行为数据的异构性,提出了一种基于异构距离和样本密度的半监督模糊聚类算法,并将该算法应用到网络入侵检测中.该方法依据网络行为数据样本的异构性计算样本与类之间的异构距离及各个类的样本密度,利用异构距离和类内样本密度计算样本与类之间的模糊隶属度,用所得隶属度对无标签样本进行加标签处理,并得到相应的分类器.在KDD CUP99数据集上进行仿真实验,结果表明该方法是可行的、高效的.%Because collecting labeled samples is more difficult than collecting unlabeled samples and network data include value attribute and symbol attribute, this paper proposes an improved semi-supervised fuzzy clustering algorithm based on heterogeneous distance and sample density for intrusion detection. The algorithm computes membership with sample den-sity of one class and heterogeneous distance of intrusion detection dataset. Then it computes distance between sample and the center of every class and sets sample belonging to class of min-distance. It makes experiment with KDDCUP99 datas-et, and experimental results show that the method improves the detection accuracy.

  3. A Generative Model for Deep Convolutional Learning

    OpenAIRE

    Pu, Yunchen; Yuan, Xin; Carin, Lawrence

    2015-01-01

    A generative model is developed for deep (multi-layered) convolutional dictionary learning. A novel probabilistic pooling operation is integrated into the deep model, yielding efficient bottom-up (pretraining) and top-down (refinement) probabilistic learning. Experimental results demonstrate powerful capabilities of the model to learn multi-layer features from images, and excellent classification results are obtained on the MNIST and Caltech 101 datasets.

  4. Semi-Supervised Kernel PCA

    DEFF Research Database (Denmark)

    Walder, Christian; Henao, Ricardo; Mørup, Morten

    We present three generalisations of Kernel Principal Components Analysis (KPCA) which incorporate knowledge of the class labels of a subset of the data points. The first, MV-KPCA, penalises within class variances similar to Fisher discriminant analysis. The second, LSKPCA is a hybrid of least...... squares regression and kernel PCA. The final LR-KPCA is an iteratively reweighted version of the previous which achieves a sigmoid loss function on the labeled points. We provide a theoretical risk bound as well as illustrative experiments on real and toy data sets....

  5. Learning and the Net Generation

    Science.gov (United States)

    Duncan, D. K.; Rudolph, A. L.; Bruning, D.

    2014-07-01

    Most instructors believe that GPA, ethnicity, native English speaking ability, class year, family income, and whether parents have a college degree are important indicators of student success in Astro 101. Research shows, however, that the single most important factor in student learning is interactivity in the classroom. While new electronic media may have some important uses, research shows that electronic device usage in the classroom by students can negatively impact their course grades by as much five percent.

  6. Generating Multimedia Components for M-Learning

    Directory of Open Access Journals (Sweden)

    Adriana REVEIU

    2009-01-01

    Full Text Available The paper proposes a solution to generate template based multimedia components for instruction and learning available both for computer based applications and for mobile devices. The field of research is situated at the intersection of computer science, mobile tools and e-learning and is generically named mobile learning or M-learning. The research goal is to provide access to computer based training resources from any location and to adapt the training content to the specific features of mobile devices, communication environment, users' preferences and users' knowledge. To become important tools in education field, the technical solutions proposed will follow to use the potential of mobile devices.

  7. Net Generation's Learning Styles in Nursing Education.

    Science.gov (United States)

    Christodoulou, Eleni; Kalokairinou, Athina

    2015-01-01

    Numerous surveys have confirmed that emerging technologies and Web 2.0 tools have been a defining feature in the lives of current students, estimating that there is a fundamental shift in the way young people communicate, socialize and learn. Nursing students in higher education are characterized as digital literate with distinct traits which influence their learning styles. Millennials exhibit distinct learning preferences such as teamwork, experiential activities, structure, instant feedback and technology integration. Higher education institutions should be aware of the implications of the Net Generation coming to university and be prepared to meet their expectations and learning needs.

  8. Unifying generative and discriminative learning principles

    Directory of Open Access Journals (Sweden)

    Strickert Marc

    2010-02-01

    Full Text Available Abstract Background The recognition of functional binding sites in genomic DNA remains one of the fundamental challenges of genome research. During the last decades, a plethora of different and well-adapted models has been developed, but only little attention has been payed to the development of different and similarly well-adapted learning principles. Only recently it was noticed that discriminative learning principles can be superior over generative ones in diverse bioinformatics applications, too. Results Here, we propose a generalization of generative and discriminative learning principles containing the maximum likelihood, maximum a posteriori, maximum conditional likelihood, maximum supervised posterior, generative-discriminative trade-off, and penalized generative-discriminative trade-off learning principles as special cases, and we illustrate its efficacy for the recognition of vertebrate transcription factor binding sites. Conclusions We find that the proposed learning principle helps to improve the recognition of transcription factor binding sites, enabling better computational approaches for extracting as much information as possible from valuable wet-lab data. We make all implementations available in the open-source library Jstacs so that this learning principle can be easily applied to other classification problems in the field of genome and epigenome analysis.

  9. Semantic e-Learning: Next Generation of e-Learning?

    Science.gov (United States)

    Konstantinos, Markellos; Penelope, Markellou; Giannis, Koutsonikos; Aglaia, Liopa-Tsakalidi

    Semantic e-learning aspires to be the next generation of e-learning, since the understanding of learning materials and knowledge semantics allows their advanced representation, manipulation, sharing, exchange and reuse and ultimately promote efficient online experiences for users. In this context, the paper firstly explores some fundamental Semantic Web technologies and then discusses current and potential applications of these technologies in e-learning domain, namely, Semantic portals, Semantic search, personalization, recommendation systems, social software and Web 2.0 tools. Finally, it highlights future research directions and open issues of the field.

  10. The Generative Learning Model and Its Implications for Science Education.

    Science.gov (United States)

    Osborne, Roger; Wittrock, Merlin

    1985-01-01

    Suggesting that learning be considered as a generative process, attempts to: (1) place generative learning ideas in the context of other viewpoints of learning; (2) explicate key postulates of the generative learning model; and (3) examine implications of these theoretical ideas for teaching, learning, curriculum development, and research. (JN)

  11. Learning generative models of natural images.

    Science.gov (United States)

    Wu, Jiann-Ming; Lin, Zheng-Han

    2002-04-01

    This work proposes an unsupervised learning process for analysis of natural images. The derivation is based on a generative model, a stochastic coin-flip process directly operating on many disjoint multivariate Gaussian distributions. Following the maximal likelihood principle and using the Potts encoding, the goodness-of-fit of the generative model to tremendous patches randomly sampled from natural images is quantitatively expressed by an objective function subject to a set of constraints. By further combination of the objective function and the minimal wiring criterion, we achieve a mixed integer and linear programming. A hybrid of the mean field annealing and the gradient descent method is applied to the mathematical framework and produces three sets of interactive dynamics for the learning process. Numerical simulations show that the learning process is effective for extraction of orientation, localization and bandpass features and the generative model can make an ensemble of a sparse code for natural images.

  12. Semi-supervised Graph Clustering with Composite Kernel and Its Application in Hyperspectral Image%半监督复合核图聚类在高光谱图像中的应用

    Institute of Scientific and Technical Information of China (English)

    李志敏; 郝盼超; 黄鸿; 黄文

    2016-01-01

    针对图的半监督聚类算法(Semi-Supervised Graph-Based Clustering, SSGC)中出现的对先验信息利用不充分、不足以应对数据异构、计算耗时大等问题,本文提出一种基于半监督复合核的图聚类算法,并应用于高光谱图像。该算法首先通过引入半监督学习方法对径向基函数(Radial Basis Function, RBF)进行了改进,以充分利用少量的标记样本和无标记样本;其次将 RBF 核与光谱角核进行融合,构造复合核权重矩阵。在权重矩阵的构造过程中, K-近邻方法的引入也简化了计算过程。在Indian Pine和Botswana高光谱数据集上的实验结果表明,相对于SSGC算法,本文算法不仅实现了更高的分类正确率,其总体分类精度提升1%∼4%,而且有效提升了运算速度。%A semi-supervised graph-based clustering method is presented with composite kernel for the hyperspectral images, mainly to solve the problems existed in an algorithm called Semi-Supervised Graph-Based Clustering (SSGC) and improve its performance. As for the realization, it firstly reforms the Radial Basis Function (RBF) by adopting semi-supervised approach, to exploit the wealth of unlabeled samples in the image. Then, it incorporates the spectral angle kernel with RBF kernel, and constructs a composite kernel. At last, the use of K-Nearest Neighbor (KNN) method while constructing the weight matrix has greatly simplified the calculation. Experimental result in Indian Pine and Botswana hyperspectral data demonstrates that this algorithm can not only get higher classification accuracy (1%∼4% higher than SSGC, 10%∼20% higher than K-means and Fuzzy C-Means (FCM), but effectively improve operation speed compared with SSGC.

  13. Generative Inferences Based on Learned Relations.

    Science.gov (United States)

    Chen, Dawn; Lu, Hongjing; Holyoak, Keith J

    2016-11-17

    A key property of relational representations is their generativity: From partial descriptions of relations between entities, additional inferences can be drawn about other entities. A major theoretical challenge is to demonstrate how the capacity to make generative inferences could arise as a result of learning relations from non-relational inputs. In the present paper, we show that a bottom-up model of relation learning, initially developed to discriminate between positive and negative examples of comparative relations (e.g., deciding whether a sheep is larger than a rabbit), can be extended to make generative inferences. The model is able to make quasi-deductive transitive inferences (e.g., "If A is larger than B and B is larger than C, then A is larger than C") and to qualitatively account for human responses to generative questions such as "What is an animal that is smaller than a dog?" These results provide evidence that relational models based on bottom-up learning mechanisms are capable of supporting generative inferences.

  14. Learning generative models for protein fold families.

    Science.gov (United States)

    Balakrishnan, Sivaraman; Kamisetty, Hetunandan; Carbonell, Jaime G; Lee, Su-In; Langmead, Christopher James

    2011-04-01

    We introduce a new approach to learning statistical models from multiple sequence alignments (MSA) of proteins. Our method, called GREMLIN (Generative REgularized ModeLs of proteINs), learns an undirected probabilistic graphical model of the amino acid composition within the MSA. The resulting model encodes both the position-specific conservation statistics and the correlated mutation statistics between sequential and long-range pairs of residues. Existing techniques for learning graphical models from MSA either make strong, and often inappropriate assumptions about the conditional independencies within the MSA (e.g., Hidden Markov Models), or else use suboptimal algorithms to learn the parameters of the model. In contrast, GREMLIN makes no a priori assumptions about the conditional independencies within the MSA. We formulate and solve a convex optimization problem, thus guaranteeing that we find a globally optimal model at convergence. The resulting model is also generative, allowing for the design of new protein sequences that have the same statistical properties as those in the MSA. We perform a detailed analysis of covariation statistics on the extensively studied WW and PDZ domains and show that our method out-performs an existing algorithm for learning undirected probabilistic graphical models from MSA. We then apply our approach to 71 additional families from the PFAM database and demonstrate that the resulting models significantly out-perform Hidden Markov Models in terms of predictive accuracy.

  15. Learning generative models of molecular dynamics.

    Science.gov (United States)

    Razavian, Narges Sharif; Kamisetty, Hetunandan; Langmead, Christopher J

    2012-01-01

    We introduce three algorithms for learning generative models of molecular structures from molecular dynamics simulations. The first algorithm learns a Bayesian-optimal undirected probabilistic model over user-specified covariates (e.g., fluctuations, distances, angles, etc). L1 regularization is used to ensure sparse models and thus reduce the risk of over-fitting the data. The topology of the resulting model reveals important couplings between different parts of the protein, thus aiding in the analysis of molecular motions. The generative nature of the model makes it well-suited to making predictions about the global effects of local structural changes (e.g., the binding of an allosteric regulator). Additionally, the model can be used to sample new conformations. The second algorithm learns a time-varying graphical model where the topology and parameters change smoothly along the trajectory, revealing the conformational sub-states. The last algorithm learns a Markov Chain over undirected graphical models which can be used to study and simulate kinetics. We demonstrate our algorithms on multiple molecular dynamics trajectories.

  16. A Research on the Generative Learning Model Supported by Context-Based Learning

    Science.gov (United States)

    Ulusoy, Fatma Merve; Onen, Aysem Seda

    2014-01-01

    This study is based on the generative learning model which involves context-based learning. Using the generative learning model, we taught the topic of Halogens. This topic is covered in the grade 10 chemistry curriculum using activities which are designed in accordance with the generative learning model supported by context-based learning. The…

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

    Directory of Open Access Journals (Sweden)

    Sami Virpioja

    2012-03-01

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

  18. 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。

  19. Generative and discriminative learning by CL-Net.

    Science.gov (United States)

    Sun, Yanmin; Wong, Andrew K C; Wang, Yang

    2007-08-01

    This correspondence presents a two-stage classification learning algorithm. The first stage approximates the class-conditional distribution of a discrete space using a separate mixture model, and the second stage investigates the class posterior probabilities by training a network. The first stage explores the generative information that is inherent in each class by using the Chow-Liu (CL) method, which approximates high-dimensional probability with a tree structure, namely, a dependence tree, whereas the second stage concentrates on discriminative learning to distinguish between classes. The resulting learning algorithm integrates the advantages of both generative learning and discriminative learning. Because it uses CL dependence-tree estimation, we call our algorithm CL-Net. Empirical tests indicate that the proposed learning algorithm makes significant improvements when compared with the related classifiers that are constructed by either generative learning or discriminative learning.

  20. DIDACTIC ENGINEERING: DESIGNING NEW GENERATION LEARNING SYSTEMS

    Directory of Open Access Journals (Sweden)

    Nail K. Nuriyev

    2016-09-01

    Full Text Available Introduction: the article deals with the organisation of training activities in the man-made environment. Didactic engineering is seen as a methodology within which problems of didactics are solved with application of pedagogical, psychological, engineering methods. It is obvious that in order to implement the training of future engineers in a competence-based format (according to educational standard a new type of teaching system is needed, with new capacities (properties. These systems should set each student towards the development of professionally significant (key abilities, taking into account his/her psychological characteristics; ensure training on the verge of permissible difficulties (developing training, and thereby achieve rapid development of key skills, through his/her zone of “immediate development”; to diagnose the quality of possession of a competence in the academic sense. For the objectivity and reliability of assessment of the level and depth of learned knowledge it is necessary to generate this evaluation in a metric format. As a result, we created a didactic system, which combines all the listed properties and the properties of classical systems. This allowed us to construct a new generation of didactic systems. Materials and Methods: the research is based on a systematic analysis of the activity of an engineer; on models of “zones of immediate development” by L. S. Vygotsky; on “developmental education” by L. N. Zankova; on the use of pedagogical and psychological patterns as well as taxonomic methods, didactic engineering, theory of probability and mathematical statistics. Results: constructed is a model for training engineers in the metric format of competence, which envisages a rapid development of students project and constructive abilit ies based on their knowledge learned. Discussion and Conclusions: the parameters defining the probability of engineer’s success have been described; the taxonomic scale

  1. Understanding of Foreign Language Learning of Generation Y

    Science.gov (United States)

    Bozavli, Ebubekir

    2016-01-01

    Different generations are constituted depending on social changes and they are designed sociologically as traditional, baby boomer, X, Y and Z. Many studies have been reported on understanding of foreign language learning generation Y. This study aims to realise the gap in and contribute to the research on language learning understanding of…

  2. Generational Perspective of Higher Education Online Student Learning Styles

    Science.gov (United States)

    Williams, Chad James

    2013-01-01

    The purpose of this study was to determine whether students associated with a generational group exhibit similar learning styles as identified by the Felder and Soloman Index of Learning Styles instrument. The secondary purpose was to determine to what degree these generational groups rate their satisfaction with online education through the use…

  3. 基于CRF模型的半监督学习迭代观点句识别研究%Sentiment Sentence Recognition through Semi-supervised Bootstrapping Based on CRF

    Institute of Scientific and Technical Information of China (English)

    丁晟春; 文能; 蒋婷; 孟美任

    2012-01-01

    During recent years, sentiment analysis about text in Chinese is becoming more and more popular in academic research. In this paper, sentiment analysis is processed on sentence level. Sentiment words published by HowNet is used as the original evaluated-word set, a large amount of evaluated-words are obtained by semi-supervised bootstrapping based on CRF model. Then sentiment sentence can be recognized by evaluated-words, and the polarity of sentiment sentence can be judged by the designed semantic rules.%本文从句子级的角度进行了中文文本的情感倾向分析,提出以HowNet中的情感词表为种子情感词集,采用基于CRF模型的半监督学习迭代方法获取大量评价词,然后依据中文词间的语义规则判断句子的极性的方法.将该方法应用于COAE2011中任务2-观点句识别,在评价词的识别和观点句极性判断都取得了很好的结果.

  4. Intergenerational Learning (Between Generation X & Y) in Learning Families: A Narrative Inquiry

    Science.gov (United States)

    Ho, C. Y. Cherri

    2010-01-01

    The purpose of this study is to examine intergenerational learning behaviour within ten Hong Kong families between Generation X parents and their Generation Y children. It tries to investigate intergenerational knowledge exchange, identify the characteristics of learning behaviour and culture in their "learning families". A narrative…

  5. Language Distance Learning for the Digital Generation

    Science.gov (United States)

    Duran-Cerda, Dolores

    2010-01-01

    The purpose of this article was to shed light on the potential of distance learning to overcome challenges in distance, space, time, and human and economic resources that limit access to language learning opportunities in cultural, literary, historical, geographical, and cross-cultural frames. Language and literature educators collectively have…

  6. Generating Leading Practices through Professional Learning

    Science.gov (United States)

    Edwards Groves, Christine; Ronnerman, Karin

    2013-01-01

    In this paper we show how practices of professional learning and practices of leading can be understood as related in ecologies of practices. We will present findings from an international empirical research project that directs us to the connectivity between professional learning and leading practices that emerged as "adventitious",…

  7. Routine-Generating and Regenerative Workplace Learning

    Science.gov (United States)

    Kira, Mari

    2010-01-01

    The research discussed in this article focuses on workplace learning in industrial manufacturing work. Everyday work episodes contributing to workplace learning are investigated in four companies operating in the Finnish and Swedish package-supplier sectors. The research adopts a qualitative, interpretive approach. Interviews with employees and…

  8. Accessible Content Generation for the Learning Disabled

    Directory of Open Access Journals (Sweden)

    Zainab Pirani

    2014-06-01

    Full Text Available The research for this paper was conducted to explore the various aspects of Learning Disabled students and how the student-centered learning environments have been influenced and aided by educational technology. The educational content material which plays the important role in the field of educational technology has to be transformed in the way accessible to the LD learner. This paper provides the guidelines for the same as well provides the comparative analysis in support of th guidelines provided.

  9. Learning Orthographic Structure with Sequential Generative Neural Networks

    Science.gov (United States)

    Testolin, Alberto; Stoianov, Ivilin; Sperduti, Alessandro; Zorzi, Marco

    2016-01-01

    Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in…

  10. How Effective Is Example Generation for Learning Declarative Concepts?

    Science.gov (United States)

    Rawson, Katherine A.; Dunlosky, John

    2016-01-01

    Declarative concepts (i.e., key terms and corresponding definitions for abstract concepts) represent foundational knowledge that students learn in many content domains. Thus, investigating techniques to enhance concept learning is of critical importance. Various theoretical accounts support the expectation that example generation will serve this…

  11. Make Learning Matter for the Multitasking Generation

    Science.gov (United States)

    Adams, Jill

    2012-01-01

    Technological advances have created amazing opportunities for people throughout the world to access and share information. These opportunities have helped to create a generation of young adolescents who want to make the most of each minute of the day, seizing opportunities to seek information and communicate at the same time. This generation is…

  12. Generative Learning Objects Instantiated with Random Numbers Based Expressions

    Directory of Open Access Journals (Sweden)

    Ciprian Bogdan Chirila

    2015-12-01

    Full Text Available The development of interactive e-learning content requires special skills like programming techniques, web integration, graphic design etc. Generally, online educators do not possess such skills and their e-learning products tend to be static like presentation slides and textbooks. In this paper we propose a new interactive model of generative learning objects as a compromise betweenstatic, dull materials and dynamic, complex software e-learning materials developed by specialized teams. We find that random numbers based automatic initialization learning objects increases content diversity, interactivity thus enabling learners’ engagement. The resulted learning object model is at a limited level of complexity related to special e-learning software, intuitive and capable of increasing learners’ interactivity, engagement and motivation through dynamic content. The approach was applied successfully on several computer programing disciplines.

  13. A Learning Design for Student-Generated Digital Storytelling

    Science.gov (United States)

    Kearney, Matthew

    2011-01-01

    The literature on digital video in education emphasises the use of pre-fabricated, instructional-style video assets. Learning designs for supporting the use of these expert-generated video products have been developed. However, there has been a paucity of pedagogical frameworks for facilitating specific genres of learner-generated video projects.…

  14. Reflections on Wittrock's Generative Model of Learning: A Motivation Perspective

    Science.gov (United States)

    Anderman, Eric M.

    2010-01-01

    In this article, I examine developments in research on achievement motivation and comment on how those developments are reflected in Wittrock's generative model of learning. Specifically, I focus on the roles of prior knowledge, the generation of knowledge, and beliefs about ability. Examples from Wittrock's theory and from current motivational…

  15. A Learning Design for Student-Generated Digital Storytelling

    Science.gov (United States)

    Kearney, Matthew

    2011-01-01

    The literature on digital video in education emphasises the use of pre-fabricated, instructional-style video assets. Learning designs for supporting the use of these expert-generated video products have been developed. However, there has been a paucity of pedagogical frameworks for facilitating specific genres of learner-generated video projects.…

  16. Generative Modeling for Machine Learning on the D-Wave

    Energy Technology Data Exchange (ETDEWEB)

    Thulasidasan, Sunil [Los Alamos National Lab. (LANL), Los Alamos, NM (United States). Information Sciences Group

    2016-11-15

    These are slides on Generative Modeling for Machine Learning on the D-Wave. The following topics are detailed: generative models; Boltzmann machines: a generative model; restricted Boltzmann machines; learning parameters: RBM training; practical ways to train RBM; D-Wave as a Boltzmann sampler; mapping RBM onto the D-Wave; Chimera restricted RBM; mapping binary RBM to Ising model; experiments; data; D-Wave effective temperature, parameters noise, etc.; experiments: contrastive divergence (CD) 1 step; after 50 steps of CD; after 100 steps of CD; D-Wave (experiments 1, 2, 3); D-Wave observations.

  17. Addressing Learning Strategies for the Next Generation

    Science.gov (United States)

    Morris, P. A.; Reiff, P. H.; Sumners, C.

    2009-12-01

    Access to computers and interactive toys such as X Box have had impacts on learning strategies. New types of simulations and entertainment approaches will be increasingly important to reach out and encourage careers in science, technology, engineering and math (STEM) disciplines including space science. Examples of effective tools are planetarium shows and CD’s and DVD’s that can be distributed to teachers, students and the general public. Planetarium shows are no longer restricted to fixed dome venues but are increasingly being adapted to portable domes that have the advantage of transporting the activity to a school, community event or neighborhood center. Groups of individuals who may not normally consider a planetarium show as a family or group event are exposed to a learning experience which is also entertaining. Selected planetarium shows are available in languages other than English, including Spanish. Hands-on interactive activities are available that will enhance the experience of the attendees. Pre and post testing have shown [Sumners et al., 2006, 2008] that these activities are effective for improving STEM knowledge. New planetarium technology includes using a Wii controller for navigating through buildings. These so far have been applied to games but could be applied to a virtual tour of the space station, for example. CD’s and DVD’s are important for augmenting the activities of the planetarium shows as they provide additional learning activities that can be used either in the home, the classroom or as an enhancement for planetarium events. Simulations on the Sun, planetary or solar events, related games such as TIC TAC TOE are easily incorporated. It is important to provide additional support for the teachers that will enable them to incorporate the data into their curriculum and state mandated achievement levels.

  18. VLT/VLTI Second-Generation Instrumentation: Lessons Learned

    Science.gov (United States)

    Gilmozzi, R.; Pasquini, L.; Russell, A.

    2016-12-01

    The five second-generation instruments already delivered for the Very Large Telescope (VLT) represent worthy successors to the first generation of instrumentation development. Despite this success, it is still possible to learn many lessons for the future. A review, preceded by a workshop, on the lessons learned from the second-generation instrumentation for the VLT and VLT Interferometer took place in November 2015, following a previous review twelve years ago on lessons learned from the first-generation instruments. The aim of the workshop was to identify lessons in order to help define/refine good practice and make recommendations for the future. This article briefly reports on the workshop and summarises the findings of the review panel, their recommendations and some of the steps to implement them.

  19. Learning Orthographic Structure With Sequential Generative Neural Networks.

    Science.gov (United States)

    Testolin, Alberto; Stoianov, Ivilin; Sperduti, Alessandro; Zorzi, Marco

    2016-04-01

    Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine (RBM), a stochastic recurrent neural network that extracts high-order structure from sensory data through unsupervised generative learning and can encode contextual information in the form of internal, distributed representations. We assessed whether this type of network can extract the orthographic structure of English monosyllables by learning a generative model of the letter sequences forming a word training corpus. We show that the network learned an accurate probabilistic model of English graphotactics, which can be used to make predictions about the letter following a given context as well as to autonomously generate high-quality pseudowords. The model was compared to an extended version of simple recurrent networks, augmented with a stochastic process that allows autonomous generation of sequences, and to non-connectionist probabilistic models (n-grams and hidden Markov models). We conclude that sequential RBMs and stochastic simple recurrent networks are promising candidates for modeling cognition in the temporal domain.

  20. Learning robust pulses for generating universal quantum gates

    Science.gov (United States)

    Dong, Daoyi; Wu, Chengzhi; Chen, Chunlin; Qi, Bo; Petersen, Ian R.; Nori, Franco

    2016-01-01

    Constructing a set of universal quantum gates is a fundamental task for quantum computation. The existence of noises, disturbances and fluctuations is unavoidable during the process of implementing quantum gates for most practical quantum systems. This paper employs a sampling-based learning method to find robust control pulses for generating a set of universal quantum gates. Numerical results show that the learned robust control fields are insensitive to disturbances, uncertainties and fluctuations during the process of realizing universal quantum gates. PMID:27782219

  1. Data Generators for Learning Systems Based on RBF Networks.

    Science.gov (United States)

    Robnik-Sikonja, Marko

    2016-05-01

    There are plenty of problems where the data available is scarce and expensive. We propose a generator of semiartificial data with similar properties to the original data, which enables the development and testing of different data mining algorithms and the optimization of their parameters. The generated data allow large-scale experimentation and simulations without danger of overfitting. The proposed generator is based on radial basis function networks, which learn sets of Gaussian kernels. These Gaussian kernels can be used in a generative mode to generate new data from the same distributions. To assess the quality of the generated data, we evaluated the statistical properties of the generated data, structural similarity, and predictive similarity using supervised and unsupervised learning techniques. To determine usability of the proposed generator we conducted a large scale evaluation using 51 data sets. The results show a considerable similarity between the original and generated data and indicate that the method can be useful in several development and simulation scenarios. We analyze possible improvements in the classification performance by adding different amounts of the generated data to the training set, performance on high-dimensional data sets, and conditions when the proposed approach is successful.

  2. A Novel Co-training Ob ject Tracking Algorithm Based on Online Semi-supervised Boosting%基于在线半监督boosting的协同训练目标跟踪算法

    Institute of Scientific and Technical Information of China (English)

    陈思; 苏松志; 李绍滋; 吕艳萍; 曹冬林

    2014-01-01

    The self-training based discriminative tracking methods use the classification results to update the classifier itself. However, these methods easily suffer from the drifting issue because the classification errors are accumulated during tracking. To overcome the disadvantages of self-training based tracking methods, a novel co-training tracking algorithm, termed Co-SemiBoost, is proposed based on online semi-supervised boosting. The proposed algorithm employs a new online co-training framework, where unlabeled samples are used to collaboratively train the classifiers respectively built on two feature views. Moreover, the pseudo-labels and weights of unlabeled samples are iteratively predicted by combining the decisions of a prior model and an online classifier. The proposed algorithm can effectively improve the discriminative ability of the classifier, and is robust to occlusions, illumination changes, etc. Thus the algorithm can better adapt to object appearance changes. Experimental results on several challenging video sequences show that the proposed algorithm achieves promising tracking performance.%基于自训练的判别式目标跟踪算法使用分类器的预测结果更新分类器自身,容易累积分类错误,从而导致漂移问题。为了克服自训练跟踪算法的不足,该文提出一种基于在线半监督boosting的协同训练目标跟踪算法(简称Co-SemiBoost),其采用一种新的在线协同训练框架,利用未标记样本协同训练两个特征视图中的分类器,同时结合先验模型和在线分类器迭代预测未标记样本的类标记和权重。该算法能够有效提高分类器的判别能力,鲁棒地处理遮挡、光照变化等问题,从而较好地适应目标外观的变化。在若干个视频序列的实验结果表明,该算法具有良好的跟踪性能。

  3. Semi-supervised canonical correlation analysis based multi-view dimensionality reduction%基于半监督典型相关分析的多视图维数约简

    Institute of Scientific and Technical Information of China (English)

    董西伟; 杨茂保; 张广顺

    2016-01-01

    为了有效地在半监督多视图情景下进行维数约简,提出了使用非负低秩图进行标签传播的半监督典型相关分析方法。非负低秩图捕获的全局线性近邻可以利用直接邻居和间接可达邻居的信息维持全局簇结构,同时低秩的性质可以保持图的压缩表示。当无标签样本通过标签传播算法获得估计的标签信息后,在每个视图上构建软标签矩阵和概率类内散度矩阵,然后通过最大化不同视图同类样本间相关性的同时最小化每个视图低维特征空间类内变化来提升特征鉴别能力。实验结果表明,所提方法比已有相关方法能够取得更好的识别性能且更鲁棒,是有效的多视图维数约简方法。%In order to efficiently reduce dimensionality in multi-view semi-supervised scenarios,this paper proposed semi-su-pervised canonical correlation analysis methods which used nonnegative low-rank graph to propagate labels.Global linear neigh-borhoods captured by nonnegative low-rank graph could utilize information from both direct and reachable indirect neighbors to preserve the global cluster structures,while the low-rank property retained a compressed representation of the graph.After esti-mating label information of unlabeled samples by label propagation algorithm,it constructed soft label matrices of all samples and probabilistic within-class scatter matrices in each view.Then,by maximizing the correlations between samples of the same class from cross views and minimizing within-class variations in the low-dimensional feature space of each view simultaneously, it enhanced discriminative power of features.Experimental results demonstrate that the proposed methods can achieve better recognition performances and robustness than existing related methods and are effective multi-view dimensionality reduction methods.

  4. Probabilistic forecasting of wind power generation using extreme learning machine

    DEFF Research Database (Denmark)

    Wan, Can; Xu, Zhao; Pinson, Pierre

    2014-01-01

    an extreme learning machine (ELM)-based probabilistic forecasting method for wind power generation. To account for the uncertainties in the forecasting results, several bootstrapmethods have been compared for modeling the regression uncertainty, based on which the pairs bootstrap method is identified...... demonstrate that the proposed method is effective for probabilistic forecasting of wind power generation with a high potential for practical applications in power systems....

  5. Adaptive distance metric learning for diffusion tensor image segmentation.

    Science.gov (United States)

    Kong, Youyong; Wang, Defeng; Shi, Lin; Hui, Steve C N; Chu, Winnie C W

    2014-01-01

    High quality segmentation of diffusion tensor images (DTI) is of key interest in biomedical research and clinical application. In previous studies, most efforts have been made to construct predefined metrics for different DTI segmentation tasks. These methods require adequate prior knowledge and tuning parameters. To overcome these disadvantages, we proposed to automatically learn an adaptive distance metric by a graph based semi-supervised learning model for DTI segmentation. An original discriminative distance vector was first formulated by combining both geometry and orientation distances derived from diffusion tensors. The kernel metric over the original distance and labels of all voxels were then simultaneously optimized in a graph based semi-supervised learning approach. Finally, the optimization task was efficiently solved with an iterative gradient descent method to achieve the optimal solution. With our approach, an adaptive distance metric could be available for each specific segmentation task. Experiments on synthetic and real brain DTI datasets were performed to demonstrate the effectiveness and robustness of the proposed distance metric learning approach. The performance of our approach was compared with three classical metrics in the graph based semi-supervised learning framework.

  6. Adaptive distance metric learning for diffusion tensor image segmentation.

    Directory of Open Access Journals (Sweden)

    Youyong Kong

    Full Text Available High quality segmentation of diffusion tensor images (DTI is of key interest in biomedical research and clinical application. In previous studies, most efforts have been made to construct predefined metrics for different DTI segmentation tasks. These methods require adequate prior knowledge and tuning parameters. To overcome these disadvantages, we proposed to automatically learn an adaptive distance metric by a graph based semi-supervised learning model for DTI segmentation. An original discriminative distance vector was first formulated by combining both geometry and orientation distances derived from diffusion tensors. The kernel metric over the original distance and labels of all voxels were then simultaneously optimized in a graph based semi-supervised learning approach. Finally, the optimization task was efficiently solved with an iterative gradient descent method to achieve the optimal solution. With our approach, an adaptive distance metric could be available for each specific segmentation task. Experiments on synthetic and real brain DTI datasets were performed to demonstrate the effectiveness and robustness of the proposed distance metric learning approach. The performance of our approach was compared with three classical metrics in the graph based semi-supervised learning framework.

  7. Development of Generative Learning Objects Using Feature Diagrams and Generative Techniques

    OpenAIRE

    Vytautas STUIKYS; Robertas DAMASEVICIUS

    2008-01-01

    Learning Objects (LOs) play a key role for supporting eLearning. In general, however, the development of LOs remains a vague issue, because there is still no clearly defined and widely adopted LO specification and development methodology. We combined two technological paradigms (feature diagrams (FDs) and generative techniques) into a coherent methodology to enhance reusability and productivity in the development of LOs. FDs are used for knowledge representation, modelling variability of the ...

  8. Learning from Chemical Visualizations: Comparing Generation and Selection

    Science.gov (United States)

    Zhang, Zhihui Helen; Linn, Marcia C.

    2013-01-01

    Dynamic visualizations can make unseen phenomena such as chemical reactions visible but students need guidance to benefit from them. This study explores the value of generating drawings versus selecting among alternatives to guide students to learn chemical reactions from a dynamic visualization of hydrogen combustion as part of an online inquiry…

  9. Individual Learning Strategies and Choice in Student-Generated Multimedia

    Science.gov (United States)

    McGahan, William T.; Ernst, Hardy; Dyson, Laurel Evelyn

    2016-01-01

    There has been an increasing focus on student-generated multimedia assessment as a way of introducing the benefits of both visual literacy and peer-mediated learning into university courses. One such assessment was offered to first-year health science students but, contrary to expectations, led to poorer performance in their end-of-semester…

  10. Individual Learning Strategies and Choice in Student-Generated Multimedia

    Science.gov (United States)

    McGahan, William T.; Ernst, Hardy; Dyson, Laurel Evelyn

    2016-01-01

    There has been an increasing focus on student-generated multimedia assessment as a way of introducing the benefits of both visual literacy and peer-mediated learning into university courses. One such assessment was offered to first-year health science students but, contrary to expectations, led to poorer performance in their end-of-semester…

  11. Approaches and Strategies in Next Generation Science Learning

    Science.gov (United States)

    Khine, Myint Swe, Ed.; Saleh, Issa M., Ed.

    2013-01-01

    "Approaches and Strategies in Next Generation Science Learning" examines the challenges involved in the development of modern curriculum models, teaching strategies, and assessments in science education in order to prepare future students in the 21st century economies. This comprehensive collection of research brings together science educators,…

  12. Structural Learning of Attack Vectors for Generating Mutated XSS Attacks

    CERN Document Server

    Wang, Yi-Hsun; Lee, Hahn-Ming; 10.4204/EPTCS.35.2

    2010-01-01

    Web applications suffer from cross-site scripting (XSS) attacks that resulting from incomplete or incorrect input sanitization. Learning the structure of attack vectors could enrich the variety of manifestations in generated XSS attacks. In this study, we focus on generating more threatening XSS attacks for the state-of-the-art detection approaches that can find potential XSS vulnerabilities in Web applications, and propose a mechanism for structural learning of attack vectors with the aim of generating mutated XSS attacks in a fully automatic way. Mutated XSS attack generation depends on the analysis of attack vectors and the structural learning mechanism. For the kernel of the learning mechanism, we use a Hidden Markov model (HMM) as the structure of the attack vector model to capture the implicit manner of the attack vector, and this manner is benefited from the syntax meanings that are labeled by the proposed tokenizing mechanism. Bayes theorem is used to determine the number of hidden states in the model...

  13. Axis: Generating Explanations at Scale with Learnersourcing and Machine Learning

    Science.gov (United States)

    Williams, Joseph Jay; Kim, Juho; Rafferty, Anna; Heffernan, Neil; Maldonado, Samuel; Gajos, Krzysztof Z.; Lasecki, Walter S.; Heffernan, Neil

    2016-01-01

    While explanations may help people learn by providing information about why an answer is correct, many problems on online platforms lack high-quality explanations. This paper presents AXIS (Adaptive eXplanation Improvement System), a system for obtaining explanations. AXIS asks learners to generate, revise, and evaluate explanations as they solve…

  14. 2020 Vision: Envisioning a New Generation of STEM Learning Research

    Science.gov (United States)

    Dierking, Lynn D.; Falk, John H.

    2016-01-01

    In this issue, we have compiled six original papers, outcomes from the U.S. National Science Foundation (US-NSF)-funded REESE (Research and Evaluation on Education in Science and Engineering) 2020 Vision: The Next Generation of STEM Learning Research project. The purpose of 2020 Vision was to re-envision the questions and frameworks guiding STEM…

  15. Structural Learning of Attack Vectors for Generating Mutated XSS Attacks

    Directory of Open Access Journals (Sweden)

    Yi-Hsun Wang

    2010-09-01

    Full Text Available Web applications suffer from cross-site scripting (XSS attacks that resulting from incomplete or incorrect input sanitization. Learning the structure of attack vectors could enrich the variety of manifestations in generated XSS attacks. In this study, we focus on generating more threatening XSS attacks for the state-of-the-art detection approaches that can find potential XSS vulnerabilities in Web applications, and propose a mechanism for structural learning of attack vectors with the aim of generating mutated XSS attacks in a fully automatic way. Mutated XSS attack generation depends on the analysis of attack vectors and the structural learning mechanism. For the kernel of the learning mechanism, we use a Hidden Markov model (HMM as the structure of the attack vector model to capture the implicit manner of the attack vector, and this manner is benefited from the syntax meanings that are labeled by the proposed tokenizing mechanism. Bayes theorem is used to determine the number of hidden states in the model for generalizing the structure model. The paper has the contributions as following: (1 automatically learn the structure of attack vectors from practical data analysis to modeling a structure model of attack vectors, (2 mimic the manners and the elements of attack vectors to extend the ability of testing tool for identifying XSS vulnerabilities, (3 be helpful to verify the flaws of blacklist sanitization procedures of Web applications. We evaluated the proposed mechanism by Burp Intruder with a dataset collected from public XSS archives. The results show that mutated XSS attack generation can identify potential vulnerabilities.

  16. Discovering binary codes for documents by learning deep generative models.

    Science.gov (United States)

    Hinton, Geoffrey; Salakhutdinov, Ruslan

    2011-01-01

    We describe a deep generative model in which the lowest layer represents the word-count vector of a document and the top layer represents a learned binary code for that document. The top two layers of the generative model form an undirected associative memory and the remaining layers form a belief net with directed, top-down connections. We present efficient learning and inference procedures for this type of generative model and show that it allows more accurate and much faster retrieval than latent semantic analysis. By using our method as a filter for a much slower method called TF-IDF we achieve higher accuracy than TF-IDF alone and save several orders of magnitude in retrieval time. By using short binary codes as addresses, we can perform retrieval on very large document sets in a time that is independent of the size of the document set using only one word of memory to describe each document.

  17. A Machine Learning Approach to Test Data Generation

    DEFF Research Database (Denmark)

    Christiansen, Henning; Dahmcke, Christina Mackeprang

    2007-01-01

    been tested, and a more thorough statistical foundation is required. We propose to use logic-statistical modelling methods for machine-learning for analyzing existing and manually marked up data, integrated with the generation of new, artificial data. More specifically, we suggest to use the PRISM...... system developed by Sato and Kameya. Based on logic programming extended with random variables and parameter learning, PRISM appears as a powerful modelling environment, which subsumes HMMs and a wide range of other methods, all embedded in a declarative language. We illustrate these principles here...

  18. Theory-generating practice. Proposing a principle for learning design

    DEFF Research Database (Denmark)

    Buhl, Mie

    2016-01-01

    This contribution proposes a principle for learning design – Theory-Generating Practice (TGP) – as an alternative to the way university courses are traditionally taught and structured, with a series of theoretical lectures isolated from practical experience and concluding with an exam or a project...... building, and takes tacit knowledge into account. The article introduces TGP, contextualizes it to a Danish tradition of didactics, and discusses it in relation to contemporary conceptual currents of didactic design and learning design. This is followed by a theoretical framing of TGP. Finally, three...

  19. Learning and Visualizing Modulation Discriminative Radio Signal Features

    Science.gov (United States)

    2016-09-01

    valued temporal domain radio signals. We propose a method for accomplishing online semi-supervised learning with a tied- weight convolutional...reduction of X. Additionally, if φ is parameterized by a weight matrix W and ψ = W T , this method is known as weight tying and has the desirable...waveforms’ input. This binary data is modulated as in-phase and quadrature (I/Q) samples using each of six methods : on-off keying (OOK), Gaussian

  20. Perceptual learning of acoustic noise generates memory-evoked potentials.

    Science.gov (United States)

    Andrillon, Thomas; Kouider, Sid; Agus, Trevor; Pressnitzer, Daniel

    2015-11-01

    Experience continuously imprints on the brain at all stages of life. The traces it leaves behind can produce perceptual learning [1], which drives adaptive behavior to previously encountered stimuli. Recently, it has been shown that even random noise, a type of sound devoid of acoustic structure, can trigger fast and robust perceptual learning after repeated exposure [2]. Here, by combining psychophysics, electroencephalography (EEG), and modeling, we show that the perceptual learning of noise is associated with evoked potentials, without any salient physical discontinuity or obvious acoustic landmark in the sound. Rather, the potentials appeared whenever a memory trace was observed behaviorally. Such memory-evoked potentials were characterized by early latencies and auditory topographies, consistent with a sensory origin. Furthermore, they were generated even on conditions of diverted attention. The EEG waveforms could be modeled as standard evoked responses to auditory events (N1-P2) [3], triggered by idiosyncratic perceptual features acquired through learning. Thus, we argue that the learning of noise is accompanied by the rapid formation of sharp neural selectivity to arbitrary and complex acoustic patterns, within sensory regions. Such a mechanism bridges the gap between the short-term and longer-term plasticity observed in the learning of noise [2, 4-6]. It could also be key to the processing of natural sounds within auditory cortices [7], suggesting that the neural code for sound source identification will be shaped by experience as well as by acoustics.

  1. Cortes' Multicultural Empowerment Model and Generative Teaching and Learning in Science.

    Science.gov (United States)

    Loving, Cathleen C.

    1998-01-01

    Adapts Cortes' Multicultural Empowerment Model to science teaching and to Wittrock's Model of Generative Learning and Teaching in Science. Encourages all children to learn science and learn about science. Contains 55 references. (DDR)

  2. Visual tracker using sequential bayesian learning: discriminative, generative, and hybrid.

    Science.gov (United States)

    Lei, Yun; Ding, Xiaoqing; Wang, Shengjin

    2008-12-01

    This paper presents a novel solution to track a visual object under changes in illumination, viewpoint, pose, scale, and occlusion. Under the framework of sequential Bayesian learning, we first develop a discriminative model-based tracker with a fast relevance vector machine algorithm, and then, a generative model-based tracker with a novel sequential Gaussian mixture model algorithm. Finally, we present a three-level hierarchy to investigate different schemes to combine the discriminative and generative models for tracking. The presented hierarchical model combination contains the learner combination (at level one), classifier combination (at level two), and decision combination (at level three). The experimental results with quantitative comparisons performed on many realistic video sequences show that the proposed adaptive combination of discriminative and generative models achieves the best overall performance. Qualitative comparison with some state-of-the-art methods demonstrates the effectiveness and efficiency of our method in handling various challenges during tracking.

  3. A fuzzy method to learn text classifier from labeled and unlabeled examples

    Institute of Scientific and Technical Information of China (English)

    刘宏; 黄上腾

    2004-01-01

    In text classification, labeling documents is a tedious and costly task, as it would consume a lot of expert time. On the other hand, it usually is easier to obtain a lot of unlabeled documents, with the help of some tools like Digital Library, Crawler Programs, and Searching Engine. To learn text classifier from labeled and unlabeled examples, a novel fuzzy method is proposed. Firstly, a Seeded Fuzzy c-means Clustering algorithm is proposed to learn fuzzy clusters from a set of labeled and unlabeled examples. Secondly, based on the resulting fuzzy clusters, some examples with high confidence are selected to construct training data set. Finally,the constructed training data set is used to train Fuzzy Support Vector Machine, and get text classifier. Empirical results on two benchmark datasets indicate that, by incorporating unlabeled examples into learning process,the method performs significantly better than FSVM trained with a small number of labeled examples only. Also, the method proposed performs at least as well as the related method-EM with Naive Bayes. One advantage of the method proposed is that it does not rely on any parametric assumptions about the data as it is usually the case with generative methods widely used in semi-supervised learning.

  4. Leveraging Random Number Generation for Mastery of Learning in Teaching Quantitative Research Courses via an E-Learning Method

    Science.gov (United States)

    Boonsathorn, Wasita; Charoen, Danuvasin; Dryver, Arthur L.

    2014-01-01

    E-Learning brings access to a powerful but often overlooked teaching tool: random number generation. Using random number generation, a practically infinite number of quantitative problem-solution sets can be created. In addition, within the e-learning context, in the spirit of the mastery of learning, it is possible to assign online quantitative…

  5. Analysis of students’ generated questions in laboratory learning environments

    Directory of Open Access Journals (Sweden)

    Juan Antonio Llorens-Molina

    2012-03-01

    Full Text Available In order to attain a reliable laboratory work assessment, we argue taking the Learning Environment as a core concept and a research paradigm that considers the factors affecting the laboratory as a particularly complex educational context. With regard to Laboratory Learning Environments (LLEs, a well known approach is the SLEI (Science Laboratory Environment Inventory. The aim of this research is to design and apply an alternative and qualitative assessment tool to characterize Laboratory Learning Environments in an introductory course of organic chemistry. An alternative and qualitative assessment tool would be useful for providing feed-back for experimental learning improvement; serving as a complementary triangulation tool in educational research on LLEs; and generating meaningful categories in order to design quantitative research instruments. Toward this end, spontaneous questions by students have been chosen as a reliable source of information. To process these questions, a methodology based on the Grounded Theory has been developed to provide a framework for characterizing LLEs. This methodology has been applied in two case studies. The conclusions lead us to argue for using more holistic assessment tools in both everyday practice and research. Likewise, a greater attention should be paid to metacognition to achieve suitable self-perception concerning students’ previous knowledge and manipulative skills.

  6. Learning Content Selection Rules for Generating Object Descriptions in Dialogue

    CERN Document Server

    Jordan, P W; 10.1613/jair.1591

    2011-01-01

    A fundamental requirement of any task-oriented dialogue system is the ability to generate object descriptions that refer to objects in the task domain. The subproblem of content selection for object descriptions in task-oriented dialogue has been the focus of much previous work and a large number of models have been proposed. In this paper, we use the annotated COCONUT corpus of task-oriented design dialogues to develop feature sets based on Dale and Reiters (1995) incremental model, Brennan and Clarks (1996) conceptual pact model, and Jordans (2000b) intentional influences model, and use these feature sets in a machine learning experiment to automatically learn a model of content selection for object descriptions. Since Dale and Reiters model requires a representation of discourse structure, the corpus annotations are used to derive a representation based on Grosz and Sidners (1986) theory of the intentional structure of discourse, as well as two very simple representations of discourse structure based purel...

  7. Automated problem generation in Learning Management Systems: a tutorial

    Directory of Open Access Journals (Sweden)

    Jaime Romero

    2016-07-01

    Full Text Available The benefits of solving problems have been widely acknowledged by literature. Its implementation in e–learning platforms can make easier its management and the learning process itself. However, its implementation can also become a very time–consuming task, particularly when the number of problems to generate is high. In this tutorial we describe a methodology that we have developed aiming to alleviate the workload of producing a great deal of problems in Moodle for an undergraduate business course. This methodology follows a six-step process and allows evaluating student’s skills in problem solving, minimizes plagiarism behaviors and provides immediate feedback. We expect this tutorial encourage other educators to apply our six steps process, thus benefiting themselves and their students of its advantages.

  8. Robust Reservoir Generation by Correlation-Based Learning

    Directory of Open Access Journals (Sweden)

    Tadashi Yamazaki

    2009-01-01

    Full Text Available Reservoir computing (RC is a new framework for neural computation. A reservoir is usually a recurrent neural network with fixed random connections. In this article, we propose an RC model in which the connections in the reservoir are modifiable. Specifically, we consider correlation-based learning (CBL, which modifies the connection weight between a given pair of neurons according to the correlation in their activities. We demonstrate that CBL enables the reservoir to reproduce almost the same spatiotemporal activity patterns in response to an identical input stimulus in the presence of noise. This result suggests that CBL enhances the robustness in the generation of the spatiotemporal activity pattern against noise in input signals. We apply our RC model to trace eyeblink conditioning. The reservoir bridged the gap of an interstimulus interval between the conditioned and unconditioned stimuli, and a readout neuron was able to learn and express the timed conditioned response.

  9. Hybrid Generative/Discriminative Learning for Automatic Image Annotation

    CERN Document Server

    Yang, Shuang Hong; Zha, Hongyuan

    2012-01-01

    Automatic image annotation (AIA) raises tremendous challenges to machine learning as it requires modeling of data that are both ambiguous in input and output, e.g., images containing multiple objects and labeled with multiple semantic tags. Even more challenging is that the number of candidate tags is usually huge (as large as the vocabulary size) yet each image is only related to a few of them. This paper presents a hybrid generative-discriminative classifier to simultaneously address the extreme data-ambiguity and overfitting-vulnerability issues in tasks such as AIA. Particularly: (1) an Exponential-Multinomial Mixture (EMM) model is established to capture both the input and output ambiguity and in the meanwhile to encourage prediction sparsity; and (2) the prediction ability of the EMM model is explicitly maximized through discriminative learning that integrates variational inference of graphical models and the pairwise formulation of ordinal regression. Experiments show that our approach achieves both su...

  10. Generation and Application of Virtual Dynamic Learning Environments

    Directory of Open Access Journals (Sweden)

    Esther Zaretsky

    2009-04-01

    Full Text Available The generation of virtual dynamic learning environments by mental imagery improved physical education of student teachers. Up-to-date studies showed that training computerized simulations improved spatial abilities, especially visualization of the body's movements in space, and enhanced academic achievements. The main program of the research concentrated on creating teaching units focusing on a variety of physical skills through computerized dynamic presentations. The findings showed that as the student teachers practiced the creation of simulations through the PowerPoint Software, it became clear to them how the computer is related to physical activities. Consequently their presentations became highly animated, and applied to the natural environment. The student teachers applied their presentations in their practical classroom and reported about their pupils' progress in physical skills. Moreover the motivation of the student teachers and pupils to both modes of learning, manipulating virtually and physically, was enhanced.

  11. Learning for the Next Generation: Predicting the Usage of Synthetic Learning Environments

    Science.gov (United States)

    Evans, Arthur William, III

    2010-01-01

    The push to further the use of technology in learning has broadened the attempts of many to find innovated ways to aid the new, technologically savvy generation of learners, in acquiring the knowledge needed for their education and training. A critical component to the success of these initiatives is the proper application of the "science of…

  12. Generative Learning, Quizzing and Cognitive Learning: An Experimental Study in the Communication Classroom

    Science.gov (United States)

    Johnson, Danette Ifert; Mrowka, Kaleigh

    2010-01-01

    This investigation tests Wittrock's generative learning model as an explanation for the positive relationship found between quizzing and student performance in a number of studies. Results support the theory, suggesting that quizzes structured to include multiple levels of Bloom, Engelhart, Furst, Hill and Krathwohl's (1956) taxonomy, and thereby…

  13. Developing a Learning Algorithm-Generated Empirical Relaxer

    Energy Technology Data Exchange (ETDEWEB)

    Mitchell, Wayne [Univ. of Colorado, Boulder, CO (United States). Dept. of Applied Math; Kallman, Josh [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Toreja, Allen [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Gallagher, Brian [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Jiang, Ming [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Laney, Dan [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2016-03-30

    One of the main difficulties when running Arbitrary Lagrangian-Eulerian (ALE) simulations is determining how much to relax the mesh during the Eulerian step. This determination is currently made by the user on a simulation-by-simulation basis. We present a Learning Algorithm-Generated Empirical Relaxer (LAGER) which uses a regressive random forest algorithm to automate this decision process. We also demonstrate that LAGER successfully relaxes a variety of test problems, maintains simulation accuracy, and has the potential to significantly decrease both the person-hours and computational hours needed to run a successful ALE simulation.

  14. 2020 Vision: Envisioning a new generation of STEM learning research

    Science.gov (United States)

    Dierking, Lynn D.; Falk, John H.

    2016-03-01

    In this issue, we have compiled six original papers, outcomes from the U.S. National Science Foundation (US-NSF)-funded REESE (Research and Evaluation on Education in Science and Engineering) 2020 Vision: The Next Generation of STEM Learning Research project. The purpose of 2020 Vision was to re-envision the questions and frameworks guiding STEM research in the twenty-first century, given that notions of learning have changed significantly in the last decade. The papers present diverse research principles that emerged from an initial 2020 Vision conference at Oregon State University (OSU), were then vetted more broadly with the science education community nationally and internationally, and presented in a public 2020 Vision symposium series also at OSU. Individually and as a group, these papers argue that if STEM learning is lifelong, life-wide and life-deep, research designs need to cut across the diverse settings and investigate the multiple contexts and media in which learners live and interact. Authors call for research paradigms that holistically reflect questions of the "what, when, where, why, how and with whom" of STEM learning. Associated Forum papers respond and expand the conversation by critically examining the recommended research principles and in some cases, challenging both authors and editors to think even more broadly. Two Key Contributor pieces highlight the contributions of researchers who have helped to push on these research boundaries, advancing science education research nationally and internationally. A final synthesis paper, a case study of research being conducted in a diverse, under-resourced community in Portland, Oregon provides one example of how the 2020 Vision research principles might be integrated into a comprehensive STEM learning research study.

  15. Protein Function Prediction Based on Active Semi-sup ervised Learning

    Institute of Scientific and Technical Information of China (English)

    WANG Xuesong,CHENG Yuhu; LI Lijing

    2016-01-01

    In our study, the active learning and semi-supervised learning methods are comprehensively used for label delivery of proteins with known functions in Protein-protein interaction (PPI) network so as to predict the func-tions of unknown proteins. Because the real PPI network is generally observed with overlapping protein nodes with multiple functions, the mislabeling of overlapping protein may result in accumulation of prediction errors. For this reason, prior to executing the label delivery process of semi-supervised learning, the adjacency matrix is used to detect overlapping proteins. As the topological structure description of interactive relation between proteins, PPI network is observed with party hub protein nodes that play an important role, in co-expression with its neighborhood. Therefore, to reduce the manual labeling cost, party hub proteins most beneficial for improvement of prediction ac-curacy are selected for class labeling and the labeled party hub proteins are added into the labeled sample set for semi-supervised learning later. As the experimental results of real yeast PPI network show, the proposed algorithm can achieve high prediction accuracy with few labeled samples.

  16. 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.

  17. Efficient generation of image chips for training deep learning algorithms

    Science.gov (United States)

    Han, Sanghui; Fafard, Alex; Kerekes, John; Gartley, Michael; Ientilucci, Emmett; Savakis, Andreas; Law, Charles; Parhan, Jason; Turek, Matt; Fieldhouse, Keith; Rovito, Todd

    2017-05-01

    Training deep convolutional networks for satellite or aerial image analysis often requires a large amount of training data. For a more robust algorithm, training data need to have variations not only in the background and target, but also radiometric variations in the image such as shadowing, illumination changes, atmospheric conditions, and imaging platforms with different collection geometry. Data augmentation is a commonly used approach to generating additional training data. However, this approach is often insufficient in accounting for real world changes in lighting, location or viewpoint outside of the collection geometry. Alternatively, image simulation can be an efficient way to augment training data that incorporates all these variations, such as changing backgrounds, that may be encountered in real data. The Digital Imaging and Remote Sensing Image Image Generation (DIRSIG) model is a tool that produces synthetic imagery using a suite of physics-based radiation propagation modules. DIRSIG can simulate images taken from different sensors with variation in collection geometry, spectral response, solar elevation and angle, atmospheric models, target, and background. Simulation of Urban Mobility (SUMO) is a multi-modal traffic simulation tool that explicitly models vehicles that move through a given road network. The output of the SUMO model was incorporated into DIRSIG to generate scenes with moving vehicles. The same approach was used when using helicopters as targets, but with slight modifications. Using the combination of DIRSIG and SUMO, we quickly generated many small images, with the target at the center with different backgrounds. The simulations generated images with vehicles and helicopters as targets, and corresponding images without targets. Using parallel computing, 120,000 training images were generated in about an hour. Some preliminary results show an improvement in the deep learning algorithm when real image training data are augmented with

  18. A Machine Learning Approach to Test Data Generation

    DEFF Research Database (Denmark)

    Christiansen, Henning; Dahmcke, Christina Mackeprang

    2007-01-01

    Programs for gene prediction in computational biology are examples of systems for which the acquisition of authentic test data is difficult as these require years of extensive research. This has lead to test methods based on semiartificially produced test data, often produced by {\\em ad hoc......} techniques complemented by statistical models such as Hidden Markov Models (HMM). The quality of such a test method depends on how well the test data reflect the regularities in known data and how well they generalize these regularities. So far only very simplified and generalized, artificial data sets have...... been tested, and a more thorough statistical foundation is required. We propose to use logic-statistical modelling methods for machine-learning for analyzing existing and manually marked up data, integrated with the generation of new, artificial data. More specifically, we suggest to use the PRISM...

  19. Deep generative learning of location-invariant visual word recognition

    Directory of Open Access Journals (Sweden)

    Maria Grazia eDi Bono

    2013-09-01

    Full Text Available It is widely believed that orthographic processing implies an approximate, flexible coding of letter position, as shown by relative-position and transposition priming effects in visual word recognition. These findings have inspired alternative proposals about the representation of letter position, ranging from noisy coding across the ordinal positions to relative position coding based on open bigrams. This debate can be cast within the broader problem of learning location-invariant representations of written words, that is, a coding scheme abstracting the identity and position of letters (and combinations of letters from their eye-centred (i.e., retinal locations. We asked whether location-invariance would emerge from deep unsupervised learning on letter strings and what type of intermediate coding would emerge in the resulting hierarchical generative model. We trained a deep network with three hidden layers on an artificial dataset of letter strings presented at five possible retinal locations. Though word-level information (i.e., word identity was never provided to the network during training, linear decoding from the activity of the deepest hidden layer yielded near-perfect accuracy in location-invariant word recognition. Conversely, decoding from lower layers yielded a large number of transposition errors. Analyses of emergent internal representations showed that word selectivity and location invariance increased as a function of layer depth. Conversely, there was no evidence for bigram coding. Finally, the distributed internal representation of words at the deepest layer showed higher similarity to the representation elicited by the two exterior letters than by other combinations of two contiguous letters, in agreement with the hypothesis that word edges have special status. These results reveal that the efficient coding of written words – which was the model’s learning objective – is largely based on letter-level information.

  20. Deep generative learning of location-invariant visual word recognition.

    Science.gov (United States)

    Di Bono, Maria Grazia; Zorzi, Marco

    2013-01-01

    It is widely believed that orthographic processing implies an approximate, flexible coding of letter position, as shown by relative-position and transposition priming effects in visual word recognition. These findings have inspired alternative proposals about the representation of letter position, ranging from noisy coding across the ordinal positions to relative position coding based on open bigrams. This debate can be cast within the broader problem of learning location-invariant representations of written words, that is, a coding scheme abstracting the identity and position of letters (and combinations of letters) from their eye-centered (i.e., retinal) locations. We asked whether location-invariance would emerge from deep unsupervised learning on letter strings and what type of intermediate coding would emerge in the resulting hierarchical generative model. We trained a deep network with three hidden layers on an artificial dataset of letter strings presented at five possible retinal locations. Though word-level information (i.e., word identity) was never provided to the network during training, linear decoding from the activity of the deepest hidden layer yielded near-perfect accuracy in location-invariant word recognition. Conversely, decoding from lower layers yielded a large number of transposition errors. Analyses of emergent internal representations showed that word selectivity and location invariance increased as a function of layer depth. Word-tuning and location-invariance were found at the level of single neurons, but there was no evidence for bigram coding. Finally, the distributed internal representation of words at the deepest layer showed higher similarity to the representation elicited by the two exterior letters than by other combinations of two contiguous letters, in agreement with the hypothesis that word edges have special status. These results reveal that the efficient coding of written words-which was the model's learning objective

  1. Timing and causality in the generation of learned eyelid responses

    Directory of Open Access Journals (Sweden)

    Raudel eSánchez-Campusano

    2011-08-01

    Full Text Available The cerebellum-red nucleus-facial motoneuron (Mn pathway has been reported as being involved in the proper timing of classically conditioned eyelid responses. This special type of associative learning serves as a model of event timing for studying the role of the cerebellum in dynamic motor control. Here, we have re-analyzed the firing activities of cerebellar posterior interpositus (IP neurons and orbicularis oculi (OO Mns in alert behaving cats during classical eyeblink conditioning, using a delay paradigm. The aim was to revisit the hypothesis that the IP neurons can be considered a neuronal phase-modulating device supporting OO Mns firing with an emergent timing mechanism and an explicit correlation code during learned eyelid movements. Optimized experimental and computational tools allowed us to determine the different causal relationships (temporal order and correlation code during and between trials. These intra- and inter-trial timing strategies expanding from sub-second range (millisecond timing to longer-lasting ranges (interval timing expanded the functional domain of cerebellar timing beyond motor control. Interestingly, the results supported the above-mentioned hypothesis. The causal inferences were influenced by the precise motor and premotor spike-timing in the cause-effect interval, and, in addition, the timing of the learned responses depended on cerebellar-Mn network causality. Furthermore, the timing of CRs depended upon the probability of simulated causal conditions in the cause-effect interval and not the mere duration of the inter-stimulus interval. In this work, the close relation between timing and causality was verified. It could thus be concluded that the firing activities of IP neurons may be related more to the proper performance of ongoing CRs (i.e., the proper timing as a consequence of the pertinent causality than to their generation and/or initiation.

  2. Generating highly accurate prediction hypotheses through collaborative ensemble learning

    Science.gov (United States)

    Arsov, Nino; Pavlovski, Martin; Basnarkov, Lasko; Kocarev, Ljupco

    2017-03-01

    Ensemble generation is a natural and convenient way of achieving better generalization performance of learning algorithms by gathering their predictive capabilities. Here, we nurture the idea of ensemble-based learning by combining bagging and boosting for the purpose of binary classification. Since the former improves stability through variance reduction, while the latter ameliorates overfitting, the outcome of a multi-model that combines both strives toward a comprehensive net-balancing of the bias-variance trade-off. To further improve this, we alter the bagged-boosting scheme by introducing collaboration between the multi-model’s constituent learners at various levels. This novel stability-guided classification scheme is delivered in two flavours: during or after the boosting process. Applied among a crowd of Gentle Boost ensembles, the ability of the two suggested algorithms to generalize is inspected by comparing them against Subbagging and Gentle Boost on various real-world datasets. In both cases, our models obtained a 40% generalization error decrease. But their true ability to capture details in data was revealed through their application for protein detection in texture analysis of gel electrophoresis images. They achieve improved performance of approximately 0.9773 AUROC when compared to the AUROC of 0.9574 obtained by an SVM based on recursive feature elimination.

  3. A Bayesian Generative Model for Learning Semantic Hierarchies

    Directory of Open Access Journals (Sweden)

    Roni eMittelman

    2014-05-01

    Full Text Available Building fine-grained visual recognition systems that are capable of recognizing tens of thousands of categories, has received much attention in recent years. The well known semantic hierarchical structure of categories and concepts, has been shown to provide a key prior which allows for optimal predictions. The hierarchical organization of various domains and concepts has been subject to extensive research, and led to the development of the WordNet domains hierarchy [18], which was also used to organize the images in the ImageNet [11] dataset, in which the category count approaches the human capacity. Still, for the human visual system, the form of the hierarchy must be discovered with minimal use of supervision or innate knowledge. In this work, we propose a new Bayesian generative model for learning such domain hierarchies, based on semantic input. Our model is motivated by the super-subordinate organization of domain labels and concepts that characterizes WordNet, and accounts for several important challenges: maintaining context information when progressing deeper into the hierarchy, learning a coherent semantic concept for each node, and modeling uncertainty in the perception process.

  4. A Bayesian generative model for learning semantic hierarchies.

    Science.gov (United States)

    Mittelman, Roni; Sun, Min; Kuipers, Benjamin; Savarese, Silvio

    2014-01-01

    Building fine-grained visual recognition systems that are capable of recognizing tens of thousands of categories, has received much attention in recent years. The well known semantic hierarchical structure of categories and concepts, has been shown to provide a key prior which allows for optimal predictions. The hierarchical organization of various domains and concepts has been subject to extensive research, and led to the development of the WordNet domains hierarchy (Fellbaum, 1998), which was also used to organize the images in the ImageNet (Deng et al., 2009) dataset, in which the category count approaches the human capacity. Still, for the human visual system, the form of the hierarchy must be discovered with minimal use of supervision or innate knowledge. In this work, we propose a new Bayesian generative model for learning such domain hierarchies, based on semantic input. Our model is motivated by the super-subordinate organization of domain labels and concepts that characterizes WordNet, and accounts for several important challenges: maintaining context information when progressing deeper into the hierarchy, learning a coherent semantic concept for each node, and modeling uncertainty in the perception process.

  5. The relationship between student-generated learning issues and self-study in problem-based learning

    NARCIS (Netherlands)

    D.H.J.M. Dolmans (Diana); H.G. Schmidt (Henk); W.H. Gijselaers (Wim)

    1994-01-01

    textabstractA major assumption of problem-based learning (PBL) is that learning issues, generated by students while discussing a problem, are used as guides for self-directed learning activities. This assumption, though basic to PBL, has never been tested. At the University of Limburg, the Netherlan

  6. Accuracy Feedback Improves Word Learning from Context: Evidence from a Meaning-Generation Task

    Science.gov (United States)

    Frishkoff, Gwen A.; Collins-Thompson, Kevyn; Hodges, Leslie; Crossley, Scott

    2016-01-01

    The present study asked whether accuracy feedback on a meaning generation task would lead to improved contextual word learning (CWL). Active generation can facilitate learning by increasing task engagement and memory retrieval, which strengthens new word representations. However, forced generation results in increased errors, which can be…

  7. Profiting from customer relationship management : The overlooked role of generative learning orientation

    OpenAIRE

    Herhausen, Dennis; Schögel, Marcus

    2013-01-01

    This study aims to examine the direct and moderating effects of generative learning on customer performance. The authors test the relationships between CRM capabilities, generative learning, customer performance, and financial performance with a cross industry survey of CEOs and senior marketing executives from 199 firms. Partial Least Squares are used to estimate the parameters of the resulting model. The results reveal that generative learning affects customer performance directly. ...

  8. Development of Web-Based Learning Application for Generation Z

    Science.gov (United States)

    Hariadi, Bambang; Dewiyani Sunarto, M. J.; Sudarmaningtyas, Pantjawati

    2016-01-01

    This study aimed to develop a web-based learning application as a form of learning revolution. The form of learning revolution includes the provision of unlimited teaching materials, real time class organization, and is not limited by time or place. The implementation of this application is in the form of hybrid learning by using Google Apps for…

  9. 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

  10. A review of literature on the use of machine learning methods for opinion mining

    Directory of Open Access Journals (Sweden)

    Aytuğ ONAN

    2016-05-01

    Full Text Available Opinion mining is an emerging field which uses methods of natural language processing, text mining and computational linguistics to extract subjective information of opinion holders. Opinion mining can be viewed as a classification problem. Hence, machine learning based methods are widely employed for sentiment classification. Machine learning based methods in opinion mining can be mainly classified as supervised, semi-supervised and unsupervised methods. In this study, main existing literature on the use of machine learning methods for opinion mining has been presented. Besides, the weak and strong characteristics of machine learning methods have been discussed.

  11. Developing Students' Listening Metacognitive Strategies Using Online Videotext Self-Dictation-Generation Learning Activity

    Science.gov (United States)

    Chang, Ching; Chang, Chih-Kai

    2014-01-01

    The study is based on the use of a flexible learning framework to help students improve information processes underlying strategy instruction in EFL listening. By exploiting the online videotext self-dictation-generation (video-SDG) learning activity implemented on the YouTube caption manager platform, the learning cycle was emphasized to promote…

  12. First-Generation Student Success: The Role of Faculty Interaction in Service Learning Courses

    Science.gov (United States)

    McKay, Valerie C.; Estrella, Jeremy

    2008-01-01

    Do service learning courses offer the opportunity for first-generation students to experience academic and social integration and ultimately, academic success? Our study answered this question by exploring the quality of interaction between first-generation students and faculty that characterizes service learning pedagogy, and by revealing ways in…

  13. Generating a Two-Phase Lesson for Guiding Beginners to Learn Basic Dance Movements

    Science.gov (United States)

    Yang, Yang; Leung, Howard; Yue, Lihua; Deng, Liqun

    2013-01-01

    In this paper, an automated lesson generation system for guiding beginners to learn basic dance movements is proposed. It analyzes the dance to generate a two-phase lesson which can provide a suitable cognitive load thus offering an efficient learning experience. In the first phase, the dance is divided into small pieces which are patterns, and…

  14. Understanding a Generative Learning Model of Instruction: A Case Study of Elementary Teacher Planning.

    Science.gov (United States)

    Flick, Lawrence B.

    1996-01-01

    Reasons for not using generative learning or inquiry-oriented strategies in teaching include the fact that it takes too much time to develop appropriate materials and the instructional pace is too slow. This research studies the thinking of elementary teachers concerning a generative learning model of instruction as they developed unit plans for…

  15. Generating a Two-Phase Lesson for Guiding Beginners to Learn Basic Dance Movements

    Science.gov (United States)

    Yang, Yang; Leung, Howard; Yue, Lihua; Deng, Liqun

    2013-01-01

    In this paper, an automated lesson generation system for guiding beginners to learn basic dance movements is proposed. It analyzes the dance to generate a two-phase lesson which can provide a suitable cognitive load thus offering an efficient learning experience. In the first phase, the dance is divided into small pieces which are patterns, and…

  16. Web Log Pre-processing and Analysis for Generation of Learning Profiles in Adaptive E-learning

    Directory of Open Access Journals (Sweden)

    Radhika M. Pai

    2016-04-01

    Full Text Available Adaptive E-learning Systems (AESs enhance the efficiency of online courses in education by providing personalized contents and user interfaces that changes according to learner’s requirements and usage patterns. This paper presents the approach to generate learning profile of each learner which helps to identify the learning styles and provide Adaptive User Interface which includes adaptive learning components and learning material. The proposed method analyzes the captured web usage data to identify the learning profile of the learners. The learning profiles are identified by an algorithmic approach that is based on the frequency of accessing the materials and the time spent on the various learning components on the portal. The captured log data is pre-processed and converted into standard XML format to generate learners sequence data corresponding to the different sessions and time spent. The learning style model adopted in this approach is Felder-Silverman Learning Style Model (FSLSM. This paper also presents the analysis of learner’s activities, preprocessed XML files and generated sequences.

  17. Web Log Pre-processing and Analysis for Generation of Learning Profiles in Adaptive E-learning

    Directory of Open Access Journals (Sweden)

    Radhika M. Pai

    2016-03-01

    Full Text Available Adaptive E-learning Systems (AESs enhance the efficiency of online courses in education by providing personalized contents and user interfaces that changes according to learner’s requirements and usage patterns. This paper presents the approach to generate learning profile of each learner which helps to identify the learning styles and provide Adaptive User Interface which includes adaptive learning components and learning material. The proposed method analyzes the captured web usage data to identify the learning profile of the learners. The learning profiles are identified by an algorithmic approach that is based on the frequency of accessing the materials and the time spent on the various learning components on the portal. The captured log data is pre-processed and converted into standard XML format to generate learners sequence data corresponding to the different sessions and time spent. The learning style model adopted in this approach is Felder-Silverman Learning Style Model (FSLSM. This paper also presents the analysis of learner’s activities, preprocessed XML files and generated sequences.

  18. Promotion of Intrinsic Motivation of New Generation Learners for Learning Physics by Digital Physics Labs

    OpenAIRE

    Peciuliauskiene, Palmira

    2015-01-01

    The article deals with the role of digital Physics experiments in the promotion of intrinsic motivation of secondary school age learners for learning Physics. The methodological basis of research is inquiry-based learning. The article focuses on the second level of inquiry-based learning referred to as structured inquiry. The study is based on the sociological approach, with the emphasis on the new generation (Generation Z) and their exclusive relationship to technology. The research problem ...

  19. Learning to Make Predictions In Partially Observable Environments Without a Generative Model

    OpenAIRE

    Talvitie, Erik; Singh, Satinder

    2014-01-01

    When faced with the problem of learning a model of a high-dimensional environment, a common approach is to limit the model to make only a restricted set of predictions, thereby simplifying the learning problem. These partial models may be directly useful for making decisions or may be combined together to form a more complete, structured model. However, in partially observable (non-Markov) environments, standard model-learning methods learn generative models, i.e. models that provide a probab...

  20. A Cross Sectional Study of the Differences between Generation, Learning Style, Modality and Learning Outcomes within a Faculty Development Program

    Science.gov (United States)

    Weeks, Joseph A., Jr.

    2014-01-01

    This research project was a descriptive study which measured the differences between generation, learning style, modality of course delivery and learning outcomes of the education sessions for participants in the regional higher education institution's professional development program. This research study focused on the faculty development program…

  1. Combining Generative and Discriminative Representation Learning for Lung CT Analysis With Convolutional Restricted Boltzmann Machines.

    Science.gov (United States)

    van Tulder, Gijs; de Bruijne, Marleen

    2016-05-01

    The choice of features greatly influences the performance of a tissue classification system. Despite this, many systems are built with standard, predefined filter banks that are not optimized for that particular application. Representation learning methods such as restricted Boltzmann machines may outperform these standard filter banks because they learn a feature description directly from the training data. Like many other representation learning methods, restricted Boltzmann machines are unsupervised and are trained with a generative learning objective; this allows them to learn representations from unlabeled data, but does not necessarily produce features that are optimal for classification. In this paper we propose the convolutional classification restricted Boltzmann machine, which combines a generative and a discriminative learning objective. This allows it to learn filters that are good both for describing the training data and for classification. We present experiments with feature learning for lung texture classification and airway detection in CT images. In both applications, a combination of learning objectives outperformed purely discriminative or generative learning, increasing, for instance, the lung tissue classification accuracy by 1 to 8 percentage points. This shows that discriminative learning can help an otherwise unsupervised feature learner to learn filters that are optimized for classification.

  2. Smart e-Learning: A greater perspective; from the fourth to the fifth generation e-learning

    Directory of Open Access Journals (Sweden)

    Shehab A. Gamalel-Din

    2010-06-01

    This research has focused on improving the effectiveness and quality of web-based e-learning through adapting the course authoring and delivery to match each individual student skills and preferences. In this article, we shed lights on the vision and status of the eight-year Smart e-Learning environment project: The main objective of this project is to employ AI techniques to advance e-learning forward towards the fifth generation e-learning as we envision it. The idea is to embed instructional design theories as well as learning and cognition theories into e-learning environments to provide a more intelligent and, hence, more effective one-to-one e-learning environments. This article only gives a high level overview; however, the more interested reader will be referred to articles describing the work in more technical details.

  3. Self-Regulated Learning and Externally Generated Feedback with Hypermedia

    Science.gov (United States)

    Moos, Daniel C.

    2011-01-01

    Think-aloud and self-report data from 65 undergraduates were used to examine the effect of feedback on self-regulation during learning with hypermedia. Participants, randomly assigned to one of three conditions (Control, Questions, Questions+Feedback), used hypermedia for 30 minutes to learn about the circulatory system. Results indicated that…

  4. Towards a New Generation of Multimedia Learning Research

    Science.gov (United States)

    Samaras, Haido; Giouvanakis, Thanasis; Bousiou, Despina; Tarabanis, Konstantinos

    2006-01-01

    Empirical research regarding the impact of multimedia on learning can be traced back several decades before the large-scale invasion of multimedia learning resources (like Cd-ROM titles and Internet applications) into the educational field and originated from areas outside the educational community. Although the results are not decisive, two…

  5. Combining generative and discriminative representation learning for lung CT analysis with convolutional restricted Boltzmann machines

    DEFF Research Database (Denmark)

    van Tulder, Gijs; de Bruijne, Marleen

    2016-01-01

    unlabeled data, but does not necessarily produce features that are optimal for classification. In this paper we propose the convolutional classification restricted Boltzmann machine, which combines a generative and a discriminative learning objective. This allows it to learn filters that are good both......The choice of features greatly influences the performance of a tissue classification system. Despite this, many systems are built with standard, predefined filter banks that are not optimized for that particular application. Representation learning methods such as restricted Boltzmann machines may...... outperform these standard filter banks because they learn a feature description directly from the training data. Like many other representation learning methods, restricted Boltzmann machines are unsupervised and are trained with a generative learning objective; this allows them to learn representations from...

  6. New Learning Strategies for Generation X. ERIC Digest No. 184.

    Science.gov (United States)

    Brown, Bettina Lankard

    The gap between Generation X and earlier generations represents much more than age and technological differences. It reflects the effects of a changing society on a generation. Social changes such as the increase in single-parent households and households with both parents working, corporate downsizing and layoffs, limited opportunities for career…

  7. The comparative effect of individually-generated vs. collaboratively-generated computer-based concept mapping on science concept learning

    Science.gov (United States)

    Kwon, So Young

    Using a quasi-experimental design, the researcher investigated the comparative effects of individually-generated and collaboratively-generated computer-based concept mapping on middle school science concept learning. Qualitative data were analyzed to explain quantitative findings. One hundred sixty-one students (74 boys and 87 girls) in eight, seventh grade science classes at a middle school in Southeast Texas completed the entire study. Using prior science performance scores to assure equivalence of student achievement across groups, the researcher assigned the teacher's classes to one of the three experimental groups. The independent variable, group, consisted of three levels: 40 students in a control group, 59 students trained to individually generate concept maps on computers, and 62 students trained to collaboratively generate concept maps on computers. The dependent variables were science concept learning as demonstrated by comprehension test scores, and quality of concept maps created by students in experimental groups as demonstrated by rubric scores. Students in the experimental groups received concept mapping training and used their newly acquired concept mapping skills to individually or collaboratively construct computer-based concept maps during study time. The control group, the individually-generated concept mapping group, and the collaboratively-generated concept mapping group had equivalent learning experiences for 50 minutes during five days, excepting that students in a control group worked independently without concept mapping activities, students in the individual group worked individually to construct concept maps, and students in the collaborative group worked collaboratively to construct concept maps during their study time. Both collaboratively and individually generated computer-based concept mapping had a positive effect on seventh grade middle school science concept learning but neither strategy was more effective than the other. However

  8. Theory-generating practice. Proposing a principle for learning design

    DEFF Research Database (Denmark)

    Buhl, Mie

    2016-01-01

    building, and takes tacit knowledge into account. The article introduces TGP, contextualizes it to a Danish tradition of didactics, and discusses it in relation to contemporary conceptual currents of didactic design and learning design. This is followed by a theoretical framing of TGP. Finally, three...... empirical examples from bachelor and master programs involving technology are used to demonstrate three ways of practicing this alternative learning design....

  9. Parameter-Free Spectral Kernel Learning

    CERN Document Server

    Mao, Qi

    2012-01-01

    Due to the growing ubiquity of unlabeled data, learning with unlabeled data is attracting increasing attention in machine learning. In this paper, we propose a novel semi-supervised kernel learning method which can seamlessly combine manifold structure of unlabeled data and Regularized Least-Squares (RLS) to learn a new kernel. Interestingly, the new kernel matrix can be obtained analytically with the use of spectral decomposition of graph Laplacian matrix. Hence, the proposed algorithm does not require any numerical optimization solvers. Moreover, by maximizing kernel target alignment on labeled data, we can also learn model parameters automatically with a closed-form solution. For a given graph Laplacian matrix, our proposed method does not need to tune any model parameter including the tradeoff parameter in RLS and the balance parameter for unlabeled data. Extensive experiments on ten benchmark datasets show that our proposed two-stage parameter-free spectral kernel learning algorithm can obtain comparable...

  10. On the Use of Extended TAM to Assess Students' Acceptance and Intent to Use Third-Generation Learning Management Systems

    Science.gov (United States)

    Ros, Salvador; Hernández, Roberto; Caminero, Agustín; Robles, Antonio; Barbero, Isabel; Maciá, Araceli; Holgado, Francisco Pablo

    2015-01-01

    Service-oriented e-learning platforms can be considered as a third generation of learning management systems (LMSs). As opposed to the previous generations, consisting of ad hoc solutions and traditional LMS, this new technology contemplates e-learning systems as services that can be integrated into different learning scenarios. This paper shows…

  11. Organisational Learning as an Emerging Process: The Generative Role of Digital Tools in Informal Learning Practices

    Science.gov (United States)

    Za, Stefano; Spagnoletti, Paolo; North-Samardzic, Andrea

    2014-01-01

    Increasing attention is paid to organisational learning, with the success of contemporary organisations strongly contingent on their ability to learn and grow. Importantly, informal learning is argued to be even more significant than formal learning initiatives. Given the widespread use of digital technologies in the workplace, what requires…

  12. Organisational Learning as an Emerging Process: The Generative Role of Digital Tools in Informal Learning Practices

    Science.gov (United States)

    Za, Stefano; Spagnoletti, Paolo; North-Samardzic, Andrea

    2014-01-01

    Increasing attention is paid to organisational learning, with the success of contemporary organisations strongly contingent on their ability to learn and grow. Importantly, informal learning is argued to be even more significant than formal learning initiatives. Given the widespread use of digital technologies in the workplace, what requires…

  13. An Approach to Metadata Generation for Learning Objects

    Science.gov (United States)

    Menendez D., Victor; Zapata G., Alfredo; Vidal C., Christian; Segura N., Alejandra; Prieto M., Manuel

    Metadata describe instructional resources and define their nature and use. Metadata are required to guarantee reusability and interchange of instructional resources into e-Learning systems. However, fulfilment of large metadata attributes is a hard and complex task for almost all LO developers. As a consequence many mistakes are made. This can cause the impoverishment of data quality in indexing, searching and recovering process. We propose a methodology to build Learning Objects from digital resources. The first phase includes automatic preprocessing of resources using techniques from information retrieval. Initial metadata obtained in this first phase are then used to search similar LO to propose missed metadata. The second phase considers assisted activities that merge computer advice with human decisions. Suggestions are based on metadata of similar Learning Object using fuzzy logic theory.

  14. Creating Next Generation Blended Learning Environments Using Mixed Reality, Video Games and Simulations

    Science.gov (United States)

    Kirkley, Sonny E.; Kirkley, Jamie R.

    2005-01-01

    In this article, the challenges and issues of designing next generation learning environments using current and emerging technologies are addressed. An overview of the issues is provided as well as design principles that support the design of instruction and the overall learning environment. Specific methods for creating cognitively complex,…

  15. Using the Motivated Strategies for Learning Questionnaire and the Strategy Inventory for Language Learning in Assessing Motivation and Learning Strategies of Generation 1.5 Korean Immigrant Students

    Directory of Open Access Journals (Sweden)

    Rosa Stoffa

    2011-01-01

    Full Text Available This study examined the potential of utilizing the Motivated Strategies for Learning Questionnaire (MSLQ and the Strategy Inventory for Language Learning (SILL as instruments in measuring Generation 1.5 students' motivation and their use of language learning strategies. The MSLQ was of particular interest because it contains both a basic motivation subscale as well as a motivation/language learning strategies subscale. Participants of this study were 104 Generation 1.5 Korean immigrant students who were members of Korean communities located in Pittsburgh and Philadelphia, Pennsylvania. Participants provided general demographic information and completed both scales in a counterbalanced manner. Results indicated that while the two scales do have some similar content, the scales do not overlap entirely and appeared to measure two discrete indices. Results also indicated that a moderate correlation between MSLQ learning strategies and SILL learning strategies was found as well as between the SILL total score and the MSLQ total score.

  16. Entrepreneurial learning requires action on the meaning generated

    DEFF Research Database (Denmark)

    Brink, Tove; Madsen, Svend Ole

    2015-01-01

    approach to integrating essential large-scale industry players and other SME managers to create extended action and value from learning. Originality/value: The findings reveal the need for a very different approach and extended theory development for SME training than the approach used for the training...

  17. 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…

  18. Learning and inference using complex generative models in a spatial localization task.

    Science.gov (United States)

    Bejjanki, Vikranth R; Knill, David C; Aslin, Richard N

    2016-01-01

    A large body of research has established that, under relatively simple task conditions, human observers integrate uncertain sensory information with learned prior knowledge in an approximately Bayes-optimal manner. However, in many natural tasks, observers must perform this sensory-plus-prior integration when the underlying generative model of the environment consists of multiple causes. Here we ask if the Bayes-optimal integration seen with simple tasks also applies to such natural tasks when the generative model is more complex, or whether observers rely instead on a less efficient set of heuristics that approximate ideal performance. Participants localized a "hidden" target whose position on a touch screen was sampled from a location-contingent bimodal generative model with different variances around each mode. Over repeated exposure to this task, participants learned the a priori locations of the target (i.e., the bimodal generative model), and integrated this learned knowledge with uncertain sensory information on a trial-by-trial basis in a manner consistent with the predictions of Bayes-optimal behavior. In particular, participants rapidly learned the locations of the two modes of the generative model, but the relative variances of the modes were learned much more slowly. Taken together, our results suggest that human performance in a more complex localization task, which requires the integration of sensory information with learned knowledge of a bimodal generative model, is consistent with the predictions of Bayes-optimal behavior, but involves a much longer time-course than in simpler tasks.

  19. Efficient generation of pronunciation dictionaries: machine learning factors during bootstrapping

    CSIR Research Space (South Africa)

    Davel, MH

    2004-10-01

    Full Text Available significantly. Further work relates to ex- ploring the ways in which the algorithmic requirements change for different phases of the bootstrapping process. 6. Acknowledgements This work was supported by the CSIR Information Society Technologies Centre... automatically,? in Proceedings of the International Conference on Acoustics and Speech Signal Processing (ICASSP), Minneapolis, 1993, vol. 2, pp. 199? 202. [5] T.J. Sejnowski and C.R. Rosenberg, ?Parallel networks that learn to pronounce english text...

  20. Generational Learning Style Preferences Based on Computer-Based Healthcare Training

    Science.gov (United States)

    Knight, Michaelle H.

    2016-01-01

    Purpose. The purpose of this mixed-method study was to determine the degree of perceived differences for auditory, visual and kinesthetic learning styles of Traditionalist, Baby Boomers, Generation X and Millennial generational healthcare workers participating in technology-assisted healthcare training. Methodology. This mixed-method research…

  1. Generation Y Students: Appropriate Learning Styles and Teaching Approaches in the Economic and Management Sciences Faculty

    Science.gov (United States)

    Wessels, P. L.; Steenkamp, L. P.

    2009-01-01

    Generation Y students (born after 1982) have developed a different set of attitudes and aptitudes as a result of growing up in an IT and media-rich environment. This article has two objectives: firstly to discuss the learning styles preferred by generation Y students in order to identify the effect of these preferences on tertiary education in…

  2. Leading and Managing Disparate Generations in Cross-Cultural Learning Organizations

    Science.gov (United States)

    Mujtaba, Bahaudin; Thomas, Gimol

    2005-01-01

    The enclosed literature focuses on learning about the various generations of the workforce and techniques that employers can utilize to organize collaborative teams in today's multigenerational and multicultural workplaces. Trainers and teachers can use this material to provide effective skills for managers that deal with a multi-generation of…

  3. Using the FotoFeedback Method to Increase Reflective Learning in the Millennial Generation

    Science.gov (United States)

    Tornabene, Ladona; Nowak, Amy Versnik; Vogelsang, Lisa

    2012-01-01

    This current generation of students, known as the Millennial Generation, has a propensity toward multi-tasking and a history of structured and tightly filled days. Reflection may not be viewed as productive and as conducive to learning as other "tasks" and thus may be neglected. However, by employing a methodology (photography) that…

  4. Generational Learning Style Preferences Based on Computer-Based Healthcare Training

    Science.gov (United States)

    Knight, Michaelle H.

    2016-01-01

    Purpose. The purpose of this mixed-method study was to determine the degree of perceived differences for auditory, visual and kinesthetic learning styles of Traditionalist, Baby Boomers, Generation X and Millennial generational healthcare workers participating in technology-assisted healthcare training. Methodology. This mixed-method research…

  5. Learning to Generate Dialogue: Theory, Practice, and Evaluation

    DEFF Research Database (Denmark)

    McCallie, Ellen; Simonsson, Elin; Gammon, Ben

    2007-01-01

    -commonly called dialogue events-that bring scientific and technical experts, social scientists, and policymakers into discussion with members of the public about contemporary science-based issues. This article clarifies the difference between non-policy-informing dialogue events and other interactions in museums......Over the past decade in the UK, communities of scientists, governmental bodies, and informal learning organizations have increasingly promoted public engagement with science. One of the most visible features of these efforts within museums is the staging of adult-focused, face-to-face forums...... of engagement through dialogue related to science and society...

  6. Rewired: Understanding the generation and the Way They Learn

    Science.gov (United States)

    Rosen, Larry D.

    2010-01-01

    Look around at today's youth and you can see how technology has changed their lives. They lie on their beds and study while listening to mp3 players, texting and chatting online with friends, and reading and posting Facebook messages. How does the new, charged-up, multitasking generation respond to traditional textbooks and lectures? Are we…

  7. Teaching and Learning Morphology: A Reflection on Generative Vocabulary Instruction

    Science.gov (United States)

    Templeton, Shane

    2012-01-01

    Students' knowledge of morphology can play a critical role in vocabulary development, and by extension, reading comprehension and writing. This reflection describes the nature of this knowledge and how it may be developed through the examination of generative vocabulary knowledge and the role of the spelling system in developing this knowledge. In…

  8. Helping the Me Generation Decenter: Service Learning with Refugees

    Science.gov (United States)

    Hawkins, LouAnne B.; Kaplan, Leslie G.

    2016-01-01

    Recent research has empirically demonstrated that young adults today are different from prior generations in their decreased empathy, increased narcissism, and decreased civic engagement. The formative years of young adulthood are a critical period for the development of civic values and civil ideologies, a time when college-age adults need to…

  9. The Effect of Imagery Generation on Science Rule Learning.

    Science.gov (United States)

    McIntosh, William J.

    1986-01-01

    Investigated effects of teacher-induced imagery generation on rule recall and transfer using boys in ninth-grade physical science classes assigned to imagery encouragement or discouragement treatment groups. Results indicate that imagery utilization significantly facilitates rule recall and that imagery encouragement during instruction leads to…

  10. Two Models for Semi-Supervised Terrorist Group Detection

    Science.gov (United States)

    Ozgul, Fatih; Erdem, Zeki; Bowerman, Chris

    Since discovery of organization structure of offender groups leads the investigation to terrorist cells or organized crime groups, detecting covert networks from crime data are important to crime investigation. Two models, GDM and OGDM, which are based on another representation model - OGRM are developed and tested on nine terrorist groups. GDM, which is basically depending on police arrest data and “caught together” information and OGDM, which uses a feature matching on year-wise offender components from arrest and demographics data, performed well on terrorist groups, but OGDM produced high precision with low recall values. OGDM uses a terror crime modus operandi ontology which enabled matching of similar crimes.

  11. Denoising by semi-supervised kernel PCA preimaging

    DEFF Research Database (Denmark)

    Hansen, Toke Jansen; Abrahamsen, Trine Julie; Hansen, Lars Kai

    2014-01-01

    Kernel Principal Component Analysis (PCA) has proven a powerful tool for nonlinear feature extraction, and is often applied as a pre-processing step for classification algorithms. In denoising applications Kernel PCA provides the basis for dimensionality reduction, prior to the so-called pre...... by incorporating a loss term, leading to an iterative algorithm for finding orthonormal components biased by the class labels, and (2) a fixed-point iteration for solving the pre-image problem based on a manifold warped RKHS. We prove viability of the proposed methods on both synthetic data and images from...

  12. Differences in Learning Preferences by Generational Cohort: Implications for Instructional Design in Corporate Web-Based Learning

    Science.gov (United States)

    Kriegel, Jessica

    2013-01-01

    In today's global and high-tech economy, the primary contributing factor to sustainable competitive advantage is the strategic development of employees, an organization's only unique asset. However, with four generations actively present in the workforce and the proliferation of web-based learning as a key method for developing…

  13. Differences in Learning Preferences by Generational Cohort: Implications for Instructional Design in Corporate Web-Based Learning

    Science.gov (United States)

    Kriegel, Jessica

    2013-01-01

    In today's global and high-tech economy, the primary contributing factor to sustainable competitive advantage is the strategic development of employees, an organization's only unique asset. However, with four generations actively present in the workforce and the proliferation of web-based learning as a key method for developing…

  14. Preferences for Learning and Skill Development at Work: Comparison of Two Generations

    Directory of Open Access Journals (Sweden)

    Mariya Karaivanova

    2014-09-01

    Full Text Available The changing economic conditions of the current dynamic and insecure labour market make learning a constant preoccupation of the workforce with view of meeting the growing qualification demands. These demands are likely to influence the work preferences of both young people now entering the labour market and older people with established career paths. Research findings suggest that the younger generation exhibits a stronger orientation towards learning and skill development as compared to the older generations. Moreover, studies show that the younger people are more ready to leave the organization when they have better learning opportunities elsewhere. The present study aims at establishing how preferences for learning and skill development in the workplace relate to a number of job and organizational characteristics. Particular focus is placed on the predictive capacity of perceived learning opportunities towards the tendency to leave the organization for either of the two generations. The study addresses work preferences of two generations in the Bulgarian labour market. To this aim, 121 respondents answered a 55-item questionnaire consisting of newly developed scales as well as scales based on or adopted from standardized instruments such as the Extended Delft Measurement Kit (Roe et al., 2000. Contrary to findings from previous research done in countries with different cultural and socio-economic background, the older people in our sample were more eager to learn and more ready to leave their organization in pursuit of better opportunities, as compared to the younger generation. Another noteworthy conclusion is that the preferences for learning and development form different patterns in each of the two age groups and are expressed in a different way for each of the two generations.

  15. A Corrective Training Algorithm for Adaptive Learning in Bag Generation

    CERN Document Server

    Chen, H H; Chen, Hsin-Hsi; Lee, Yue-Shi

    1994-01-01

    The sampling problem in training corpus is one of the major sources of errors in corpus-based applications. This paper proposes a corrective training algorithm to best-fit the run-time context domain in the application of bag generation. It shows which objects to be adjusted and how to adjust their probabilities. The resulting techniques are greatly simplified and the experimental results demonstrate the promising effects of the training algorithm from generic domain to specific domain. In general, these techniques can be easily extended to various language models and corpus-based applications.

  16. Learning as a Generative Activity%学习是一种生成活动

    Institute of Scientific and Technical Information of China (English)

    洛根·费奥雷拉[美; 理查德E.梅耶[著; 陆琦; 盛群力

    2016-01-01

    This paper is based on the idea that meaningful learning is a generative activity in which the learner actively seeks to make sense of the presented material.Generative learning is of great significance towards the research on science of learning,science of assessment,and science of instruction.Generative learning takes place when the learner engages in appropriate cognitive processing during learning.Generative learning is demonstrated when students who learn with generative learning strategies or generative instructional methods perform better on transfer tests than students who learn from standard instruction.Generative learning can be promoted by using appropriate instructional methods or learning strategies.There are eight generative learning strategies that have been testified,namely,summarizing,mapping,drawing,imagining,self-testing,self-explaining,teaching,and enacting.Rooted in the work of Wittrock and others,the concept of generative learning continues as a dominant view of today’s learning research area,and represents the prospect of future learning science research.%意义学习是一种生成活动,即学习者总是努力想去理解所呈现的材料。生成学习对研究学习科学、评估科学和教学科学都有着重要意义。生成学习发生于学习者在学习时进行适当认知加工的过程中;与接受标准式教学的学生们相比,在学习过程中接受生成学习策略或者生成教学方法的学生们会在知识迁移上做得更好;生成学习会在使用得当的教学方法或学习策略下得到强化。八种已经得到研究证实的生成学习策略是善作小结、结构映射、绘制图示、联想要义、自我检查、自我解释、乐于教人与生动再现。生成学习的概念植根于威特洛克以及其他学者的研究成果,它不仅是当下学习研究领域的主流观点,也代表了未来学习科学研究发展的前景方向。

  17. Learning about the Milky Way potential with generative stream models

    Science.gov (United States)

    McMillan, Paul

    2015-08-01

    Streams are formed when satellites of a galaxy are pulled apart by tidal forces and the stars then drift apart because they are placed on different orbits. Therefore it is the difference between the orbits that determines the shape of the stream (rather than the stream nearly following a single orbit). This means that a good model of the structure of a stream can be defined in terms of orbital frequencies and angle coordinates.I’ll talk about a new method for creating generative models of streams based on this insight. Given that the orbital frequencies are directly related to the actions, the method of torus modelling (which finds the orbits corresponding to a given value of actions) is ideally suited to the problem. I’ll show results from a new method that interpolates between orbits (tori), to rapidly generate stream models that can be used to determine the gravitational potential that the stream is moving in. This method has now been made publicly available.

  18. Preferences for Learning and Skill Development at Work: Comparison of Two Generations

    OpenAIRE

    Mariya Karaivanova; Irina Zinovieva

    2014-01-01

    The changing economic conditions of the current dynamic and insecure labour market make learning a constant preoccupation of the workforce with view of meeting the growing qualification demands. These demands are likely to influence the work preferences of both young people now entering the labour market and older people with established career paths. Research findings suggest that the younger generation exhibits a stronger orientation towards learning and skill development as compared to the...

  19. Innovating Training through Immersive Environments: Generation Y, Exploratory Learning, and Serious Games

    Science.gov (United States)

    Gendron, Gerald

    2012-01-01

    Over the next decade, those entering Service and Joint Staff positions within the military will come from a different generation than the current leadership. They will come from Generation Y and have differing preferences for learning. Immersive learning environments like serious games and virtual world initiatives can complement traditional training methods to provide a better overall training program for staffs. Generation Y members desire learning methods which are relevant and interactive, regardless of whether they are delivered over the internet or in person. This paper focuses on a project undertaken to assess alternative training methods to teach special operations staffs. It provides a summary of the needs analysis used to consider alternatives and to better posture the Department of Defense for future training development.

  20. Learning a generative probabilistic grammar of experience: a process-level model of language acquisition.

    Science.gov (United States)

    Kolodny, Oren; Lotem, Arnon; Edelman, Shimon

    2015-03-01

    We introduce a set of biologically and computationally motivated design choices for modeling the learning of language, or of other types of sequential, hierarchically structured experience and behavior, and describe an implemented system that conforms to these choices and is capable of unsupervised learning from raw natural-language corpora. Given a stream of linguistic input, our model incrementally learns a grammar that captures its statistical patterns, which can then be used to parse or generate new data. The grammar constructed in this manner takes the form of a directed weighted graph, whose nodes are recursively (hierarchically) defined patterns over the elements of the input stream. We evaluated the model in seventeen experiments, grouped into five studies, which examined, respectively, (a) the generative ability of grammar learned from a corpus of natural language, (b) the characteristics of the learned representation, (c) sequence segmentation and chunking, (d) artificial grammar learning, and (e) certain types of structure dependence. The model's performance largely vindicates our design choices, suggesting that progress in modeling language acquisition can be made on a broad front-ranging from issues of generativity to the replication of human experimental findings-by bringing biological and computational considerations, as well as lessons from prior efforts, to bear on the modeling approach. Copyright © 2014 Cognitive Science Society, Inc.

  1. Implementation of Imitation Learning using Natural Learner Central Pattern Generator Neural Networks.

    Science.gov (United States)

    Shahbazi, Hamed; Parandeh, Reyhaneh; Jamshidi, Kamal

    2016-11-01

    In this paper a new design of neural networks is introduced, which is able to generate oscillatory patterns. The fundamental building block of the neural network is O-neurons that can generate an oscillation in its transfer functions. Since the natural policy gradient learning has been used in training a central pattern generator paradigm, it is called Natural Learner CPG Neural Networks (NLCPGNN). O-neurons are connected and coupled to each other in order to shape a network and their unknown parameters are found by a natural policy gradient learning algorithm. The main contribution of this paper is design of this learning algorithm which is able to simultaneously search for the weights and topology of the network. This system is capable to obtain any complex motion and rhythmic trajectory via first layer and learn rhythmic trajectories in the second layer and converge towards all these movements. Moreover this two layers system is able to provide various features of a learner model for instance resistance against perturbations, modulation of trajectories amplitude and frequency. Simulation of the learning system in the robot simulator (WEBOTS) that is linked with MATLAB software has been done. Implementation on a real NAO robot demonstrates that the robot has learned desired motion with high accuracy. These results show proposed system produces high convergence rate and low test errors.

  2. Generational influences in academic emergency medicine: teaching and learning, mentoring, and technology (part I).

    Science.gov (United States)

    Mohr, Nicholas M; Moreno-Walton, Lisa; Mills, Angela M; Brunett, Patrick H; Promes, Susan B

    2011-02-01

    For the first time in history, four generations are working together-traditionalists, baby boomers, generation Xers (Gen Xers), and millennials. Members of each generation carry with them a unique perspective of the world and interact differently with those around them. Through a review of the literature and consensus by modified Delphi methodology of the Society for Academic Emergency Medicine Aging and Generational Issues Task Force, the authors have developed this two-part series to address generational issues present in academic emergency medicine (EM). Understanding generational characteristics and mitigating strategies can help address some common issues encountered in academic EM. Through recognition of the unique characteristics of each of the generations with respect to teaching and learning, mentoring, and technology, academicians have the opportunity to strategically optimize interactions with one another.

  3. Generational Influences in Academic Emergency Medicine: Teaching and Learning, Mentoring, and Technology (Part I)

    Science.gov (United States)

    Mohr, Nicholas M.; Moreno-Walton, Lisa; Mills, Angela M.; Brunett, Patrick H.; Promes, Susan B.

    2010-01-01

    For the first time in history, four generations are working together – Traditionalists, Baby Boomers, Generation Xers, and Millennials. Members of each generation carry with them a unique perspective of the world and interact differently with those around them. Through a review of the literature and consensus by modified Delphi methodology of the Society for Academic Emergency Medicine (SAEM) Aging and Generational Issues Task Force, the authors have developed this two-part series to address generational issues present in academic emergency medicine (EM). Understanding generational characteristics and mitigating strategies can help address some common issues encountered in academic EM. Through recognition of the unique characteristics of each of the generations with respect to teaching and learning, mentoring, and technology, academicians have the opportunity to strategically optimize interactions with one another. PMID:21314779

  4. Leveraging Machine Learning to Estimate Soil Salinity through Satellite-Based Remote Sensing

    Science.gov (United States)

    Welle, P.; Ravanbakhsh, S.; Póczos, B.; Mauter, M.

    2016-12-01

    Human-induced salinization of agricultural soils is a growing problem which now affects an estimated 76 million hectares and causes billions of dollars of lost agricultural revenues annually. While there are indications that soil salinization is increasing in extent, current assessments of global salinity levels are outdated and rely heavily on expert opinion due to the prohibitive cost of a worldwide sampling campaign. A more practical alternative to field sampling may be earth observation through remote sensing, which takes advantage of the distinct spectral signature of salts in order to estimate soil conductivity. Recent efforts to map salinity using remote sensing have been met with limited success due to tractability issues of managing the computational load associated with large amounts of satellite data. In this study, we use Google Earth Engine to create composite satellite soil datasets, which combine data from multiple sources and sensors. These composite datasets contain pixel-level surface reflectance values for dates in which the algorithm is most confident that the surface contains bare soil. We leverage the detailed soil maps created and updated by the United States Geological Survey as label data and apply machine learning regression techniques such as Gaussian processes to learn a smooth mapping from surface reflection to noisy estimates of salinity. We also explore a semi-supervised approach using deep generative convolutional networks to leverage the abundance of unlabeled satellite images in producing better estimates for salinity values where we have relatively fewer measurements across the globe. The general method results in two significant contributions: (1) an algorithm that can be used to predict levels of soil salinity in regions without detailed soil maps and (2) a general framework that serves as an example for how remote sensing can be paired with extensive label data to generate methods for prediction of physical phenomenon.

  5. Generation Y Health Professional Students ’ Preferred Teaching and Learning Approaches: A Systematic Review

    Directory of Open Access Journals (Sweden)

    Caroline Mary Hills

    2017-01-01

    Full Text Available Generation Y or Millennials are descriptors for those born between 1982 and 2000. This cohort has grown up in the digital age and is purported to have different learning preferences from previous generations. Students are important stakeholders in identifying their preferred teaching and learning approaches in health professional programs. This study aimed to identify, appraise, and synthesize the best available evidence regarding the teaching and learning preferences of Generation Y health professional students. The review considered any objectively measured or self-reported outcomes of teaching and learning reported from Generation Y health professional student perspectives. In accordance with a previously published Joanna Briggs Institute Protocol, a three-step search strategy was completed. Two research articles (nursing and dental hygiene students and three dissertations (nursing were critically appraised. All studies were cross-sectional descriptive studies. A range of pedagogical approaches was reported, including lecture, group work, and teaching clinical skills. Based on the Joanna Briggs Institute levels of evidence, reviewers deemed the evidence as Level 3. Some generational differences were reported, but these were inconsistent across the studies reviewed. There is, therefore, insufficient evidence to provide specific recommendations for the preferred educational approaches of health professional students and further research is warranted.

  6. Didactic Networks: A Proposal for e-learning Content Generation

    Directory of Open Access Journals (Sweden)

    F. Javier Del Alamo

    2010-12-01

    Full Text Available The Didactic Networks proposed in this paper are based on previous publications in the field of the RSR (Rhetorical-Semantic Relations. The RSR is a set of primitive relations used for building a specific kind of semantic networks for artificial intelligence applications on the web: the RSN (Rhetorical-Semantic Networks. We bring into focus the RSR application in the field of elearning, by defining Didactic Networks as a new set of semantic patterns oriented to the development of elearning applications. The different lines we offer in our research fall mainly into three levels: (1 The most basic one is in the field of computational linguistics and related to Logical Operations on RSR (RSR Inverses and plurals, RSR combinations, etc, once they have been created. The application of Walter Bosma's results regarding rhetorical distance application and treatment as semantic weighted networks is one of the important issues here. (2 In parallel, we have been working on the creation of a knowledge representation and storage model and data architecture capable of supporting the definition of knowledge networks based on RSR. (3 The third strategic line is in the meso-level, the formulation of a molecular structure of knowledge based on the most frequently used patterns. The main contribution at this level is the set of Fundamental Cognitive Networks (FCN as an application of Novak's mental maps proposal. This paper is part of this third intermediate level, and the Fundamental Didactic Networks (FDN are the result of the application of rhetorical theory procedures to the instructional theory. We have formulated a general set of RSR capable of building discourse, making it possible to express any concept, procedure or principle in terms of knowledge nodes and RSRs. The Instructional knowledge can then be elaborated in the same way. This network structure expressing the instructional knowledge in terms of RSR makes the objective of developing web-learning

  7. Lessons learned from tubes pulled from French steam generators

    Energy Technology Data Exchange (ETDEWEB)

    Berge, Ph.; Boursier, J.M.; Dallery, D.; De Keroulas, F.; Rouillon, Y. [Electricite de France, Generating and Transmission Div. (France)

    1998-07-01

    Since 1981, the Chinon Hot Laboratory has completed more than 380 metallurgical examinations of pulled French steam generator tubes. Electricite de France decided to perform such investigations from the very outset of the French nuclear program, in order to contribute to nuclear power plant safety. The main reasons for withdrawing tubes are to evaluate the degradation, to validate non destructive examination (NDE) techniques, to gain a better understanding of cracking phenomena, and to ensure that the criteria on which plugging operations are based remain conservative. Considerable experience has been accumulated in the field of primary water stress corrosion cracking (PWSCC), OD (secondary) side corrosion, leak and burst tests, and various tube plugging techniques. This paper focuses on the PWSCC phenomenon and on the secondary side corrosion process, and in particular, attempts to correlate French data from pulled tubes with the results of fundamental R and D studies. Finally, within the framework of the Nuclear Power Plant Safety and Maintenance Policy, all these results are discussed in terms of optimization of the field inspection of tube bundles and plugging criteria. (author)

  8. Just Imagine! Learning to Emulate and Infer Actions with a Stochastic Generative Architecture

    Directory of Open Access Journals (Sweden)

    Fabian eSchrodt

    2016-03-01

    Full Text Available Theories on embodied cognition emphasize that our mind develops by processing and inferring structures given the encountered bodily experiences. Here we propose a distributed neural network architecture that learns a stochastic generative model from experiencing bodily actions. Our modular system learns from various manifolds of action perceptions in the form of (i relative positional motion of the individual body parts, (ii angular motion of joints, as well as (iii relatively stable top-down action identities. By Hebbian learning, this information is spatially segmented in separate neural modules that provide embodied state codes as well as temporal predictions of the state progression inside and across the modules. The network is generative in space and time, thus, being able to predict both, missing sensory information as well as next sensory information. We link the developing encodings to visuo-motor and multimodal representations that appear to be involved in action observation. Our results show that the system learns to infer action types as well as motor codes from partial sensory information by emulating observed actions with the own developing body model. We further evaluate the generative capabilities by showing that the system is able to generate internal imaginations of the learned types of actions without sensory stimulation, including visual images of the actions. The model highlights the important roles of motor cognition and embodied simulation for bootstrapping action understanding capabilities. We conclude that stochastic generative models appear very suitable for both, generating goal-directed actions, as well as predicting observed visuo-motor trajectories and action goals.

  9. A Machine Learning Based Analytical Framework for Semantic Annotation Requirements

    CERN Document Server

    Hassanzadeh, Hamed; 10.5121/ijwest.2011.2203

    2011-01-01

    The Semantic Web is an extension of the current web in which information is given well-defined meaning. The perspective of Semantic Web is to promote the quality and intelligence of the current web by changing its contents into machine understandable form. Therefore, semantic level information is one of the cornerstones of the Semantic Web. The process of adding semantic metadata to web resources is called Semantic Annotation. There are many obstacles against the Semantic Annotation, such as multilinguality, scalability, and issues which are related to diversity and inconsistency in content of different web pages. Due to the wide range of domains and the dynamic environments that the Semantic Annotation systems must be performed on, the problem of automating annotation process is one of the significant challenges in this domain. To overcome this problem, different machine learning approaches such as supervised learning, unsupervised learning and more recent ones like, semi-supervised learning and active learn...

  10. The Passion of Teaching: Learning from an Older Generation of Teachers

    Science.gov (United States)

    Santoro, Ninetta; Pietsch, Marilyn; Borg, Tracey

    2012-01-01

    This article reports on a qualitative small-scale case study that investigated what pre-service teachers learned from a former generation of teachers about the context and nature of teaching and teacher education during the 1950s and 1960s. Data comprised semi-structured interviews and a grounded theoretical approach was used to analyse the data.…

  11. Learning the Brain in Introductory Psychology: Examining the Generation Effect for Mnemonics and Examples

    Science.gov (United States)

    McCabe, Jennifer A.

    2015-01-01

    The goal of this research was to determine whether there is a generation effect for learner-created keyword mnemonics and real-life examples, compared to instructor-provided materials, when learning neurophysiological terms and definitions in introductory psychology. Students participated in an individual (Study 1) or small-group (Study 2)…

  12. Greening the Net Generation: Outdoor Adult Learning in the Digital Age

    Science.gov (United States)

    Walter, Pierre

    2013-01-01

    Adult learning today takes place primarily within walled classrooms or in other indoor settings, and often in front of various types of digital screens. As adults have adopted the digital technologies and indoor lifestyle attributed to the so-called "Net Generation," we have become detached from contact with the natural world outdoors.…

  13. A new generative complexity science of learning for a complex pedagogy

    NARCIS (Netherlands)

    Jörg, T.

    2007-01-01

    Proposal for the SIG Chaos and Complexity Theories at AERA 2007 Title: A New Generative Complexity Science of Learning for a Complex Pedagogy Ton Jörg IVLOS Institute of Education University of Utrecht The Netherlands A.G.D.Jorg@ivlos.uu.nl Introduction My paper focuses on the link between thinking

  14. Learner-Generated Designs in Participatory Culture: What They Are and How They Are Shaping Learning

    Science.gov (United States)

    Kim, Beaumie; Tan, Lynde; Bielaczyc, Katerine

    2015-01-01

    In this special issue, the authors purport to interrogate and further their understanding of the commonly cited term, "design," specifically "learner-generated designs." This issue brings together scholars from multiple disciplines, including learning sciences, literacy studies, science education, digital media, and pedagogy,…

  15. Modeling Causal Learning Using Bayesian Generic Priors on Generative and Preventive Powers

    OpenAIRE

    Lu, Hongjing; Yuille, Alan L; Liljeholm, Mimi; Cheng, Patricia W.; Holyoak, Keith J.

    2006-01-01

    We present a Bayesian model of causal learning that incorporates generic priors on distributions of weights representing potential powers to either produce or prevent an effect. These generic priors favor necessary and sufficient causes. Across three experiments, the model explains the systematic pattern of human judgments observed for questions regarding support for a causal link, for both generative and preventive causes.

  16. Generative Learning Theory, Paradigm Shifts, and Constructivism in Educational Psychology: A Tribute to Merl Wittrock

    Science.gov (United States)

    Tobias, Sigmund

    2010-01-01

    This appreciation of Wittrock's contributions to educational psychology suggests that his 1974 article describing generative learning theory was remarkably prescient. In that article Wittrock set the stage for the subsequent paradigm shift from cognitive to constructivist approaches to instruction. Furthermore, his suggestion that schools were the…

  17. A new generative complexity science of learning for a complex pedagogy

    NARCIS (Netherlands)

    Jörg, T.

    2007-01-01

    Proposal for the SIG Chaos and Complexity Theories at AERA 2007 Title: A New Generative Complexity Science of Learning for a Complex Pedagogy Ton Jörg IVLOS Institute of Education University of Utrecht The Netherlands A.G.D.Jorg@ivlos.uu.nl Introduction My paper focuses on the link between thinking

  18. Cortes' Multicultural Empowerment Model and¯Generative Teaching and Learning in Science

    Science.gov (United States)

    Loving, Cathleen C.

    Using Cortes' Multicultural Empowerment Model as a guide, and a moderate rational, realist philosophical framework (somewhat broadened by a post modern perspective), I adapt the Cortes' model to science teaching and to Wittrock's Model of Generative Learning and Teaching in science. My goal is to develop and demonstrate a balanced multicultural approach to teaching children of different ethnic cultures about the nature of science - one that both values and teaches their cultures and beliefs, while moving them towards important mainstream notions of good science. I justify the Cortes model by comparing it to other major multicultural approaches. I then interweave Cortes' notion of multicultural empowerment with Wittrock's generative attributes, using a lesson about plants as an example. The intent is to succeed not only in having all children learn science, but also learn about science.

  19. Effective Modification of a Nonprescription Medicines Course to Optimize Learning of Millennial Generation Students

    Directory of Open Access Journals (Sweden)

    Kristin A. Casper, Pharm.D.

    2013-01-01

    Full Text Available Objective: To describe examples of effective teaching strategies utilized within a required nonprescription therapeutics course, in order to accommodate learning characteristics of Millennials.Case Study: Instructors identified unique characteristics of Millennial generation students through literature review and focused educational workshops. These characteristics include the desire for active learning where didactic lectures make a connection to life, the incorporation of technology, and assignments that focus on team work. Course modifications were then made based on these characteristics including redesign of large group course lectures with incorporation of patient cases, inclusion of a variety of online components including the opportunity to provide course feedback, and active learning small group projects within workshop sections.Evaluation: Student evaluation of the course and instructors significantly improved after introducing changes to the course compared to previous years. Each component of the student evaluation resulted in a statistically significant change in mean score. Verbal and written evaluations indicated a very positive learning experience for students. Grade mean (3.3 vs. 3.8, p<0.001 and range (0.00-4.00 GPA in 2007 vs. 2.00-4.00 GPA in 2008, p <0.001 also improved compared to previous years.Conclusions: By identifying characteristics of Millennial generation student learners, traditional teaching methods can be modified in order to enhance retention of material and optimize their learning process. Course changes improved the learning experience for students and instructors. Instructors’ willingness to evaluate generational differences and adapt teaching enhances the learning experiences in the classroom for both students and instructors.

  20. Generating and Analysing Data for Applied Research on Emerging Technologies: A Grounded Action Learning Approach

    Directory of Open Access Journals (Sweden)

    Pak Yoong

    2004-01-01

    Full Text Available One of the difficulties of conducting applied qualitative research on the applications of emerging technologies is finding available sources of relevant data for analysis. Because the adoption of emerging technologies is, by definition, new in many organizations, there is often a lack of experienced practitioners who have relevant background and are willing to provide useful information for the study. Therefore, it is necessary to design research approaches that can generate accessible and relevant data. This paper describes two case studies in which the researchers used a grounded action learning approach to study the nature of e-facilitation for face-to-face and for distributed electronic meetings. The grounded action learning approach combines two research methodologies, grounded theory and action learning, to produce a rigorous and flexible method for studying e-facilitation. The implications of this grounded action learning approach for practice and research will be discussed.

  1. Multiple Chaotic Central Pattern Generators with Learning for Legged Locomotion and Malfunction Compensation

    DEFF Research Database (Denmark)

    Ren, Guanjiao; Chen, Weihai; Dasgupta, Sakyasingha

    2015-01-01

    on a simulated annealing algorithm. In a normal situation, the CPGs synchronize and their dynamics are identical. With leg malfunction or disability, the CPGs lose synchronization leading to independent dynamics. In this case, the learning mechanism is applied to automatically adjust the remaining legs...... chaotic CPG controller has difficulties dealing with leg malfunction. Specifically, in the scenarios presented here, its movement permanently deviates from the desired trajectory. To address this problem, we extend the single chaotic CPG to multiple CPGs with learning. The learning mechanism is based...... in a physical simulation of a quadruped as well as a hexapod robot and finally in a real six-legged walking machine called AMOSII. The experimental results presented here reveal that using multiple CPGs with learning is an effective approach for adaptive locomotion generation where, for instance, different body...

  2. Heterogeneous Suppression of Sequential Effects in Random Sequence Generation, but Not in Operant Learning

    Science.gov (United States)

    Shteingart, Hanan; Loewenstein, Yonatan

    2016-01-01

    There is a long history of experiments in which participants are instructed to generate a long sequence of binary random numbers. The scope of this line of research has shifted over the years from identifying the basic psychological principles and/or the heuristics that lead to deviations from randomness, to one of predicting future choices. In this paper, we used generalized linear regression and the framework of Reinforcement Learning in order to address both points. In particular, we used logistic regression analysis in order to characterize the temporal sequence of participants’ choices. Surprisingly, a population analysis indicated that the contribution of the most recent trial has only a weak effect on behavior, compared to more preceding trials, a result that seems irreconcilable with standard sequential effects that decay monotonously with the delay. However, when considering each participant separately, we found that the magnitudes of the sequential effect are a monotonous decreasing function of the delay, yet these individual sequential effects are largely averaged out in a population analysis because of heterogeneity. The substantial behavioral heterogeneity in this task is further demonstrated quantitatively by considering the predictive power of the model. We show that a heterogeneous model of sequential dependencies captures the structure available in random sequence generation. Finally, we show that the results of the logistic regression analysis can be interpreted in the framework of reinforcement learning, allowing us to compare the sequential effects in the random sequence generation task to those in an operant learning task. We show that in contrast to the random sequence generation task, sequential effects in operant learning are far more homogenous across the population. These results suggest that in the random sequence generation task, different participants adopt different cognitive strategies to suppress sequential dependencies when

  3. Learning-Related Changes in Adolescents' Neural Networks during Hypothesis-Generating and Hypothesis-Understanding Training

    Science.gov (United States)

    Lee, Jun-Ki; Kwon, Yongju

    2012-01-01

    Fourteen science high school students participated in this study, which investigated neural-network plasticity associated with hypothesis-generating and hypothesis-understanding in learning. The students were divided into two groups and participated in either hypothesis-generating or hypothesis-understanding type learning programs, which were…

  4. Learning-Related Changes in Adolescents' Neural Networks during Hypothesis-Generating and Hypothesis-Understanding Training

    Science.gov (United States)

    Lee, Jun-Ki; Kwon, Yongju

    2012-01-01

    Fourteen science high school students participated in this study, which investigated neural-network plasticity associated with hypothesis-generating and hypothesis-understanding in learning. The students were divided into two groups and participated in either hypothesis-generating or hypothesis-understanding type learning programs, which were…

  5. Model of e-learning with electronic educational resources of new generation

    Directory of Open Access Journals (Sweden)

    A. V. Loban

    2017-01-01

    Full Text Available Purpose of the article: improving of scientific and methodical base of the theory of the е-learning of variability. Methods used: conceptual and logical modeling of the е-learning of variability process with electronic educational resource of new generation and system analysis of the interconnection of the studied subject area, methods, didactics approaches and information and communication technologies means. Results: the formalization complex model of the е-learning of variability with electronic educational resource of new generation is developed, conditionally decomposed into three basic components: the formalization model of the course in the form of the thesaurusclassifier (“Author of e-resource”, the model of learning as management (“Coordination. Consultation. Control”, the learning model with the thesaurus-classifier (“Student”. Model “Author of e-resource” allows the student to achieve completeness, high degree of didactic elaboration and structuring of the studied material in triples of variants: modules of education information, practical task and control tasks; the result of the student’s (author’s of e-resource activity is the thesaurus-classifier. Model of learning as management is based on the principle of personal orientation of learning in computer environment and determines the logic of interaction between the lecturer and the student when determining the triple of variants individually for each student; organization of a dialogue between the lecturer and the student for consulting purposes; personal control of the student’s success (report generation and iterative search for the concept of the class assignment in the thesaurus-classifier before acquiring the required level of training. Model “Student” makes it possible to concretize the learning tasks in relation to the personality of the student and to the training level achieved; the assumption of the lecturer about the level of training of a

  6. Evolution of co-management: role of knowledge generation, bridging organizations and social learning.

    Science.gov (United States)

    Berkes, Fikret

    2009-04-01

    Over a period of some 20 years, different aspects of co-management (the sharing of power and responsibility between the government and local resource users) have come to the forefront. The paper focuses on a selection of these: knowledge generation, bridging organizations, social learning, and the emergence of adaptive co-management. Co-management can be considered a knowledge partnership. Different levels of organization, from local to international, have comparative advantages in the generation and mobilization of knowledge acquired at different scales. Bridging organizations provide a forum for the interaction of these different kinds of knowledge, and the coordination of other tasks that enable co-operation: accessing resources, bringing together different actors, building trust, resolving conflict, and networking. Social learning is one of these tasks, essential both for the co-operation of partners and an outcome of the co-operation of partners. It occurs most efficiently through joint problem solving and reflection within learning networks. Through successive rounds of learning and problem solving, learning networks can incorporate new knowledge to deal with problems at increasingly larger scales, with the result that maturing co-management arrangements become adaptive co-management in time.

  7. Advances in Bayesian Model Based Clustering Using Particle Learning

    Energy Technology Data Exchange (ETDEWEB)

    Merl, D M

    2009-11-19

    implementation of Carvalho et al that allow us to retain the computational advantages of particle learning while improving the suitability of the methodology to the analysis of streaming data and simultaneously facilitating the real time discovery of latent cluster structures. Section 4 demonstrates our methodological enhancements in the context of several simulated and classical data sets, showcasing the use of particle learning methods for online anomaly detection, label generation, drift detection, and semi-supervised classification, none of which would be achievable through a standard MCMC approach. Section 5 concludes with a discussion of future directions for research.

  8. Rotor Position Estimation for Switched Reluctance Wind Generator Using Extreme Learning Machine

    DEFF Research Database (Denmark)

    Wang, Chao; Liu, Xiao; Chen, Zhe

    2014-01-01

    Reluctance Wind Generator (SRWG) based on Extreme Learning Machine (ELM) which could build a nonlinear mapping between flux linkage-current and rotor position. The learning data are derived from magnetization curves of the SRWG which are obtained from Finite Element Analysis (FEA) of an SRG with 8/6 stator...... wind turbines are operating. Fast and accurate rotor position estimation is essential to promote the sensorless control as well as sensor fault tolerant operation of the SRG, which may improve the reliability of the system. This paper presents a rotor position sensorless estimation scheme for Switched...

  9. LEARNING STRATEGY IMPLEMENTATION OF GENERATIVE LEARNING ASSISTED SCIENTIST’S CARD TO IMPROVE SELF EFFICACY OF JUNIOR HIGH SCHOOL STUDENT IN CLASS VIII

    Directory of Open Access Journals (Sweden)

    R. Yuliarti

    2016-01-01

    Full Text Available In general, self-efficacy of the students is still low. This study aims to determine the learning strategies implementation of generative learning assisted scientist's card in improving self-efficacy and cognitive learning outcomes of the students. The study designed form One Group Pretest-Posttest Design. The improvement of self-efficacy can be determined from the change in the questionnaire score before and after the learning and observations during the learning process. Cognitive learning outcomes are known from pretest and posttest scores. To determine the improvement, the data were analyzed by using the gain test. The results showed that N-gain of self-efficacy is 0.13 (low and N-gain of cognitive learning is 0.60 (medium. Based on the observation, students’ self-efficacy has increased each meeting. Cognitive learning results also achieved mastery learning as big as 72.88%. It could be concluded that the learning strategy of generative learning assisted scientist's card can improve self efficacy and cognitive learning outcomes of the students.Pada umumnya, self efficacy yang dimiliki siswa masih rendah. Penelitian ini bertujuan untuk mengetahui penerapan strategi pembelajaran generative learning berbantuan scientist’s card dalam meningkatkan self efficacy dan  hasil belajar  kognitif siswa.  Desain penelitian berbentuk One Group Pretest-Posttest Design. Peningkatan self efficacy dapat diketahui dari perubahan  skor angket sebelum dan sesudah pembelajaran dan hasil observasi selama pembelajaran. Hasil  belajar kognitif diketahui dari skor pretest dan posttest. Untuk mengetahui peningkatannya, data yang diperoleh dianalisis menggunakan uji gain. Hasil penelitian menunjukkan bahwa peningkatan self efficacy berkatagori rendah dan peningkatan hasil belajar kognitif berkatagori sedang. Berdasarkan hasil observasi, self efficacy siswa setiap pertemuan meningkat. Hasil belajar ranah kognitif juga mencapai ketuntasan belajar .Jadi dapat

  10. Behaviour Generation in Humanoids by Learning Potential-Based Policies from Constrained Motion

    Directory of Open Access Journals (Sweden)

    Matthew Howard

    2008-01-01

    Full Text Available Movement generation that is consistent with observed or demonstrated behaviour is an efficient way to seed movement planning in complex, high-dimensional movement systems like humanoid robots. We present a method for learning potential-based policies from constrained motion data. In contrast to previous approaches to direct policy learning, our method can combine observations from a variety of contexts where different constraints are in force, to learn the underlying unconstrained policy in form of its potential function. This allows us to generalise and predict behaviour where novel constraints apply. We demonstrate our approach on systems of varying complexity, including kinematic data from the ASIMO humanoid robot with 22 degrees of freedom.

  11. Flexible Generation of E-Learning Exams in R: Moodle Quizzes, OLAT Assessments, and Beyond

    Directory of Open Access Journals (Sweden)

    Achim Zeileis

    2014-06-01

    Full Text Available The capabilities of the package exams for automatic generation of (statistical exams in R are extended by adding support for learning management systems: As in earlier versions of the package exam generation is still based on separate Sweave ?les for each exercise but rather than just producing di?erent types of PDF output ?les, the package can now render the same exercises into a wide variety of output formats. These include HTML (with various options for displaying mathematical content and XML speci?cations for online exams in learning management systems such as Moodle or OLAT. This ?exibility is accomplished by a new modular and extensible design of the package that allows for reading all weaved exercises into R and managing associated supplementary ?les (such as graphics or data ?les. The manuscript discusses the readily available user interfaces, the design of the underlying infrastructure, and how new functionality can be built on top of the existing tools.

  12. Incremental Learning of Context Free Grammars by Parsing-Based Rule Generation and Rule Set Search

    Science.gov (United States)

    Nakamura, Katsuhiko; Hoshina, Akemi

    This paper discusses recent improvements and extensions in Synapse system for inductive inference of context free grammars (CFGs) from sample strings. Synapse uses incremental learning, rule generation based on bottom-up parsing, and the search for rule sets. The form of production rules in the previous system is extended from Revised Chomsky Normal Form A→βγ to Extended Chomsky Normal Form, which also includes A→B, where each of β and γ is either a terminal or nonterminal symbol. From the result of bottom-up parsing, a rule generation mechanism synthesizes minimum production rules required for parsing positive samples. Instead of inductive CYK algorithm in the previous version of Synapse, the improved version uses a novel rule generation method, called ``bridging,'' which bridges the lacked part of the derivation tree for the positive string. The improved version also employs a novel search strategy, called serial search in addition to minimum rule set search. The synthesis of grammars by the serial search is faster than the minimum set search in most cases. On the other hand, the size of the generated CFGs is generally larger than that by the minimum set search, and the system can find no appropriate grammar for some CFL by the serial search. The paper shows experimental results of incremental learning of several fundamental CFGs and compares the methods of rule generation and search strategies.

  13. Fully Parallel Self-Learning Analog Support Vector Machine Employing Compact Gaussian Generation Circuits

    Science.gov (United States)

    Zhang, Renyuan; Shibata, Tadashi

    2012-04-01

    An analog support vector machine (SVM) processor employing a fully parallel self-learning circuitry was developed for the classification of highly dimensional patterns. To implement a highly dimensional Gaussian function, which is the most powerful kernel function in classification algorithms but computationally expensive, a compact analog Gaussian generation circuit was developed. By employing this proposed Gaussian generation circuit, a fully parallel self-learning processor based on an SVM algorithm was built for 64 dimension pattern classification. The chip real estate occupied by the processor is very small. The object images from two classes were converted into 64 dimension vectors using the algorithm developed in a previous work and fed into the processor. The learning process autonomously proceeded without any clock-based control and self-converged within a single clock cycle of the system (at 10 MHz). Some test object images were used to verify the learning performance. According to the circuit simulation results, it was shown that all the test images were classified into correct classes in real time. A proof-of-concept chip was designed in a 0.18 µm complementary metal-oxide-semiconductor (CMOS) technology, and the performance of the proposed SVM processor was confirmed from the measurement results of the fabricated chips.

  14. Hydrogen Fuel Cell Analysis: Lessons Learned from Stationary Power Generation Final Report

    Energy Technology Data Exchange (ETDEWEB)

    Scott E. Grasman; John W. Sheffield; Fatih Dogan; Sunggyu Lee; Umit O. Koylu; Angie Rolufs

    2010-04-30

    This study considered opportunities for hydrogen in stationary applications in order to make recommendations related to RD&D strategies that incorporate lessons learned and best practices from relevant national and international stationary power efforts, as well as cost and environmental modeling of pathways. The study analyzed the different strategies utilized in power generation systems and identified the different challenges and opportunities for producing and using hydrogen as an energy carrier. Specific objectives included both a synopsis/critical analysis of lessons learned from previous stationary power programs and recommendations for a strategy for hydrogen infrastructure deployment. This strategy incorporates all hydrogen pathways and a combination of distributed power generating stations, and provides an overview of stationary power markets, benefits of hydrogen-based stationary power systems, and competitive and technological challenges. The motivation for this project was to identify the lessons learned from prior stationary power programs, including the most significant obstacles, how these obstacles have been approached, outcomes of the programs, and how this information can be used by the Hydrogen, Fuel Cells & Infrastructure Technologies Program to meet program objectives primarily related to hydrogen pathway technologies (production, storage, and delivery) and implementation of fuel cell technologies for distributed stationary power. In addition, the lessons learned address environmental and safety concerns, including codes and standards, and education of key stakeholders.

  15. Costing Generated Runtime Execution Plans for Large-Scale Machine Learning Programs

    OpenAIRE

    Boehm, Matthias

    2015-01-01

    Declarative large-scale machine learning (ML) aims at the specification of ML algorithms in a high-level language and automatic generation of hybrid runtime execution plans ranging from single node, in-memory computations to distributed computations on MapReduce (MR) or similar frameworks like Spark. The compilation of large-scale ML programs exhibits many opportunities for automatic optimization. Advanced cost-based optimization techniques require---as a fundamental precondition---an accurat...

  16. Children's Everyday Learning by Assuming Responsibility for Others: Indigenous Practices as a Cultural Heritage Across Generations.

    Science.gov (United States)

    Fernández, David Lorente

    2015-01-01

    This chapter uses a comparative approach to examine the maintenance of Indigenous practices related with Learning by Observing and Pitching In in two generations--parent generation and current child generation--in a Central Mexican Nahua community. In spite of cultural changes and the increase of Western schooling experience, these practices persist, to different degrees, as a Nahua cultural heritage with close historical relations to the key value of cuidado (stewardship). The chapter explores how children learn the value of cuidado in a variety of everyday activities, which include assuming responsibility in many social situations, primarily in cultivating corn, raising and protecting domestic animals, health practices, and participating in family ceremonial life. The chapter focuses on three main points: (1) Cuidado (assuming responsibility for), in the Nahua socio-cultural context, refers to the concepts of protection and "raising" as well as fostering other beings, whether humans, plants, or animals, to reach their potential and fulfill their development. (2) Children learn cuidado by contributing to family endeavors: They develop attention and self-motivation; they are capable of responsible actions; and they are able to transform participation to achieve the status of a competent member of local society. (3) This collaborative participation allows children to continue the cultural tradition and to preserve a Nahua heritage at a deeper level in a community in which Nahuatl language and dress have disappeared, and people do not identify themselves as Indigenous.

  17. Evaluating mobile learning practice. Towards a framework for analysis of user-generated contexts with reference to the socio-cultural ecology of mobile learning

    Directory of Open Access Journals (Sweden)

    Judith Seipold

    2011-04-01

    Full Text Available Against the conceptual and theoretical background of a socio-culturally orientated approach to mobile learning (Pachler, Bachmair and Cook, 2010, this paper examines the evaluation of user-generated contexts by referring to an example from the use of mobile phones in schools. We discuss how mobile device-related, user- generated contexts around structures, agency and cultural practices might be brought into a fruitful relationship with institution-based learning. And, we provide categories for evaluating the use of mobile devices to generate meaning from and with fragmented and discontinuous media and modes at the interface of learning in formal, institutionalised and informal, self-directed settings. The evaluation criteria build on the framework of a socio-cultural ecology of mobile learning developed by the London Mobile Learning Group.

  18. Effects of Student-Generated Questions as the Source of Online Drill-and-Practice Activities on Learning

    Science.gov (United States)

    Yu, Fu-Yun; Chen, Yi-Jun

    2014-01-01

    This study investigated the effects of online drill-and-practice activities using student-generated questions on academic performance and motivation as compared with online drill-and-practice using teacher-generated questions and no drill-and-practice in a student question-generation (SQG) learning context. A quasi-experimental research method was…

  19. Active learning based segmentation of Crohns disease from abdominal MRI.

    Science.gov (United States)

    Mahapatra, Dwarikanath; Vos, Franciscus M; Buhmann, Joachim M

    2016-05-01

    This paper proposes a novel active learning (AL) framework, and combines it with semi supervised learning (SSL) for segmenting Crohns disease (CD) tissues from abdominal magnetic resonance (MR) images. Robust fully supervised learning (FSL) based classifiers require lots of labeled data of different disease severities. Obtaining such data is time consuming and requires considerable expertise. SSL methods use a few labeled samples, and leverage the information from many unlabeled samples to train an accurate classifier. AL queries labels of most informative samples and maximizes gain from the labeling effort. Our primary contribution is in designing a query strategy that combines novel context information with classification uncertainty and feature similarity. Combining SSL and AL gives a robust segmentation method that: (1) optimally uses few labeled samples and many unlabeled samples; and (2) requires lower training time. Experimental results show our method achieves higher segmentation accuracy than FSL methods with fewer samples and reduced training effort.

  20. Behavior generation strategy of artificial behavioral system by self-learning paradigm for autonomous robot tasks

    Science.gov (United States)

    Dağlarli, Evren; Temeltaş, Hakan

    2008-04-01

    In this study, behavior generation and self-learning paradigms are investigated for the real-time applications of multi-goal mobile robot tasks. The method is capable to generate new behaviors and it combines them in order to achieve multi goal tasks. The proposed method is composed from three layers: Behavior Generating Module, Coordination Level and Emotion -Motivation Level. Last two levels use Hidden Markov models to manage dynamical structure of behaviors. The kinematics and dynamic model of the mobile robot with non-holonomic constraints are considered in the behavior based control architecture. The proposed method is tested on a four-wheel driven and four-wheel steered mobile robot with constraints in simulation environment and results are obtained successfully.

  1. Internally generated sequences in learning and executing goal-directed behavior.

    Science.gov (United States)

    Pezzulo, Giovanni; van der Meer, Matthijs A A; Lansink, Carien S; Pennartz, Cyriel M A

    2014-12-01

    A network of brain structures including hippocampus (HC), prefrontal cortex, and striatum controls goal-directed behavior and decision making. However, the neural mechanisms underlying these functions are unknown. Here, we review the role of 'internally generated sequences': structured, multi-neuron firing patterns in the network that are not confined to signaling the current state or location of an agent, but are generated on the basis of internal brain dynamics. Neurophysiological studies suggest that such sequences fulfill functions in memory consolidation, augmentation of representations, internal simulation, and recombination of acquired information. Using computational modeling, we propose that internally generated sequences may be productively considered a component of goal-directed decision systems, implementing a sampling-based inference engine that optimizes goal acquisition at multiple timescales of on-line choice, action control, and learning.

  2. Generation and the subjective feeling of "aha!" are independently related to learning from insight.

    Science.gov (United States)

    Kizilirmak, Jasmin M; Galvao Gomes da Silva, Joana; Imamoglu, Fatma; Richardson-Klavehn, Alan

    2016-11-01

    It has been proposed that sudden insight into the solutions of problems can enhance long-term memory for those solutions. However, the nature of insight has been operationalized differently across studies. Here, we examined two main aspects of insight problem-solving-the generation of a solution and the subjective "aha!" experience-and experimentally evaluated their respective relationships to long-term memory formation (encoding). Our results suggest that generation (generated solution vs. presented solution) and the "aha!" experience ("aha!" vs. no "aha!") are independently related to learning from insight, as well as to the emotional response towards understanding the solution during encoding. Moreover, we analyzed the relationship between generation and the "aha!" experience and two different kinds of later memory tests, direct (intentional) and indirect (incidental). Here, we found that the generation effect was larger for indirect testing, reflecting more automatic retrieval processes, while the relationship with the occurrence of an "aha!" experience was somewhat larger for direct testing. Our results suggest that both the generation of a solution and the subjective experience of "aha!" indicate processes that benefit long-term memory formation, though differently. This beneficial effect is possibly due to the intrinsic reward associated with sudden comprehension and the detection of schema-consistency, i.e., that novel information can be easily integrated into existing knowledge.

  3. Adult-generated hippocampal neurons allow the flexible use of spatially precise learning strategies.

    Directory of Open Access Journals (Sweden)

    Alexander Garthe

    Full Text Available Despite enormous progress in the past few years the specific contribution of newly born granule cells to the function of the adult hippocampus is still not clear. We hypothesized that in order to solve this question particular attention has to be paid to the specific design, the analysis, and the interpretation of the learning test to be used. We thus designed a behavioral experiment along hypotheses derived from a computational model predicting that new neurons might be particularly relevant for learning conditions, in which novel aspects arise in familiar situations, thus putting high demands on the qualitative aspects of (re-learning.In the reference memory version of the water maze task suppression of adult neurogenesis with temozolomide (TMZ caused a highly specific learning deficit. Mice were tested in the hidden platform version of the Morris water maze (6 trials per day for 5 days with a reversal of the platform location on day 4. Testing was done at 4 weeks after the end of four cycles of treatment to minimize the number of potentially recruitable new neurons at the time of testing. The reduction of neurogenesis did not alter longterm potentiation in CA3 and the dentate gyrus but abolished the part of dentate gyrus LTP that is attributed to the new neurons. TMZ did not have any overt side effects at the time of testing, and both treated mice and controls learned to find the hidden platform. Qualitative analysis of search strategies, however, revealed that treated mice did not advance to spatially precise search strategies, in particular when learning a changed goal position (reversal. New neurons in the dentate gyrus thus seem to be necessary for adding flexibility to some hippocampus-dependent qualitative parameters of learning.Our finding that a lack of adult-generated granule cells specifically results in the animal's inability to precisely locate a hidden goal is also in accordance with a specialized role of the dentate gyrus in

  4. Effect of problem solving support and cognitive style on idea generation: Implications for Technology-Enhanced-Learning

    NARCIS (Netherlands)

    Stoyanov, Slavi; Kirschner, Paul A.

    2008-01-01

    Stoyanov, S., & Kirschner, P. (2007). Effect of problem solving support and cognitive style on idea generation: Implications for Technology-Enhanced-Learning. Journal of Research on Technology in Education, 40(1), 49-63.

  5. Elaborative encoding through self-generation enhances outcomes with errorless learning: Findings from the Skypekids memory study.

    Science.gov (United States)

    Haslam, Catherine; Wagner, Joseph; Wegener, Signy; Malouf, Tania

    2017-01-01

    Errorless learning has demonstrated efficacy in the treatment of memory impairment in adults and older adults with acquired brain injury. In the same population, use of elaborative encoding through supported self-generation in errorless paradigms has been shown to further enhance memory performance. However, the evidence base relevant to application of both standard and self-generation forms of errorless learning in children is far weaker. We address this limitation in the present study to examine recall performance in children with brain injury (n = 16) who were taught novel age-appropriate science and social science facts through the medium of Skype. All participants were taught these facts under conditions of standard errorless learning, errorless learning with self-generation, and trial-and-error learning after which memory was tested at 5-minute, 30-minute, 1-hour and 24-hour delays. Analysis revealed no main effect of time, with participants retaining most information acquired over the 24-hour testing period, but a significant effect of condition. Notably, self-generation proved more effective than both standard errorless and trial-and-error learning. Further analysis of the data revealed that severity of attentional impairment was less detrimental to recall performance under errorless conditions. This study extends the literature to provide further evidence of the value of errorless learning methods in children with ABI and the first demonstration of the effectiveness of self-generation when delivered via the Internet.

  6. The Generation of Textual Entailment with NLML in an Intelligent Dialogue system for Language Learning CSIEC

    CERN Document Server

    Jia, Jiyou

    2008-01-01

    This research report introduces the generation of textual entailment within the project CSIEC (Computer Simulation in Educational Communication), an interactive web-based human-computer dialogue system with natural language for English instruction. The generation of textual entailment (GTE) is critical to the further improvement of CSIEC project. Up to now we have found few literatures related with GTE. Simulating the process that a human being learns English as a foreign language we explore our naive approach to tackle the GTE problem and its algorithm within the framework of CSIEC, i.e. rule annotation in NLML, pattern recognition (matching), and entailment transformation. The time and space complexity of our algorithm is tested with some entailment examples. Further works include the rules annotation based on the English textbooks and a GUI interface for normal users to edit the entailment rules.

  7. Effects of spaced learning in the water maze on development of dentate granule cells generated in adult mice.

    Science.gov (United States)

    Trinchero, Mariela F; Koehl, Muriel; Bechakra, Malik; Delage, Pauline; Charrier, Vanessa; Grosjean, Noelle; Ladeveze, Elodie; Schinder, Alejandro F; Abrous, D Nora

    2015-11-01

    New dentate granule cells (GCs) are generated in the hippocampus throughout life. These adult-born neurons are required for spatial learning in the Morris water maze (MWM). In rats, spatial learning shapes the network by regulating their number and dendritic development. Here, we explored whether such modulatory effects exist in mice. New GCs were tagged using thymidine analogs or a GFP-expressing retrovirus. Animals were exposed to a reference memory protocol for 10-14 days (spaced training) at different times after newborn cells labeling. Cell proliferation, cell survival, cell death, neuronal phenotype, and dendritic and spine development were examined using immunohistochemistry. Surprisingly, spatial learning did not modify any of the parameters under scrutiny including cell number and dendritic morphology. These results suggest that although new GCs are required in mice for spatial learning in the MWM, they are, at least for the developmental intervals analyzed here, refractory to behavioral stimuli generated in the course of learning in the MWM.

  8. Lessons learned in the generation of biomedical research datasets using Semantic Open Data technologies.

    Science.gov (United States)

    Legaz-García, María del Carmen; Miñarro-Giménez, José Antonio; Menárguez-Tortosa, Marcos; Fernández-Breis, Jesualdo Tomás

    2015-01-01

    Biomedical research usually requires combining large volumes of data from multiple heterogeneous sources. Such heterogeneity makes difficult not only the generation of research-oriented dataset but also its exploitation. In recent years, the Open Data paradigm has proposed new ways for making data available in ways that sharing and integration are facilitated. Open Data approaches may pursue the generation of content readable only by humans and by both humans and machines, which are the ones of interest in our work. The Semantic Web provides a natural technological space for data integration and exploitation and offers a range of technologies for generating not only Open Datasets but also Linked Datasets, that is, open datasets linked to other open datasets. According to the Berners-Lee's classification, each open dataset can be given a rating between one and five stars attending to can be given to each dataset. In the last years, we have developed and applied our SWIT tool, which automates the generation of semantic datasets from heterogeneous data sources. SWIT produces four stars datasets, given that fifth one can be obtained by being the dataset linked from external ones. In this paper, we describe how we have applied the tool in two projects related to health care records and orthology data, as well as the major lessons learned from such efforts.

  9. PBL triggers in relation to students' generated learning issues and predetermined faculty objectives: Study in a Malaysian public university.

    Science.gov (United States)

    Ruslai, Nurul Hidayati; Salam, Abdus

    2016-01-01

    Foundational elements of problem based learning (PBL) are triggers, tutors and students. Ineffective triggers are important issues for students' inability to generate appropriate learning issues. The objective of this study was to evaluate PBL triggers and to determine similarities of students' generated learning issues with predetermined faculty objectives. It was a retrospective study conducted in 2014 analyzing all 24 PBL-triggers used at Centre for Foundation Studies, International Islamic University Malaysia, in four semesters during two consecutive years 2011 and 2012. Triggers were used as textual and illustration format equally in each semester. Total 16 PBL-triggers with highest and lowest achieving similarities of learning issues with predetermined faculty objectives were selected equally from each semester and format. The trigger quality and learning issues related to predetermine faculty objectives were analyzed and presented as mean and percent distribution. Mean similarities score of students' generated learning issues were 3.4 over 5 predetermined faculty objectives which was 68%, varied from 58% to 79%. More than 70% similarities were generated from five textual and four illustrated triggers, while PBL, it is the designing considering influential variables that influence higher outcomes. Triggers should have planned clues that lead students to generate issues correlate with faculty objectives. Educational institution should emphasize on training needs of faculty at regular interval to develop and re-in force teachers' skills in trigger design, thereby to promote a sustainable educational and organizational development.

  10. Is a PBL curriculum a better nutrient medium for student-generated learning issues than a PBL island?

    Science.gov (United States)

    Gehlhar, K; Wüller, A; Lieverscheidt, H; Fischer, M R; Schäfer, T

    2010-12-01

    Problem based learning (PBL) is often introduced in curricula in form of short segments. In the literature the value of these PBL-islands is doubted. In order to gain more insight in this curricular approach, we compared student generated learning issues, from a 7-week PBL-island introduced in a traditional curriculum (PBL-I), with the gold standard of a PBL-based model-curriculum (PBL-B) existing in parallel at the same University (Ruhr-University Bochum, Germany). Both tracks use five identical PBL-cases. Thousand seven hundred and three student-generated learning issues of 252 tutorial groups (193 PBL-I and 59 PBL-B groups with six to seven students per group) were analysed in seven different categories. Results showed that overall there were no substantial differences between both curricula. PBL-B students generated more problem-related and less basic science clinical learning issues than PBL-I students, but in both groups learning issues were related to the same number of different subjects. Furthermore, students in the PBL-curriculum tend to generate little less but slightly better phrased issues. Taken together, we found no substantial evidence with respect to student-generated learning issues that could prove that students cannot work with the PBL-method, even if it is introduced later in the curriculum and last only for a short period of time.

  11. Network traffic classification based on ensemble learning and co-training

    Institute of Scientific and Technical Information of China (English)

    HE HaiTao; LUO XiaoNan; MA FeiTeng; CHE ChunHui; WANG JianMin

    2009-01-01

    Classification of network traffic Is the essential step for many network researches. However, with the rapid evolution of Internet applications the effectiveness of the port-based or payload-based identifi-cation approaches has been greatly diminished In recent years. And many researchers begin to turn their attentions to an alternative machine learning based method. This paper presents a novel machine learning-based classification model, which combines ensemble learning paradigm with co-training tech-niques. Compared to previous approaches, most of which only employed single classifier, multiple clas-sifiers and semi-supervised learning are applied in our method and it mainly helps to overcome three shortcomings: limited flow accuracy rate, weak adaptability and huge demand of labeled training set. In this paper, statistical characteristics of IP flows are extracted from the packet level traces to establish the feature set, then the classification model is created and tested and the empirical results prove its feasibility and effectiveness.

  12. Generation and Validation of Spatial Distribution of Hourly Wind Speed Time-Series using Machine Learning

    Science.gov (United States)

    Veronesi, F.; Grassi, S.

    2016-09-01

    Wind resource assessment is a key aspect of wind farm planning since it allows to estimate the long term electricity production. Moreover, wind speed time-series at high resolution are helpful to estimate the temporal changes of the electricity generation and indispensable to design stand-alone systems, which are affected by the mismatch of supply and demand. In this work, we present a new generalized statistical methodology to generate the spatial distribution of wind speed time-series, using Switzerland as a case study. This research is based upon a machine learning model and demonstrates that statistical wind resource assessment can successfully be used for estimating wind speed time-series. In fact, this method is able to obtain reliable wind speed estimates and propagate all the sources of uncertainty (from the measurements to the mapping process) in an efficient way, i.e. minimizing computational time and load. This allows not only an accurate estimation, but the creation of precise confidence intervals to map the stochasticity of the wind resource for a particular site. The validation shows that machine learning can minimize the bias of the wind speed hourly estimates. Moreover, for each mapped location this method delivers not only the mean wind speed, but also its confidence interval, which are crucial data for planners.

  13. Supercomputer Assisted Generation of Machine Learning Agents for the Calibration of Building Energy Models

    Energy Technology Data Exchange (ETDEWEB)

    Sanyal, Jibonananda [ORNL; New, Joshua Ryan [ORNL; Edwards, Richard [ORNL

    2013-01-01

    Building Energy Modeling (BEM) is an approach to model the energy usage in buildings for design and retrot pur- poses. EnergyPlus is the agship Department of Energy software that performs BEM for dierent types of buildings. The input to EnergyPlus can often extend in the order of a few thousand parameters which have to be calibrated manu- ally by an expert for realistic energy modeling. This makes it challenging and expensive thereby making building en- ergy modeling unfeasible for smaller projects. In this paper, we describe the \\Autotune" research which employs machine learning algorithms to generate agents for the dierent kinds of standard reference buildings in the U.S. building stock. The parametric space and the variety of building locations and types make this a challenging computational problem necessitating the use of supercomputers. Millions of En- ergyPlus simulations are run on supercomputers which are subsequently used to train machine learning algorithms to generate agents. These agents, once created, can then run in a fraction of the time thereby allowing cost-eective cali- bration of building models.

  14. Combining generative and discriminative models for semantic segmentation of CT scans via active learning.

    Science.gov (United States)

    Iglesias, Juan Eugenio; Konukoglu, Ender; Montillo, Albert; Tu, Zhuowen; Criminisi, Antonio

    2011-01-01

    This paper presents a new supervised learning framework for the efficient recognition and segmentation of anatomical structures in 3D computed tomography (CT), with as little training data as possible. Training supervised classifiers to recognize organs within CT scans requires a large number of manually delineated exemplar 3D images, which are very expensive to obtain. In this study, we borrow ideas from the field of active learning to optimally select a minimum subset of such images that yields accurate anatomy segmentation. The main contribution of this work is in designing a combined generative-discriminative model which: i) drives optimal selection of training data; and ii) increases segmentation accuracy. The optimal training set is constructed by finding unlabeled scans which maximize the disagreement between our two complementary probabilistic models, as measured by a modified version of the Jensen-Shannon divergence. Our algorithm is assessed on a database of 196 labeled clinical CT scans with high variability in resolution, anatomy, pathologies, etc. Quantitative evaluation shows that, compared with randomly selecting the scans to annotate, our method decreases the number of training images by up to 45%. Moreover, our generative model of body shape substantially increases segmentation accuracy when compared to either using the discriminative model alone or a generic smoothness prior (e.g. via a Markov Random Field).

  15. Learning-Related Changes in Adolescents' Neural Networks During Hypothesis-Generating and Hypothesis-Understanding Training

    Science.gov (United States)

    Lee, Jun-Ki; Kwon, Yongju

    2010-11-01

    Fourteen science high school students participated in this study, which investigated neural-network plasticity associated with hypothesis-generating and hypothesis-understanding in learning. The students were divided into two groups and participated in either hypothesis-generating or hypothesis-understanding type learning programs, which were composed of 12 topics taught over a 12-week period. To measure change in student competence and brain networks, a paper & pencil test and an fMRI scanning session were administered before and after the training programs. Unlike the hypothesis-understanding group, a before and after training comparison for the hypothesis-generating group showed significantly strong changes in hypothesis explanation quotients and functional brain connectivity associated with hypothesis-generating. However, for the hypothesis-understanding group, the brain network related to hypothesis-understanding significantly strengthened, not from hypothesis-generating type learning, but from hypothesis-understanding type learning. These findings suggest that for hypothesis-generating and hypothesis-understanding there are at least two specialized brain network systems or processes at work in the brain. Furthermore, hypothesis-generating competence could be developed by appropriate training programs such as teaching by way of active hypothesis generation rather than present passive expository teaching practices.

  16. Co-constructing cultural landscapes for disciplinary learning in and out of school: the next generation science standards and learning progressions in action

    Science.gov (United States)

    Córdova, Ralph A.; Balcerzak, Phyllis

    2016-12-01

    The authors of this study are teacher-researchers, the first is a university researcher and former third and fourth grade teacher, while the second author is a university-based science educator. They report findings from a community-based study that Ralph, the first author, and his students conducted across two academic years (2001-2003) in order to illustrate the ways in which the next generation science standards and learning progressions can be appropriated as social-constructed practices inside and outside of school. The authors argue that what constitutes science learning in school is not a `state of grace' dictated by standards. Rather, becoming a scientist within a community of learners is a cultural phenomenon that teachers and students co-construct and as such teachers can approach the next generation science standards and learning progressions as opportunities to create intentional, disciplinary practice-based learning communities inside and outside of school.

  17. Analysis and classification of collective behavior using generative modeling and nonlinear manifold learning.

    Science.gov (United States)

    Butail, Sachit; Bollt, Erik M; Porfiri, Maurizio

    2013-11-07

    In this paper, we build a framework for the analysis and classification of collective behavior using methods from generative modeling and nonlinear manifold learning. We represent an animal group with a set of finite-sized particles and vary known features of the group structure and motion via a class of generative models to position each particle on a two-dimensional plane. Particle positions are then mapped onto training images that are processed to emphasize the features of interest and match attainable far-field videos of real animal groups. The training images serve as templates of recognizable patterns of collective behavior and are compactly represented in a low-dimensional space called embedding manifold. Two mappings from the manifold are derived: the manifold-to-image mapping serves to reconstruct new and unseen images of the group and the manifold-to-feature mapping allows frame-by-frame classification of raw video. We validate the combined framework on datasets of growing level of complexity. Specifically, we classify artificial images from the generative model, interacting self-propelled particle model, and raw overhead videos of schooling fish obtained from the literature. © 2013 Elsevier Ltd. All rights reserved.

  18. DISTRIBUTED LEADERSHIP COLLABORATION FACTORS TO SUPPORT IDEA GENERATION IN COMPUTER-SUPPORTED COLLABORATIVE e-LEARNING

    Directory of Open Access Journals (Sweden)

    Niki Lambropoulos

    2011-01-01

    Full Text Available This paper aims to identify, discuss and analyze students’ collaboration factors related to distributed leadership (DL, which correlates with interaction quality evident in idea generation. Scripting computer-supported collaborative e-learning (CSCeL activities based on DL can scaffold students’ interactions that support collaboration and promote idea generation. Furthermore, the associated tools can facilitate collaboration via scripting and shed light on students’ interactions and dialogical sequences. Such detailed planning can result in effective short e-courses. In this case study, 21 MSc students’ teams worked on a DL project within a 2-day e-course at the IT Institute (ITIN, France. The research methods involved a self-reported questionnaire; the Non-Negative Matrix Factorization (NNMF algorithm with qualitative analysis; and outcomes from the Social Network Analysis (SNA tools implemented within the forums. The results indicated that scripting DL based on the identified distributed leadership attributes can support values such as collaboration and can be useful in supporting idea generation in short e-courses.

  19. Generations.

    Science.gov (United States)

    Chambers, David W

    2005-01-01

    Groups naturally promote their strengths and prefer values and rules that give them an identity and an advantage. This shows up as generational tensions across cohorts who share common experiences, including common elders. Dramatic cultural events in America since 1925 can help create an understanding of the differing value structures of the Silents, the Boomers, Gen Xers, and the Millennials. Differences in how these generations see motivation and values, fundamental reality, relations with others, and work are presented, as are some applications of these differences to the dental profession.

  20. Test Framing Generates a Stability Bias for Predictions of Learning by Causing People to Discount their Learning Beliefs.

    Science.gov (United States)

    Ariel, Robert; Hines, Jarrod C; Hertzog, Christopher

    2014-08-01

    People estimate minimal changes in learning when making predictions of learning (POLs) for future study opportunities despite later showing increased performance and an awareness of that increase (Kornell & Bjork, 2009). This phenomenon is conceptualized as a stability bias in judgments about learning. We investigated the malleability of this effect, and whether it reflected people's underlying beliefs about learning. We manipulated prediction framing to emphasize the role of testing vs. studying on memory and directly measured beliefs about multi-trial study effects on learning by having participants construct predicted learning curves before and after the experiment. Mean POLs were more sensitive to the number of study-test opportunities when performance was framed in terms of study benefits rather than testing benefits and POLs reflected pre-existing beliefs about learning. The stability bias is partially due to framing and reflects discounted beliefs about learning benefits rather than inherent belief in the stability of performance.

  1. Placement of distributed generation using Teaching-Learning-Based Optimization (TLBO in south of Kerman

    Directory of Open Access Journals (Sweden)

    M. Maghfoori

    2014-10-01

    Full Text Available In this paper a new approach using Teaching-Learning-Based Optimization (TLBO is presented for the placement of Distributed Generators (DGs in radial distribution systems in south of Kerman. In this approach a multiple objective planning framework is used to evaluate the impact of DG placement and sizing for an optimal development of the distribution system. In this study, the optimum sizes and locations of DG units are found by considering the power losses and voltage profile as variables into the objective function. The optimization process is done using the link between the Digsilent and Matlab. The results obtained show the improvement of the system in the presence of DGs.

  2. Multiple Chaotic Central Pattern Generators with Learning for Legged Locomotion and Malfunction Compensation

    DEFF Research Database (Denmark)

    Ren, Guanjiao; Chen, Weihai; Dasgupta, Sakyasingha

    2015-01-01

    An originally chaotic system can be controlled into various periodic dynamics. When it is implemented into a legged robot’s locomotion control as a central pattern generator (CPG), sophisticated gait patterns arise so that the robot can perform various walking behaviors. However, such a single...... on a simulated annealing algorithm. In a normal situation, the CPGs synchronize and their dynamics are identical. With leg malfunction or disability, the CPGs lose synchronization leading to independent dynamics. In this case, the learning mechanism is applied to automatically adjust the remaining legs......’ oscillation frequencies so that the robot adapts its locomotion to deal with the malfunction. As a consequence, the trajectory produced by the multiple chaotic CPGs resembles the original trajectory far better than the one produced by only a single CPG. The performance of the system is evaluated first...

  3. The obser-view: a method of generating data and learning

    DEFF Research Database (Denmark)

    Kragelund, Linda

    2013-01-01

    Aim: This paper introduces the obser-view method to generate data and be a learning space for researcher and research participant in qualitative research, and shows how obser-view can be used in research. Background: The obser-view was developed in a qualitative research project about student...... participated in the project. Nine participated in obser-views because it was incorporated into the research design halfway through the project. Review methods: A review of literature resulted in two sources in which "obserview" was used to gain explanations of research participants' acts whereas I sought...... a greater understanding of research participants' acts. Discussion: Whether to include a dialogue between me and the student nurses in the research design in continuation of my observation of them was a dilemma. I realised that an argument for doing it was that the method would support my efforts to make my...

  4. Embodied learning of a generative neural model for biological motion perception and inference.

    Science.gov (United States)

    Schrodt, Fabian; Layher, Georg; Neumann, Heiko; Butz, Martin V

    2015-01-01

    Although an action observation network and mirror neurons for understanding the actions and intentions of others have been under deep, interdisciplinary consideration over recent years, it remains largely unknown how the brain manages to map visually perceived biological motion of others onto its own motor system. This paper shows how such a mapping may be established, even if the biologically motion is visually perceived from a new vantage point. We introduce a learning artificial neural network model and evaluate it on full body motion tracking recordings. The model implements an embodied, predictive inference approach. It first learns to correlate and segment multimodal sensory streams of own bodily motion. In doing so, it becomes able to anticipate motion progression, to complete missing modal information, and to self-generate learned motion sequences. When biological motion of another person is observed, this self-knowledge is utilized to recognize similar motion patterns and predict their progress. Due to the relative encodings, the model shows strong robustness in recognition despite observing rather large varieties of body morphology and posture dynamics. By additionally equipping the model with the capability to rotate its visual frame of reference, it is able to deduce the visual perspective onto the observed person, establishing full consistency to the embodied self-motion encodings by means of active inference. In further support of its neuro-cognitive plausibility, we also model typical bistable perceptions when crucial depth information is missing. In sum, the introduced neural model proposes a solution to the problem of how the human brain may establish correspondence between observed bodily motion and its own motor system, thus offering a mechanism that supports the development of mirror neurons.

  5. Embodied Learning of a Generative Neural Model for Biological Motion Perception and Inference

    Directory of Open Access Journals (Sweden)

    Fabian eSchrodt

    2015-07-01

    Full Text Available Although an action observation network and mirror neurons for understanding the actions and intentions of others have been under deep, interdisciplinary consideration over recent years, it remains largely unknown how the brain manages to map visually perceived biological motion of others onto its own motor system. This paper shows how such a mapping may be established, even if the biologically motion is visually perceived from a new vantage point. We introduce a learning artificial neural network model and evaluate it on full body motion tracking recordings. The model implements an embodied, predictive inference approach. It first learns to correlate and segment multimodal sensory streams of own bodily motion. In doing so, it becomes able to anticipate motion progression, to complete missing modal information, and to self-generate learned motion sequences. When biological motion of another person is observed, this self-knowledge is utilized to recognize similar motion patterns and predict their progress. Due to the relative encodings, the model shows strong robustness in recognition despite observing rather large varieties of body morphology and posture dynamics. By additionally equipping the model with the capability to rotate its visual frame of reference, it is able to deduce the visual perspective onto the observed person, establishing full consistency to the embodied self-motion encodings by means of active inference. In further support of its neuro-cognitive plausibility, we also model typical bistable perceptions when crucial depth information is missing. In sum, the introduced neural model proposes a solution to the problem of how the human brain may establish correspondence between observed bodily motion and its own motor system, thus offering a mechanism that supports the development of mirror neurons.

  6. Learning a generative syntax from transparent syntactic atoms in the linguistic input.

    Science.gov (United States)

    Ninio, Anat

    2014-11-01

    We examined parents' two-word utterances expressing core syntactic relations in order to test the hypothesis that they may enable children to derive the atoms of hierarchical syntax, namely, the asymmetrical Merge/Dependency relation between pairs of words, and, in addition, to identify variables serving generative syntactic rules. Using a large English-language parental corpus, we located all two-word utterances containing a verb and its subject, object, or indirect object. Analysis showed that parental two-word sentences contain transparent information on the binary dependency/merge relation responsible for syntactic connectivity. The syntactic atoms modelled in the two-word input contain natural variables for dependents, making generalization to other contexts an immediate possibility. In a second study, a large sample of children were found to use the same verbs in the great majority of their early sentences expressing the same core grammatical relations. The results support a learning model according to which children learn the basics of syntax from parental two-word sentences.

  7. 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...

  8. Generation of Tag Clouds for E-learning Documents-a Value-added Service to Peer Learners

    Directory of Open Access Journals (Sweden)

    M. Ravichandran

    2015-05-01

    Full Text Available In an E-learning environment users (learners/facilitators have access to a large collection of learning documents stored in various online databases. Retrieving the most relevant learning documents available on database-driven websites is often difficult, as great amounts of textual content is involved. A peer learner may require a general sketch of the digital document content available in the database in order to find out whether the document information is useful for his/her search requirements. In this research study, we present a method for generating tag clouds for E-learning documents as a value added service to peer learners. We propose a system that generates the cluster summary of e-learning document and provides visual representation of these documents stored in a database. Visualization of the content of the database can be helpful during the peer learners search process and to reach their study goals, thus serving as a value-added service. The uniqueness of this method is that it reveals the fundamental structure that provides the text document with certain semantics and is capable of retrieving the most appropriate information. The relationship between the tags is obtained using the Multi Objective Hierarchical Cluster (MOHC technique. In this study, we tested our proposed method using different datasets and present the tag clouds obtained by the computations for each dataset. The experimental results of tag cloud generation for e-learning documents demonstrate the accuracy and effectiveness of our proposed approach.

  9. Use of semantic technologies for the development of a dynamic trajectories generator in a Semantic Chemistry eLearning platform

    CERN Document Server

    Huber, Richard; Todor, Alexandru; Krebs, Sebastian; Heese, Ralf; Paschke, Adrian

    2010-01-01

    ChemgaPedia is a multimedia, webbased eLearning service platform that currently contains about 18.000 pages organized in 1.700 chapters covering the complete bachelor studies in chemistry and related topics of chemistry, pharmacy, and life sciences. The eLearning encyclopedia contains some 25.000 media objects and the eLearning platform provides services such as virtual and remote labs for experiments. With up to 350.000 users per month the platform is the most frequently used scientific educational service in the German spoken Internet. In this demo we show the benefit of mapping the static eLearning contents of ChemgaPedia to a Linked Data representation for Semantic Chemistry which allows for generating dynamic eLearning paths tailored to the semantic profiles of the users.

  10. Formaldehyde impairs learning and memory involving the disturbance of hydrogen sulfide generation in the hippocampus of rats.

    Science.gov (United States)

    Tang, Xiao-Qing; Zhuang, Yuan-Yuan; Zhang, Ping; Fang, Heng-Rong; Zhou, Cheng-Fang; Gu, Hong-Feng; Zhang, Hui; Wang, Chun-Yan

    2013-01-01

    Formaldehyde (FA), a well-known indoor and outdoor pollutant, has been implicated as the responsible agent in the development of neurocognitive disorders. Hydrogen sulfide (H(2)S), the third gasotransimitter, is an endogenous neuromodulator, which facilitates the induction of hippocampal long-term potentiation, involving the functions of learning and memory. In the present study, we analyzed the effects of intracerebroventricular injection of FA on the formation of learning and memory and the generation of endogenous H(2)S in the hippocampus of rats. We found that the intracerebroventricular injection of FA in rats impairs the function of learning and memory in the Morris water maze and novel object recognition test and increases the formation of apoptosis and lipid peroxidation in the hippocampus. We also showed that FA exposure inhibits the expression of cystathionine β-synthase, the major enzyme responsible for endogenous H(2)S generation in hippocampus and decreases the production of endogenous H(2)S in hippocampus in rats. These results suggested that FA-disturbed generation of endogenous H(2)S in hippocampus leads to the oxidative stress-mediated neuron damage, ultimately impairing the function of learning and memory. Our findings imply that the disturbance of endogenous H(2)S generation in hippocampus is a potential contributing mechanism underling FA-caused learning and memory impairment.

  11. Age related-changes in the neural basis of self-generation in verbal paired associate learning.

    Science.gov (United States)

    Vannest, Jennifer; Maloney, Thomas; Kay, Benjamin; Siegel, Miriam; Allendorfer, Jane B; Banks, Christi; Altaye, Mekibib; Szaflarski, Jerzy P

    2015-01-01

    Verbal information is better retained when it is self-generated rather than when it is received passively. The application of self-generation procedures has been found to improve memory in healthy elderly and in individuals with impaired cognition. Overall, the available studies support the notion that active participation in verbal encoding engages memory mechanisms that supplement those used during passive observation. Thus, the objective of this study was to investigate the age-related changes in the neural mechanisms involved in the encoding of paired-associates using a self-generation method that has been shown to improve memory performance across the lifespan. Subjects were 113 healthy right-handed adults (Edinburgh Handedness Inventory >50; 67 females) ages 18-76, native speakers of English with no history of neurological or psychiatric disorders. Subjects underwent fMRI at 3 T while performing didactic learning ("read") or self-generation learning ("generate") of 30 word pairs per condition. After fMRI, recognition memory for the second word in each pair was evaluated outside of the scanner. On the post-fMRI testing more "generate" words were correctly recognized than "read" words (p adults recognizing the "generated" words less accurately (p age, but the benefit from self-generation remained consistently significant across ages. Independent component analysis of the neuroimaging data revealed an extensive set of components engaged in self-generation learning compared with didactic learning, and identified areas that were associated with age-related changes independent of performance.

  12. Automated learning of generative models for subcellular location: building blocks for systems biology.

    Science.gov (United States)

    Zhao, Ting; Murphy, Robert F

    2007-12-01

    The goal of location proteomics is the systematic and comprehensive study of protein subcellular location. We have previously developed automated, quantitative methods to identify protein subcellular location families, but there have been no effective means of communicating their patterns to integrate them with other information for building cell models. We built generative models of subcellular location that are learned from a collection of images so that they not only represent the pattern, but also capture its variation from cell to cell. Our models contain three components: a nuclear model, a cell shape model and a protein-containing object model. We built models for six patterns that consist primarily of discrete structures. To validate the generated images, we showed that they are recognized with reasonable accuracy by a classifier trained on real images. We also showed that the model parameters themselves can be used as features to discriminate the classes. The models allow the synthesis of images with the expectation that they are drawn from the same underlying statistical distribution as the images used to train them. They can potentially be combined for many proteins to yield a high resolution location map in support of systems biology.

  13. Factor Analysis of the ESL/EFL Strategy Inventory for Language Learning: Generation 1.5 Korean Immigrant College Students' Language Learning Strategies

    Science.gov (United States)

    Heo, Misook; Stoffa, Rosa; Kush, Joseph C.

    2012-01-01

    This study explores factors related to the language learning strategies of second language learners, specifically Generation 1.5 Korean immigrant students--the seventh-largest and one of the fastest growing foreign-born groups in the USA. Participants in this study were members of the Korean communities located in Pittsburgh and Philadelphia who…

  14. At the Core of the Apple Store: Images of Next Generation Learning

    Science.gov (United States)

    Washor, Elliot; Mojkowski, Charles; Newsom, Loran

    2009-01-01

    The physical, psychological, cultural, social, and organizational elements of a learning environment are as important as the learning opportunities themselves. The Apple Store blends retail and school into a new type of learning environment that lets the customer learn anything, at any time, at any level, from experts, expert practitioners, and…

  15. Supporting the Implementation of Externally Generated Learning Outcomes and Learning-Centered Curriculum Development: An Integrated Framework

    Science.gov (United States)

    Hubball, Harry; Gold, Neil; Mighty, Joy; Britnell, Judy

    2007-01-01

    This article provides an overview of one Canadian provincially initiated curriculum reform effort in which several generic learning outcomes were established. It also presents a flexible, practical, and integrated framework for the development, implementation, and evaluation of program-level learning outcomes in undergraduate curricula contexts.…

  16. Generational, Cultural, and Linguistic Integration for Literacy Learning and Teaching in Uganda: Pedagogical Possibilities, Challenges, and Lessons from One NGO

    Science.gov (United States)

    Ngaka, Willy; Graham, Ross; Masaazi, Fred Masagazi; Anyandru, Elly Moses

    2016-01-01

    This qualitative case study focuses on a volunteer-led local NGO in Uganda to examine how integrating generations, cultures, and languages is enhancing literacy learning to help ethnically and linguistically diverse rural communities survive in the prevailing globally competitive neoliberal environment. Immersing the study in the social practices…

  17. Acquisition of nonlinear forward optics in generative models: two-stage "downside-up" learning for occluded vision.

    Science.gov (United States)

    Tajima, Satohiro; Watanabe, Masataka

    2011-03-01

    We propose a two-stage learning method which implements occluded visual scene analysis into a generative model, a type of hierarchical neural network with bi-directional synaptic connections. Here, top-down connections simulate forward optics to generate predictions for sensory driven low-level representation, whereas bottom-up connections function to send the prediction error, the difference between the sensory based and the predicted low-level representation, to higher areas. The prediction error is then used to update the high-level representation to obtain better agreement with the visual scene. Although the actual forward optics is highly nonlinear and the accuracy of simulated forward optics is crucial for these types of models, the majority of previous studies have only investigated linear and simplified cases of forward optics. Here we take occluded vision as an example of nonlinear forward optics, where an object in front completely masks out the object behind. We propose a two-staged learning method inspired by the staged development of infant visual capacity. In the primary learning stage, a minimal set of object basis is acquired within a linear generative model using the conventional unsupervised learning scheme. In the secondary learning stage, an auxiliary multi-layer neural network is trained to acquire nonlinear forward optics by supervised learning. The important point is that the high-level representation of the linear generative model serves as the input and the sensory driven low-level representation provides the desired output. Numerical simulations show that occluded visual scene analysis can indeed be implemented by the proposed method. Furthermore, considering the format of input to the multi-layer network and analysis of hidden-layer units leads to the prediction that whole object representation of partially occluded objects, together with complex intermediate representation as a consequence of nonlinear transformation from non-occluded to

  18. Biological oscillations for learning walking coordination: dynamic recurrent neural network functionally models physiological central pattern generator.

    Science.gov (United States)

    Hoellinger, Thomas; Petieau, Mathieu; Duvinage, Matthieu; Castermans, Thierry; Seetharaman, Karthik; Cebolla, Ana-Maria; Bengoetxea, Ana; Ivanenko, Yuri; Dan, Bernard; Cheron, Guy

    2013-01-01

    The existence of dedicated neuronal modules such as those organized in the cerebral cortex, thalamus, basal ganglia, cerebellum, or spinal cord raises the question of how these functional modules are coordinated for appropriate motor behavior. Study of human locomotion offers an interesting field for addressing this central question. The coordination of the elevation of the 3 leg segments under a planar covariation rule (Borghese et al., 1996) was recently modeled (Barliya et al., 2009) by phase-adjusted simple oscillators shedding new light on the understanding of the central pattern generator (CPG) processing relevant oscillation signals. We describe the use of a dynamic recurrent neural network (DRNN) mimicking the natural oscillatory behavior of human locomotion for reproducing the planar covariation rule in both legs at different walking speeds. Neural network learning was based on sinusoid signals integrating frequency and amplitude features of the first three harmonics of the sagittal elevation angles of the thigh, shank, and foot of each lower limb. We verified the biological plausibility of the neural networks. Best results were obtained with oscillations extracted from the first three harmonics in comparison to oscillations outside the harmonic frequency peaks. Physiological replication steadily increased with the number of neuronal units from 1 to 80, where similarity index reached 0.99. Analysis of synaptic weighting showed that the proportion of inhibitory connections consistently increased with the number of neuronal units in the DRNN. This emerging property in the artificial neural networks resonates with recent advances in neurophysiology of inhibitory neurons that are involved in central nervous system oscillatory activities. The main message of this study is that this type of DRNN may offer a useful model of physiological central pattern generator for gaining insights in basic research and developing clinical applications.

  19. Biological oscillations for learning walking coordination: dynamic recurrent neural network functionally models physiological central pattern generator

    Directory of Open Access Journals (Sweden)

    Thomas eHoellinger

    2013-05-01

    Full Text Available The existence of dedicated neuronal modules such as those organized in the cerebral cortex, thalamus, basal ganglia, cerebellum or spinal cord raises the question of how these functional modules are coordinated for appropriate motor behavior. Study of human locomotion offers an interesting field for addressing this central question. The coordination of the elevation of the 3 leg segments under a planar covariation rule (Borghese et al., 1996 was recently modeled (Barliya et al., 2009 by phase-adjusted simple oscillators shedding new light on the understanding of the central pattern generator processing relevant oscillation signals. We describe the use of a dynamic recurrent neural network (DRNN mimicking the natural oscillatory behavior of human locomotion for reproducing the planar covariation rule in both legs at different walking speeds. Neural network learning was based on sinusoid signals integrating frequency and amplitude features of the first three harmonics of the sagittal elevation angles of the thigh, shank and foot of each lower limb. We verified the biological plausibility of the neural networks. Best results were obtained with oscillations extracted from the first three harmonics in comparison to oscillations outside the harmonic frequency peaks. Physiological replication steadily increased with the number of neuronal units from 1 to 80, where similarity index reached 0.99. Analysis of synaptic weighting showed that the proportion of inhibitory connections consistently increased with the number of neuronal units in the DRNN. This emerging property in the artificial neural networks resonates with recent advances in neurophysiology of inhibitory neurons that are involved in central nervous system oscillatory activities. The main message of this study is that this type of DRNN may offer a useful model of physiological central pattern generator for gaining insights in basic research and developing clinical applications.

  20. Generated rules for AIDS and e-learning classifier using rough set approach

    Directory of Open Access Journals (Sweden)

    Sarina Sulaiman

    2016-07-01

    Full Text Available The emergence and growth of internet usage has accumulated an extensive amount of data. These data contain a wealth of undiscovered valuable information and problems of incomplete data set may lead to observation error. This research explored a technique to analyze data that transforms meaningless data to meaningful information. The work focused on Rough Set (RS to deal with incomplete data and rules derivation. Rules with high and low left-hand-side (LHS support value generated by RS were used as query statements to form a cluster of data. The model was tested on AIDS blog data set consisting of 146 bloggers and E-Learning@UTM (EL log data set comprising 23105 URLs. 5-fold and 10-fold cross validation were used to split the data. Naïve algorithm and Boolean algorithm as discretization techniques and Johnson’s algorithm (Johnson and Genetic algorithm (GA as reduction techniques were employed to compare the results. 5-fold cross validation tended to suit AIDS data well while 10-fold cross validation was the best for EL data set. Johnson and GA yielded the same number of rules for both data sets. These findings are significant as evidence in terms of accuracy that was achieved using the proposed model

  1. Improving Learning through Interventions of Student-Generated Questions and Concept Maps

    Science.gov (United States)

    Berry, Jack W.; Chew, Stephen L.

    2008-01-01

    Using the principles of the scholarship of teaching and learning, we evaluated 2 learning strategies to determine if they could improve student exam performance in general psychology. After the second of 3 exams, we gave students the option of participating in a specific learning activity and assessed its impact using the third exam. In Study 1,…

  2. Appreciative Inquiry: Guided Reflection to Generate Change in Service-Learning Courses

    Science.gov (United States)

    Lahman, Mary

    2012-01-01

    Service-learning scholars contend that engaging students in systematic reflection during community service promotes one, if not all, of the following student outcomes: (1) academic learning; (2) personal growth; and (3) civic engagement. For communication instructors in particular, Applegate and Morreale (1999) proposed that service-learning both…

  3. Benchmarking Deep Learning Frameworks for the Classification of Very High Resolution Satellite Multispectral Data

    Science.gov (United States)

    Papadomanolaki, M.; Vakalopoulou, M.; Zagoruyko, S.; Karantzalos, K.

    2016-06-01

    In this paper we evaluated deep-learning frameworks based on Convolutional Neural Networks for the accurate classification of multispectral remote sensing data. Certain state-of-the-art models have been tested on the publicly available SAT-4 and SAT-6 high resolution satellite multispectral datasets. In particular, the performed benchmark included the AlexNet, AlexNet-small and VGG models which had been trained and applied to both datasets exploiting all the available spectral information. Deep Belief Networks, Autoencoders and other semi-supervised frameworks have been, also, compared. The high level features that were calculated from the tested models managed to classify the different land cover classes with significantly high accuracy rates i.e., above 99.9%. The experimental results demonstrate the great potentials of advanced deep-learning frameworks for the supervised classification of high resolution multispectral remote sensing data.

  4. Drug name recognition in biomedical texts: a machine-learning-based method.

    Science.gov (United States)

    He, Linna; Yang, Zhihao; Lin, Hongfei; Li, Yanpeng

    2014-05-01

    Currently, there is an urgent need to develop a technology for extracting drug information automatically from biomedical texts, and drug name recognition is an essential prerequisite for extracting drug information. This article presents a machine-learning-based approach to recognize drug names in biomedical texts. In this approach, a drug name dictionary is first constructed with the external resource of DrugBank and PubMed. Then a semi-supervised learning method, feature coupling generalization, is used to filter this dictionary. Finally, the dictionary look-up and the condition random field method are combined to recognize drug names. Experimental results show that our approach achieves an F-score of 92.54% on the test set of DDIExtraction2011.

  5. The effects of question-generation training on metacognitive knowledge, self regulation and learning approaches in science.

    Science.gov (United States)

    Cano García, Francisco; García, Ángela; Berbén, A B G; Pichardo, M C; Justicia, Fernando

    2014-01-01

    Although much research has examined the impact of question generation on students' reading comprehension and learning from lectures, far less research has analysed its influence on how students learn and study science. The present study aims to bridge this knowledge gap. Using a quasi-experimental design, three complete ninth-grade science classes, with a total of 72 students, were randomly assigned to three conditions (groups): (G1) questioning-training by providing prompts; (G2) question-generation without any explicit instruction; and (G3) no question control. Participants' pre-test and post-test self-reported measures of metacognitive knowledge, self-regulation and learning approaches were collected and data analysed with multivariate and univariate analyses of covariance. (a) MANCOVA revealed a significant effect for group; (b) ANCOVAs showed the highest average gains for G1 and statistically significant between-group differences in the two components of metacognition: metacognitive knowledge and self-regulation; and (c) the direction of these differences seemed to vary in each of these components. Question-generation training influenced how students learned and studied, specifically their metacognition, and it had a medium to large effect size, which was somewhat related to the prompts used.

  6. A novel approach to locomotion learning: Actor-Critic architecture using central pattern generators and dynamic motor primitives.

    Science.gov (United States)

    Li, Cai; Lowe, Robert; Ziemke, Tom

    2014-01-01

    In this article, we propose an architecture of a bio-inspired controller that addresses the problem of learning different locomotion gaits for different robot morphologies. The modeling objective is split into two: baseline motion modeling and dynamics adaptation. Baseline motion modeling aims to achieve fundamental functions of a certain type of locomotion and dynamics adaptation provides a "reshaping" function for adapting the baseline motion to desired motion. Based on this assumption, a three-layer architecture is developed using central pattern generators (CPGs, a bio-inspired locomotor center for the baseline motion) and dynamic motor primitives (DMPs, a model with universal "reshaping" functions). In this article, we use this architecture with the actor-critic algorithms for finding a good "reshaping" function. In order to demonstrate the learning power of the actor-critic based architecture, we tested it on two experiments: (1) learning to crawl on a humanoid and, (2) learning to gallop on a puppy robot. Two types of actor-critic algorithms (policy search and policy gradient) are compared in order to evaluate the advantages and disadvantages of different actor-critic based learning algorithms for different morphologies. Finally, based on the analysis of the experimental results, a generic view/architecture for locomotion learning is discussed in the conclusion.

  7. A Novel Approach to Locomotion Learning: Actor-Critic Architecture using Central Pattern Generators and Dynamic Motor Primitives

    Directory of Open Access Journals (Sweden)

    Cai eLi

    2014-10-01

    Full Text Available In this article, we propose an architecture of a bio-inspired controller that addresses the problem of learning different locomotion gaits for different robot morphologies. The modelling objective is split into two: baseline motion modelling and dynamics adaptation. Baseline motion modelling aims to achieve fundamental functions of a certain type of locomotion and dynamics adaptation provides a ``reshaping function for adapting the baseline motion to desired motion. Based on this assumption, a three-layer architecture is developed using central pattern generators (CPGs, a bio-inspired locomotor center for the the baseline motion and dynamic motor primitives (DMPs, a model with universal ``reshaping functions. In this article, we use this architecture with the actor-critic algorithms for finding a good ``reshaping function. In order to demonstrate the learning power of the actor-critic based architecture, we tested it on two experiments: 1 learning to crawl on a humanoid and, 2 learning to gallop on a puppy robot. Two types of actor-critic algorithms (policy search and policy gradient are compared in order to evaluate the advantages and disadvantages of different actor-critic based learning algorithms for different morphologies. Finally, based on the analysis of the experimental results, a generic view/architecture for locomotion learning is discussed in the conclusion.

  8. Evaluation of machine learning tools for inspection of steam generator tube structures using pulsed eddy current

    Science.gov (United States)

    Buck, J. A.; Underhill, P. R.; Morelli, J.; Krause, T. W.

    2017-02-01

    Degradation of nuclear steam generator (SG) tubes and support structures can result in a loss of reactor efficiency. Regular in-service inspection, by conventional eddy current testing (ECT), permits detection of cracks, measurement of wall loss, and identification of other SG tube degradation modes. However, ECT is challenged by overlapping degradation modes such as might occur for SG tube fretting accompanied by tube off-set within a corroding ferromagnetic support structure. Pulsed eddy current (PEC) is an emerging technology examined here for inspection of Alloy-800 SG tubes and associated carbon steel drilled support structures. Support structure hole size was varied to simulate uniform corrosion, while SG tube was off-set relative to hole axis. PEC measurements were performed using a single driver with an 8 pick-up coil configuration in the presence of flat-bottom rectangular frets as an overlapping degradation mode. A modified principal component analysis (MPCA) was performed on the time-voltage data in order to reduce data dimensionality. The MPCA scores were then used to train a support vector machine (SVM) that simultaneously targeted four independent parameters associated with; support structure hole size, tube off-centering in two dimensions and fret depth. The support vector machine was trained, tested, and validated on experimental data. Results were compared with a previously developed artificial neural network (ANN) trained on the same data. Estimates of tube position showed comparable results between the two machine learning tools. However, the ANN produced better estimates of hole inner diameter and fret depth. The better results from ANN analysis was attributed to challenges associated with the SVM when non-constant variance is present in the data.

  9. Age related-changes in the neural basis of self-generation in verbal paired associate learning

    Directory of Open Access Journals (Sweden)

    Jennifer Vannest

    2015-01-01

    Full Text Available Verbal information is better retained when it is self-generated rather than when it is received passively. The application of self-generation procedures has been found to improve memory in healthy elderly and in individuals with impaired cognition. Overall, the available studies support the notion that active participation in verbal encoding engages memory mechanisms that supplement those used during passive observation. Thus, the objective of this study was to investigate the age-related changes in the neural mechanisms involved in the encoding of paired-associates using a self-generation method that has been shown to improve memory performance across the lifespan. Subjects were 113 healthy right-handed adults (Edinburgh Handedness Inventory >50; 67 females ages 18–76, native speakers of English with no history of neurological or psychiatric disorders. Subjects underwent fMRI at 3 T while performing didactic learning (“read” or self-generation learning (“generate” of 30 word pairs per condition. After fMRI, recognition memory for the second word in each pair was evaluated outside of the scanner. On the post-fMRI testing more “generate” words were correctly recognized than “read” words (p < 0.001 with older adults recognizing the “generated” words less accurately (p < 0.05. Independent component analysis of fMRI data identified task-related brain networks. Several components were positively correlated with the task reflecting multiple cognitive processes involved in self-generated encoding; other components correlated negatively with the task, including components of the default-mode network. Overall, memory performance on generated words decreased with age, but the benefit from self-generation remained consistently significant across ages. Independent component analysis of the neuroimaging data revealed an extensive set of components engaged in self-generation learning compared with didactic learning, and identified

  10. Age Difference and Face-Saving in an Inter-Generational Problem-Based Learning Group

    Science.gov (United States)

    Robinson, Leslie

    2016-01-01

    This study used grounded theory methodology to investigate whether learning in a problem-based learning (PBL) group was influenced by student demographic diversity. Data comprised observations, in the form of video footage, of one first-year PBL group carried out over the period of an academic year, along with student interviews. Using the…

  11. An experimental study on expectations and learning in overlapping generations models

    NARCIS (Netherlands)

    Heemeijer, P.; Hommes, C.H.; Sonnemans, J.; Tuinstra, J.

    2012-01-01

    A plethora of models of learning has been developed and studied in macro-economic models in recent years. In this paper we will try to discriminate between these learning models by running laboratory experiments with incentivized human subjects. Participants predict inflation rates for 50 successive

  12. Automatic Organization and Generation of Presentation Slides for E-Learning

    Science.gov (United States)

    Sathiyamurthy, K.; Geetha, T. V.

    2012-01-01

    The effectiveness of an e-learning system for distance education to a large extent depends on the relevancy and presentation of learning content to the learner. The ability to gather documents on a particular topic from the web and adapt the contents of the document to suit the learner is an important task from the content creation perspective of…

  13. Social Web Content Enhancement in a Distance Learning Environment: Intelligent Metadata Generation for Resources

    Science.gov (United States)

    García-Floriano, Andrés; Ferreira-Santiago, Angel; Yáñez-Márquez, Cornelio; Camacho-Nieto, Oscar; Aldape-Pérez, Mario; Villuendas-Rey, Yenny

    2017-01-01

    Social networking potentially offers improved distance learning environments by enabling the exchange of resources between learners. The existence of properly classified content results in an enhanced distance learning experience in which appropriate materials can be retrieved efficiently; however, for this to happen, metadata needs to be present.…

  14. Concrescent Conversations: Generating a Cooperative Learning Experience in Principles of Management--A Postmodern Analysis

    Science.gov (United States)

    Akan, Obasi Haki

    2005-01-01

    By taking a postmodern ontology that elevates becoming over the modern ontology of being, the author of this article proposes a theory and describes a method that teachers can use to enhance students' cooperative learning of management principles. The author asserts that the social construction of learning groups is an effect of organizing…

  15. Learning a Generative Probabilistic Grammar of Experience: A Process-Level Model of Language Acquisition

    Science.gov (United States)

    Kolodny, Oren; Lotem, Arnon; Edelman, Shimon

    2015-01-01

    We introduce a set of biologically and computationally motivated design choices for modeling the learning of language, or of other types of sequential, hierarchically structured experience and behavior, and describe an implemented system that conforms to these choices and is capable of unsupervised learning from raw natural-language corpora. Given…

  16. Concrescent Conversations: Generating a Cooperative Learning Experience in Principles of Management--A Postmodern Analysis

    Science.gov (United States)

    Akan, Obasi Haki

    2005-01-01

    By taking a postmodern ontology that elevates becoming over the modern ontology of being, the author of this article proposes a theory and describes a method that teachers can use to enhance students' cooperative learning of management principles. The author asserts that the social construction of learning groups is an effect of organizing…

  17. The Role of a Reference Synthetic Data Generator within the Field of Learning Analytics

    Science.gov (United States)

    Berg, Alan\tM.; Mol, Stefan T.; Kismihók, Gábor; Sclater, Niall

    2016-01-01

    This paper details the anticipated impact of synthetic "big" data on learning analytics (LA) infrastructures, with a particular focus on data governance, the acceleration of service development, and the benchmarking of predictive models. By reviewing two cases, one at the sector-wide level (the Jisc learning analytics architecture) and…

  18. Learning Nursing in the Workplace Community: The Generation of Professional Capital

    Science.gov (United States)

    Gobbi, Mary

    This chapter explores the connections between learning, working and professional communities in nursing. It draws on experiences and research in nursing practice and education, where not only do isolated professionals learn as a result of their actions for patients and others, but those professionals are part of a community whose associated networks enable learning to occur. Several characteristics of this professional community are shared with those found in Communities of Practice (CoPs) (Lave and Wenger, 1991; Wenger, 1998), but the balance and importance of many elements can differ. For instance, whilst Lave and Wenger (1991) describe many aspects of situated learning in CoPs that apply to nurses, their model is of little help in understanding the ways in which other professions as well as patients/clients and carers influence the development of nursing practice. Therefore, I shall argue that it is not just the Community of Practice that we need to consider

  19. IBook-Interactive and Semantic Multimedia Content Generation for eLearning

    Directory of Open Access Journals (Sweden)

    Arjumand Younus

    2011-05-01

    Full Text Available Over the years the World Wide Web has seen a major transformation with dynamic content and interactivity being delivered through Web 2.0 and provision of meaning to Web content through the Semantic Web. Web 2.0 has given rise to special methods of eLearning; we believe that interactive multimedia and semantic technologies applied together can further enable effective reuse of such applications thereby taking eLearning a step further. As proof of this idea we present IBook which is an eLearning application that uses concepts from both the fields of Web 2.0 and Semantic Web. It presents multimedia in a form that enhances the users learning experience through the use of Web 2.0 and Semantic Web.

  20. Keeping it personal: self-generated learning tools for lifelong professional development

    OpenAIRE

    Cooper, Barry; Pickering, Maggie

    2010-01-01

    Approaches to learning in the twenty-first century need to reflect student diversity in order to widen and sustain participation in education and continuing professional development (CPD). There is some evidence of success in widening access to professional social care employment through training and qualification programmes but a notable lack of success in sustaining this participation into CPD and lifelong professional learning. This paper argues for an increased contribution of more person...

  1. Hebbian learning in a model with dynamic rate-coded neurons: an alternative to the generative model approach for learning receptive fields from natural scenes.

    Science.gov (United States)

    Hamker, Fred H; Wiltschut, Jan

    2007-09-01

    Most computational models of coding are based on a generative model according to which the feedback signal aims to reconstruct the visual scene as close as possible. We here explore an alternative model of feedback. It is derived from studies of attention and thus, probably more flexible with respect to attentive processing in higher brain areas. According to this model, feedback implements a gain increase of the feedforward signal. We use a dynamic model with presynaptic inhibition and Hebbian learning to simultaneously learn feedforward and feedback weights. The weights converge to localized, oriented, and bandpass filters similar as the ones found in V1. Due to presynaptic inhibition the model predicts the organization of receptive fields within the feedforward pathway, whereas feedback primarily serves to tune early visual processing according to the needs of the task.

  2. Creating the New from the Old: Combinatorial Libraries Generation with Machine-Learning-Based Compound Structure Optimization.

    Science.gov (United States)

    Podlewska, Sabina; Czarnecki, Wojciech M; Kafel, Rafał; Bojarski, Andrzej J

    2017-02-15

    The growing computational abilities of various tools that are applied in the broadly understood field of computer-aided drug design have led to the extreme popularity of virtual screening in the search for new biologically active compounds. Most often, the source of such molecules consists of commercially available compound databases, but they can also be searched for within the libraries of structures generated in silico from existing ligands. Various computational combinatorial approaches are based solely on the chemical structure of compounds, using different types of substitutions for new molecules formation. In this study, the starting point for combinatorial library generation was the fingerprint referring to the optimal substructural composition in terms of the activity toward a considered target, which was obtained using a machine learning-based optimization procedure. The systematic enumeration of all possible connections between preferred substructures resulted in the formation of target-focused libraries of new potential ligands. The compounds were initially assessed by machine learning methods using a hashed fingerprint to represent molecules; the distribution of their physicochemical properties was also investigated, as well as their synthetic accessibility. The examination of various fingerprints and machine learning algorithms indicated that the Klekota-Roth fingerprint and support vector machine were an optimal combination for such experiments. This study was performed for 8 protein targets, and the obtained compound sets and their characterization are publically available at http://skandal.if-pan.krakow.pl/comb_lib/ .

  3. Teacher education in the generative virtual classroom: developing learning theories through a web-delivered, technology-and-science education context

    Science.gov (United States)

    Schaverien, Lynette

    2003-12-01

    This paper reports the use of a research-based, web-delivered, technology-and-science education context (the Generative Virtual Classroom) in which student-teachers can develop their ability to recognize, describe, analyse and theorize learning. Addressing well-recognized concerns about narrowly conceived, anachronistic and ineffective technology-and-science education, this e-learning environment aims to use advanced technologies for learning, to bring about larger scale improvement in classroom practice than has so far been effected by direct intervention through teacher education. Student-teachers' short, intensive engagement with the Generative Virtual Classroom during their practice teaching is examined. Findings affirm the worth of this research-based e-learning system for teacher education and the power of a biologically based, generative theory to make sense of the learning that occurred.

  4. High Temperature Gas-Cooled Reactors Lessons Learned Applicable to the Next Generation Nuclear Plant

    Energy Technology Data Exchange (ETDEWEB)

    J. M. Beck; L. F. Pincock

    2011-04-01

    The purpose of this report is to identify possible issues highlighted by these lessons learned that could apply to the NGNP in reducing technical risks commensurate with the current phase of design. Some of the lessons learned have been applied to the NGNP and documented in the Preconceptual Design Report. These are addressed in the background section of this document and include, for example, the decision to use TRISO fuel rather than BISO fuel used in the Peach Bottom reactor; the use of a reactor pressure vessel rather than prestressed concrete found in Fort St. Vrain; and the use of helium as a primary coolant rather than CO2. Other lessons learned, 68 in total, are documented in Sections 2 through 6 and will be applied, as appropriate, in advancing phases of design. The lessons learned are derived from both negative and positive outcomes from prior HTGR experiences. Lessons learned are grouped according to the plant, areas, systems, subsystems, and components defined in the NGNP Preconceptual Design Report, and subsequent NGNP project documents.

  5. Nonlinear Deep Kernel Learning for Image Annotation.

    Science.gov (United States)

    Jiu, Mingyuan; Sahbi, Hichem

    2017-02-08

    Multiple kernel learning (MKL) is a widely used technique for kernel design. Its principle consists in learning, for a given support vector classifier, the most suitable convex (or sparse) linear combination of standard elementary kernels. However, these combinations are shallow and often powerless to capture the actual similarity between highly semantic data, especially for challenging classification tasks such as image annotation. In this paper, we redefine multiple kernels using deep multi-layer networks. In this new contribution, a deep multiple kernel is recursively defined as a multi-layered combination of nonlinear activation functions, each one involves a combination of several elementary or intermediate kernels, and results into a positive semi-definite deep kernel. We propose four different frameworks in order to learn the weights of these networks: supervised, unsupervised, kernel-based semisupervised and Laplacian-based semi-supervised. When plugged into support vector machines (SVMs), the resulting deep kernel networks show clear gain, compared to several shallow kernels for the task of image annotation. Extensive experiments and analysis on the challenging ImageCLEF photo annotation benchmark, the COREL5k database and the Banana dataset validate the effectiveness of the proposed method.

  6. Modeling and Estimating of Load Demand of Electricity Generated from Hydroelectric Power Plants in Turkey using Machine Learning Methods

    Directory of Open Access Journals (Sweden)

    DURSUN, B.

    2014-02-01

    Full Text Available In this study, the electricity load demand, between 2012 and 2021, has been estimated using the load demand of the electricity generated from hydroelectric power plants in Turkey between 1970 and 2011. Among machine learning algorithms, Multilayer Perceptron, Locally Weighted Learning, Additive Regression, M5Rules and ZeroR classifiers are used to estimate the electricity load demand. Among them, M5Rules and Multilayer Perceptron classifiers are observed to have better performance than the others. ZeroR classifier is a kind of majority classifier used to compare the performances of other classifiers. Locally Weighted Learning and Additive Regression classifiers are Meta classifiers. In the training period conducted by Locally Weighted Learning and Additive Regression classifiers, when Multilayer Perceptron and M5Rules classifiers are chosen respectively, it is possible to obtain models with the highest performance. As a result of the experiments performed using M5Rules and Multilayer Perceptron classifiers, correlation coefficient values of 0.948 and 0.9933 are obtained respectively. And, Mean Absolute Error and Root Mean Squared Error value of Multilayer Perceptron classifier are closer to zero than that of M5Rules classifier. Therefore, it can be said the model performed by Multilayer Perceptron classifier has the best performance compared to the models of other classifiers.

  7. Finishing and Special Motifs: Lessons Learned from CRISPR Analysis Using Next-Generation Draft Sequences ( 7th Annual SFAF Meeting, 2012)

    Energy Technology Data Exchange (ETDEWEB)

    Campbell, Catherine [Noblis

    2012-06-01

    Catherine Campbell on "Finishing and Special Motifs: Lessons learned from CRISPR analysis using next-generation draft sequences" at the 2012 Sequencing, Finishing, Analysis in the Future Meeting held June 5-7, 2012 in Santa Fe, New Mexico.

  8. Analysis of dynamic Cournot learning models for generation companies based on conjectural variations and forward expectation

    Energy Technology Data Exchange (ETDEWEB)

    Gutierrez-Alcaraz, G.; Tovar-Hernandez, Jose H.; Moreno-Goytia, Edgar L. [Departamento de Ingenieria Electrica y Electronica del Instituto Tecnologico de Morelia, Morelia, Mich., Mexico (Mexico)

    2009-12-15

    Electricity spot markets generally operate on an hourly basis; under this condition GENCOs can closely observe their competitors' market behavior. For this purposes, a detailed dynamic model is one of the tools used by GENCOs to understand the behavioral variations of competitors over time. The required abilities to rapidly adjust one's own decision-making create a need for new learning procedures and models. Conjectural variations (CV) have been proposed as a learning approach. In this paper a model based on forward expectations (FE) is proposed as a learning approach, and through illustrative examples it is shown that the market equilibria found by the CV model are also obtained by the FE model. (author)

  9. Creative Generation of 3D Objects with Deep Learning and Innovation Engines

    DEFF Research Database (Denmark)

    Lehman, Joel Anthony; Risi, Sebastian; Clune, Jeff

    2016-01-01

    Advances in supervised learning with deep neural networks have enabled robust classification in many real world domains. An interesting question is if such advances can also be leveraged effectively for computational creativity. One insight is that because evolutionary algorithms are free from...... strict requirements of mathematical smoothness, they can exploit powerful deep learning representations through arbitrary computational pipelines. In this way, deep networks trained on typical supervised tasks can be used as an ingredient in an evolutionary algorithm driven towards creativity....... To highlight such potential, this paper creates novel 3D objects by leveraging feedback from a deep network trained only to recognize 2D images. This idea is tested by extending previous work with Innovation Engines, i.e. a principled combination of deep learning and evolutionary algorithms for computational...

  10. Creative Generation of 3D Objects with Deep Learning and Innovation Engines

    DEFF Research Database (Denmark)

    Lehman, Joel Anthony; Risi, Sebastian; Clune, Jeff

    2016-01-01

    Advances in supervised learning with deep neural networks have enabled robust classification in many real world domains. An interesting question is if such advances can also be leveraged effectively for computational creativity. One insight is that because evolutionary algorithms are free from...... strict requirements of mathematical smoothness, they can exploit powerful deep learning representations through arbitrary computational pipelines. In this way, deep networks trained on typical supervised tasks can be used as an ingredient in an evolutionary algorithm driven towards creativity....... To highlight such potential, this paper creates novel 3D objects by leveraging feedback from a deep network trained only to recognize 2D images. This idea is tested by extending previous work with Innovation Engines, i.e. a principled combination of deep learning and evolutionary algorithms for computational...

  11. Millennial generation student nurses' perceptions of the impact of multiple technologies on learning.

    Science.gov (United States)

    Montenery, Susan M; Walker, Marjorie; Sorensen, Elizabeth; Thompson, Rhonda; Kirklin, Dena; White, Robin; Ross, Carl

    2013-01-01

    To determine how millennial nursing students perceive the effects of instructional technology on their attentiveness, knowledge, critical thinking, and satisfaction. BACKGROUND Millennial learners develop critical thinking through experimentation, active participation, and multitasking with rapid shifts between technological devices. They desire immediate feedback. METHOD; A descriptive, longitudinal, anonymous survey design was used with a convenience sample of 108 sophomore, junior, and senior baccalaureate nursing students (participation rates 95 percent, winter, 85 percent, spring). Audience response, virtual learning, simulation, and computerized testing technologies were used. An investigator-designed instrument measured attentiveness, knowledge, critical thinking, and satisfaction (Cronbach's alphas 0.73, winter; 0.84, spring). Participants positively rated the audience response, virtual learning, and simulation instructional technologies on their class participation, learning, attention, and satisfaction. They strongly preferred computerized testing. Consistent with other studies, these students engaged positively with new teaching strategies using contemporary instructional technology. Faculty should consider using instructional technologies.

  12. First-Generation College Student Dissertation Abstracts: Research Strategies, Topical Analysis, and Lessons Learned

    Science.gov (United States)

    Banning, James H.

    2014-01-01

    First-generation college students are students whose parents or guardians did not obtain a four year college degree (Davis, 2012). As a group these students make up a large part of the college student population and are often reported to encounter difficulties in their campus experience. While the topic of first-generation student has received…

  13. Making errorless learning more active: self-generation in an error free learning context is superior to standard errorless learning of face-name associations in people with Alzheimer's disease.

    Science.gov (United States)

    Laffan, Amanda J; Metzler-Baddeley, Claudia; Walker, Ian; Jones, Roy W

    2010-04-01

    Errorless learning (EL) principles have been shown to enable people with memory impairments to acquire various types of information (Grandmaison & Simard, 2003; Wilson, 2005). However, the effects of EL, based on simple repetition only, tend to be limited with regards to their size and longevity. The present study investigated whether EL could be improved by actively engaging people with Alzheimer's disease in the learning process. Patients learned the names of famous faces over 10 training sessions, treated either with a non-learning control, a simple repetition EL procedure, or an EL condition in which responses had to be self-generated. Cued recall rates after the final training session were significantly greater for the names treated with the self-generated EL technique compared to the control and the repetition EL conditions. In addition, there was evidence that patients with less severe general cognitive impairment benefit more from active generation than more severely impaired patients. The implications of this research for individualised memory rehabilitation programmes are discussed.

  14. An Automated Method to Generate e-Learning Quizzes from Online Language Learner Writing

    Science.gov (United States)

    Flanagan, Brendan; Yin, Chengjiu; Hirokawa, Sachio; Hashimoto, Kiyota; Tabata, Yoshiyuki

    2013-01-01

    In this paper, the entries of Lang-8, which is a Social Networking Site (SNS) site for learning and practicing foreign languages, were analyzed and found to contain similar rates of errors for most error categories reported in previous research. These similarly rated errors were then processed using an algorithm to determine corrections suggested…

  15. Scaffolding students' use of learner-generated content in a technology-enhanced inquiry learning environment

    NARCIS (Netherlands)

    Dijk, van Alieke M.; Lazonder, Ard W.

    2016-01-01

    Having students inspect and use each other's work is a promising way to advance inquiry-based science learning. Research has nevertheless shown that additional guidance is needed for students to take full advantage of the work produced by their peers. The present study investigated whether scaffoldi

  16. Hypothesis Generation, Evaluation, and Memory Abilities in Adult Human Concept Learning.

    Science.gov (United States)

    Cason, Carolyn L.; And Others

    Studies were made between performance on tests of mental abilities and concept learning tasks; it is pointed out that the researcher is usually confronted with administering large batteries of tests of mental abilities and then analyzing his results with one of the factor analytic techniques. An information process analysis of tests of mental…

  17. Combining Generative and Discriminative Representation Learning for Lung CT Analysis With Convolutional Restricted Boltzmann Machines

    NARCIS (Netherlands)

    G. van Tulder (Gijs); M. de Bruijne (Marleen)

    2016-01-01

    textabstractThe choice of features greatly influences the performance of a tissue classification system. Despite this, many systems are built with standard, predefined filter banks that are not optimized for that particular application. Representation learning methods such as restricted Boltzmann ma

  18. THIRD GENERATION TELEPHONY: NEW TECHNOLOGICAL SUPPORT FOR COMPUTER ASSISTED LANGUAGE LEARNING

    Directory of Open Access Journals (Sweden)

    Jose Carlos Garcia Cabrero

    2002-06-01

    Full Text Available The expansion of the lnternet has Ied to the development of distance teaching models based on the net (e learning. One of the crucial factors in this phenomenon is the continuous training required by workers to maintain or improve their professional skills. Foreign languages are, without doubt, one of the most in demand subjects. This is because they are needed for comunication in an increasingly globalized world. The development of new wireless communication technologies, UMTS or 3G nets, and their corresponding access terminals (Palm-size PCs, PPCs, with wireless telephone connection. also called smart-phones. will enable solutions to be found for some of the problems Iaeed hy current e-learning users. These problems include access speed and the physical constraints of tlhe ( The new wireless comunication technologies will bring other benefits like portability. always on-line, etc. This article presents one on' the world's first prototypes of language learning software or smart-phones, produced by the Laboratorio de lngenieria Didactica e lngenieria Linguistica of UNED (Didactic Engineering and Linguistic Engineering Laboratory (LIDIL, http://www.vip.~ined.es. i¿le Enllearning Spanish for business.

  19. Generative Learning Strategy Use and Self-Regulatory Prompting in Digital Text

    Science.gov (United States)

    Reid, Alan J.; Morrison, Gary M.

    2014-01-01

    The digital revolution is shifting print-based textbooks to digital text, and it has afforded the opportunity to incorporate meaningful learning strategies and otherwise separate metacognitive activities directly into these texts as embedded support. A sample of 89 undergraduates read a digital, expository text on the basics of photography. The…

  20. Narrative Generates a Learning Spiral in Education: Recognition, Reflection, and Reconstruction

    Science.gov (United States)

    Liu, Xueyang

    2015-01-01

    The narrative form is everywhere. It can be as common as our daily stories and as significant as a great novel. Narrating can be a process of self-assessment and introspection around a certain theme. In this sense it is important in education. In this paper I argue that people learn not only by listening to narrative but also by teaching others…

  1. Combining Generative and Discriminative Representation Learning for Lung CT Analysis With Convolutional Restricted Boltzmann Machines

    NARCIS (Netherlands)

    G. van Tulder (Gijs); M. de Bruijne (Marleen)

    2016-01-01

    textabstractThe choice of features greatly influences the performance of a tissue classification system. Despite this, many systems are built with standard, predefined filter banks that are not optimized for that particular application. Representation learning methods such as restricted Boltzmann

  2. Applied Use of a Health Communications Model to Generate Interest in Learning

    Science.gov (United States)

    Banas, Jennifer Rebecca

    2009-01-01

    Educators are regularly challenged to design instruction that motivates learners. If interest is lacking, the challenge can be even greater. Tailoring is a message design technique that could help to stimulate interest and consequently, motivation to learn. Effective tailoring requires the formal assessment of learner characteristics and the…

  3. Information-Seeking Behavior in Generation Y Students: Motivation, Critical Thinking, and Learning Theory

    Science.gov (United States)

    Weiler, Angela

    2005-01-01

    Research in information-seeking behavior, motivation, critical thinking, and learning theory was explored and compared in a search for possible motivating factors behind students' dependence on television and the Internet for their information needs. The research indicates that only a very small percentage of the general population prefer to learn…

  4. Leaders Behaving Badly: Using Power to Generate Undiscussables in Action Learning Sets

    Science.gov (United States)

    Donovan, Paul Jeffrey

    2014-01-01

    "Undiscussables" are topics associated with threat or embarrassment that are avoided by groups, where that avoidance is also not discussed. Their deleterious effect on executive groups has been a point of discussion for several decades. More recently critical action learning (AL) has brought a welcome focus to power relations within AL…

  5. Glial Tumor Necrosis Factor Alpha (TNFα) Generates Metaplastic Inhibition of Spinal Learning

    Science.gov (United States)

    Huie, J. Russell; Baumbauer, Kyle M.; Lee, Kuan H.; Bresnahan, Jacqueline C.; Beattie, Michael S.; Ferguson, Adam R.; Grau, James W.

    2012-01-01

    Injury-induced overexpression of tumor necrosis factor alpha (TNFα) in the spinal cord can induce chronic neuroinflammation and excitotoxicity that ultimately undermines functional recovery. Here we investigate how TNFα might also act to upset spinal function by modulating spinal plasticity. Using a model of instrumental learning in the injured spinal cord, we have previously shown that peripheral intermittent stimulation can produce a plastic change in spinal plasticity (metaplasticity), resulting in the prolonged inhibition of spinal learning. We hypothesized that spinal metaplasticity may be mediated by TNFα. We found that intermittent stimulation increased protein levels in the spinal cord. Using intrathecal pharmacological manipulations, we showed TNFα to be both necessary and sufficient for the long-term inhibition of a spinal instrumental learning task. These effects were found to be dependent on glial production of TNFα and involved downstream alterations in calcium-permeable AMPA receptors. These findings suggest a crucial role for glial TNFα in undermining spinal learning, and demonstrate the therapeutic potential of inhibiting TNFα activity to rescue and restore adaptive spinal plasticity to the injured spinal cord. TNFα modulation represents a novel therapeutic target for improving rehabilitation after spinal cord injury. PMID:22745823

  6. The e-Generation: The Use of Technology for Foreign Language Learning

    Science.gov (United States)

    Gonzalez-Vera, Pilar

    2016-01-01

    After the Bologna Process, European Higher Education was reformulated as a response to a change of roles in higher education in a globalised society. The implementation of a new system of credits, the European Credit Transfer System (ECTS), implied an enormous increase of autonomous learning hours. The high percentage of student workload reflected…

  7. When Failure Is Not An Option: Designing Competency-Based Pathways for Next Generation Learning

    Science.gov (United States)

    Sturgis, Chris; Patrick, Susan

    2010-01-01

    This exploration into competency-based innovation at the school, district, and state levels suggests that competency-based pathways are a re-engineering of this nation's education system around learning--a re-engineering designed for success in which failure is no longer an option. Competency-based approaches build upon standards reforms, offering…

  8. When Success Is the Only Option: Designing Competency-Based Pathways for Next Generation Learning

    Science.gov (United States)

    Sturgis, Chris; Patrick, Susan

    2010-01-01

    This exploration into competency-based innovation at the school, district, and state levels suggests that competency-based pathways are a re-engineering of this nation's education system around learning--a re-engineering designed for success in which failure is no longer viable. This discussion draws on interviews and site visits with innovators…

  9. The role of a reference synthetic data generator within the field of learning analytics

    NARCIS (Netherlands)

    Berg, A.M.; Mol, S.T.; Kismihók, G.; Sclater, N.

    2016-01-01

    This paper details the anticipated impact of synthetic ‘big’ data on learning analytics (LA) infrastructures, with a particular focus on data governance, the acceleration of service development, and the benchmarking of predictive models. By reviewing two cases, one at sector wide level and the other

  10. The role of a reference synthetic data generator within the field of learning analytics

    NARCIS (Netherlands)

    Berg, A.M.; Mol, S.T.; Kismihók, G.; Sclater, N.

    2016-01-01

    This paper details the anticipated impact of synthetic ‘big’ data on learning analytics (LA) infrastructures, with a particular focus on data governance, the acceleration of service development, and the benchmarking of predictive models. By reviewing two cases, one at sector wide level and the other

  11. Milestones and Millennials: A Perfect Pairing-Competency-Based Medical Education and the Learning Preferences of Generation Y.

    Science.gov (United States)

    Desy, Janeve R; Reed, Darcy A; Wolanskyj, Alexandra P

    2017-02-01

    Millennials are quickly becoming the most prevalent generation of medical learners. These individuals have a unique outlook on education and have different preferences and expectations than their predecessors. As evidenced by its implementation by the Accreditation Council for Graduate Medical Education in the United States and the Royal College of Physicians and Surgeons in Canada, competency based medical education is rapidly gaining international acceptance. Characteristics of competency based medical education can be perfectly paired with Millennial educational needs in several dimensions including educational expectations, the educational process, attention to emotional quotient and professionalism, assessment, feedback, and intended outcomes. We propose that with its attention to transparency, personalized learning, and frequent formative assessment, competency based medical education is an ideal fit for the Millennial generation as it realigns education and assessment with the needs of these 21st century learners.

  12. Cross-View Action Recognition via Transferable Dictionary Learning.

    Science.gov (United States)

    Zheng, Jingjing; Jiang, Zhuolin; Chellappa, Rama

    2016-06-01

    Discriminative appearance features are effective for recognizing actions in a fixed view, but may not generalize well to a new view. In this paper, we present two effective approaches to learn dictionaries for robust action recognition across views. In the first approach, we learn a set of view-specific dictionaries where each dictionary corresponds to one camera view. These dictionaries are learned simultaneously from the sets of correspondence videos taken at different views with the aim of encouraging each video in the set to have the same sparse representation. In the second approach, we additionally learn a common dictionary shared by different views to model view-shared features. This approach represents the videos in each view using a view-specific dictionary and the common dictionary. More importantly, it encourages the set of videos taken from the different views of the same action to have the similar sparse representations. The learned common dictionary not only has the capability to represent actions from unseen views, but also makes our approach effective in a semi-supervised setting where no correspondence videos exist and only a few labeled videos exist in the target view. The extensive experiments using three public datasets demonstrate that the proposed approach outperforms recently developed approaches for cross-view action recognition.

  13. Learning for Semantic Parsing and Natural Language Generation Using Statistical Machine Translation Techniques

    Science.gov (United States)

    2007-08-01

    XTAG grammar used by FERGUS is a bidirectional (or reversible) grammar that has been used for parsing as well ( Schabes and Joshi, 1988). The use of a...answer- ing (Wang et al., 2007), and syntactic parsing for resource-poor languages (Chiang et al., 2006). Shieber and Schabes (1990a,b) propose that... Schabes , 1990b; Bos, 2005; Zettlemoyer and Collins, 2007). In the future, we would like to devise learning algorithms similar to WASP that construct

  14. NASA's Learning Technology Project: Developing Educational Tools for the Next Generation of Explorers

    Science.gov (United States)

    Federman, A. N.; Hogan, P. J.

    2003-12-01

    Since 1996, NASA's Learning Technology has pioneered the use of innovative technology toinspire students to pursue careers in STEM(Science, Technology, Engineering and Math.) In the past this has included Web sites like Quest and the Observatorium, webcasts and distance learning courses, and even interactive television broadcasts. Our current focus is on development of several mission oriented software packages, targeted primarily at the middle-school population, but flexible enough to be used by elementary to graduate students. These products include contributions to an open source solar system simulator, a 3D planetary encyclopedia), development of a planetary surface viewer (atlas) and others. Whenever possible these software products are written to be 'open source' and multi-platform, for the widest use and easiest access for developers. Along with the software products, we are developing activities and lesson plans that are tested and used by educators in the classroom. The products are reviewed by professional educators. Together these products constitute the NASA Experential Platform for learning, in which the tools used by the public are similar (and in some respects) the same as those used by professional investigators. Efforts are now underway to incorporate actual MODIS and other real time data uplink capabilities.

  15. On the relationships between generative encodings, regularity, and learning abilities when evolving plastic artificial neural networks.

    Science.gov (United States)

    Tonelli, Paul; Mouret, Jean-Baptiste

    2013-01-01

    A major goal of bio-inspired artificial intelligence is to design artificial neural networks with abilities that resemble those of animal nervous systems. It is commonly believed that two keys for evolving nature-like artificial neural networks are (1) the developmental process that links genes to nervous systems, which enables the evolution of large, regular neural networks, and (2) synaptic plasticity, which allows neural networks to change during their lifetime. So far, these two topics have been mainly studied separately. The present paper shows that they are actually deeply connected. Using a simple operant conditioning task and a classic evolutionary algorithm, we compare three ways to encode plastic neural networks: a direct encoding, a developmental encoding inspired by computational neuroscience models, and a developmental encoding inspired by morphogen gradients (similar to HyperNEAT). Our results suggest that using a developmental encoding could improve the learning abilities of evolved, plastic neural networks. Complementary experiments reveal that this result is likely the consequence of the bias of developmental encodings towards regular structures: (1) in our experimental setup, encodings that tend to produce more regular networks yield networks with better general learning abilities; (2) whatever the encoding is, networks that are the more regular are statistically those that have the best learning abilities.

  16. On the relationships between generative encodings, regularity, and learning abilities when evolving plastic artificial neural networks.

    Directory of Open Access Journals (Sweden)

    Paul Tonelli

    Full Text Available A major goal of bio-inspired artificial intelligence is to design artificial neural networks with abilities that resemble those of animal nervous systems. It is commonly believed that two keys for evolving nature-like artificial neural networks are (1 the developmental process that links genes to nervous systems, which enables the evolution of large, regular neural networks, and (2 synaptic plasticity, which allows neural networks to change during their lifetime. So far, these two topics have been mainly studied separately. The present paper shows that they are actually deeply connected. Using a simple operant conditioning task and a classic evolutionary algorithm, we compare three ways to encode plastic neural networks: a direct encoding, a developmental encoding inspired by computational neuroscience models, and a developmental encoding inspired by morphogen gradients (similar to HyperNEAT. Our results suggest that using a developmental encoding could improve the learning abilities of evolved, plastic neural networks. Complementary experiments reveal that this result is likely the consequence of the bias of developmental encodings towards regular structures: (1 in our experimental setup, encodings that tend to produce more regular networks yield networks with better general learning abilities; (2 whatever the encoding is, networks that are the more regular are statistically those that have the best learning abilities.

  17. Developing the Next Generation of Civic-Minded Neuroscience Scholars: Incorporating Service Learning and Advocacy Throughout a Neuroscience Program.

    Science.gov (United States)

    Fox, Cecilia M

    2015-01-01

    The Neuroscience Program of Moravian College aspires to produce well-informed, morally responsible and civically engaged individuals who will become the next generation of community leaders. Through the integration of service learning and advocacy into a Neuroscience curriculum, undergraduates are consistently involved in meaningful community service with instruction and reflection that enriches their learning experience, teaches civic responsibility and strengthens their college and local communities. As a result of our brain awareness outreach programming, formation of a local Society for Neuroscience chapter and advocacy for scientific funding initiatives, we have created a model of student engagement that has connected the academic to the practical in life altering ways for our undergraduates. Our service experiences have become an educational awakening as critical reflective thought creates new meaning and leads to growth and the ability to take informed actions. As expressed in our students' portfolio writings, our service learning endeavors have lead to personal growth, contributed to humane conditions and engaged these citizens in purposeful association with one another.

  18. Underspecification-Based Grammatical Feedback Generation Tailored to the Learner's Current Acquisition Level in an e-Learning System for German as Second Language

    Science.gov (United States)

    Harbusch, Karin; Cameran, Christel-Joy; Härtel, Johannes

    2014-01-01

    We present a new feedback strategy implemented in a natural language generation-based e-learning system for German as a second language (L2). Although the system recognizes a large proportion of the grammar errors in learner-produced written sentences, its automatically generated feedback only addresses errors against rules that are relevant at…

  19. Laying the Groundwork: Lessons Learned from the Telecommunications Industry for Distributed Generation; Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Wise, A. L.

    2008-05-01

    The telecommunications industry went through growing pains in the past that hold some interesting lessons for the growing distributed generation (DG) industry. The technology shifts and stakeholders involved with the historic market transformation of the telecommunications sector mirror similar factors involved in distributed generation today. An examination of these factors may inform best practices when approaching the conduits necessary to accelerate the shifting of our nation's energy system to cleaner forms of generation and use. From a technical perspective, the telecom industry in the 1990s saw a shift from highly centralized systems that had no capacity for adaptation to highly adaptive, distributed network systems. From a management perspective, the industry shifted from small, private-company structures to big, capital-intensive corporations. This presentation will explore potential correlation and outline the lessons that we can take away from this comparison.

  20. Learning from examples - Generation and evaluation of decision trees for software resource analysis

    Science.gov (United States)

    Selby, Richard W.; Porter, Adam A.

    1988-01-01

    A general solution method for the automatic generation of decision (or classification) trees is investigated. The approach is to provide insights through in-depth empirical characterization and evaluation of decision trees for software resource data analysis. The trees identify classes of objects (software modules) that had high development effort. Sixteen software systems ranging from 3,000 to 112,000 source lines were selected for analysis from a NASA production environment. The collection and analysis of 74 attributes (or metrics), for over 4,700 objects, captured information about the development effort, faults, changes, design style, and implementation style. A total of 9,600 decision trees were automatically generated and evaluated. The trees correctly identified 79.3 percent of the software modules that had high development effort or faults, and the trees generated from the best parameter combinations correctly identified 88.4 percent of the modules on the average.

  1. Learning a generative model of images by factoring appearance and shape.

    Science.gov (United States)

    Le Roux, Nicolas; Heess, Nicolas; Shotton, Jamie; Winn, John

    2011-03-01

    Computer vision has grown tremendously in the past two decades. Despite all efforts, existing attempts at matching parts of the human visual system's extraordinary ability to understand visual scenes lack either scope or power. By combining the advantages of general low-level generative models and powerful layer-based and hierarchical models, this work aims at being a first step toward richer, more flexible models of images. After comparing various types of restricted Boltzmann machines (RBMs) able to model continuous-valued data, we introduce our basic model, the masked RBM, which explicitly models occlusion boundaries in image patches by factoring the appearance of any patch region from its shape. We then propose a generative model of larger images using a field of such RBMs. Finally, we discuss how masked RBMs could be stacked to form a deep model able to generate more complicated structures and suitable for various tasks such as segmentation or object recognition.

  2. Generating Multimedia Components for M-Learning%创建移动学习的多媒体组件

    Institute of Scientific and Technical Information of China (English)

    王颖

    2014-01-01

    该文提出了一个基于模板的生成多媒体组件的方案,适用于应用计算机或移动设备的教学。该研究领域结合了计算机科学、移动设备和电子学习的研究,被称为移动学习或者M-Learning。研究的目标是为处于任何地点的学习者提供获取电子学习资料的途径,让学习内容更适合移动设备的特点、交流环境、学习者的知识和喜好。该技术解决方案将充分发挥移动设备的潜力,广泛应用于教育领域。%The paper proposes a solution to generate template based multimedia components for instruction and learning available both for computer based applications and for mobile devices. The field of research is situated at the intersection of computer sci-ence, mobile tools and e-learning and is generically named mobile learning or M-learning. The research goal is to provide access to computer based training resources from any location and to adapt the training content to the specific features of mobile devic-es, communication environment, users’preferences and users’knowledge. To become important tools in education field, the technical solutions proposed will follow to use the potential of mobile devices.

  3. Generating a learning curve for pediatric caudal epidural blocks: an empirical evaluation of technical skills in novice and experienced anesthetists.

    Science.gov (United States)

    Schuepfer, G; Konrad, C; Schmeck, J; Poortmans, G; Staffelbach, B; Jöhr, M

    2000-01-01

    Learning curves for anesthesia procedures in adult patients have been determined, but no data are available on procedures in pediatric anesthesia. The aim of this study was to assess the number of caudal blocks needed to guarantee a high success rate in performing caudal epidural analgesia in children. At a teaching hospital, the technical skills of 7 residents in anesthesiology who performed caudal blocks were evaluated during 4 months using a standardized self-evaluation questionnaire. At the start of the study period, the residents had no prior experience in pediatric anesthesia or in performing caudal epidural blocks. All residents entered the pediatric rotation after a minimum of 1 year of training in adult general and regional anesthesia. The blocks were rated using a binary score. For comparison, the success rates of 8 experienced staff anesthesiologists were collected during the same period using the same self-evaluation questionnaire. Statistical analyses were performed by generating individual and institutional learning curves using the pooled data. The learning curves were calculated with the aid of a least-square fit model and 95% confidence intervals were estimated by a Monte Carlo procedure with a bootstrap technique. The success rate of residents was 80% after 32 procedures (95% confidence interval of 0.59 to 1.00). The pooled success rate of the staff anesthesiologists was 0.73 (mean) with a standard deviation of 0.45, which was not statistically different from the success rate of the residents. High success rates in performing caudal anesthesia in pediatric patients can be acquired after a limited number of cases. Success rates of residents learning this procedure are comparable to the results of staff anesthesiologists.

  4. Apollo CSM Power Generation System Design Considerations, Failure Modes and Lessons Learned

    Science.gov (United States)

    Interbartolo, Michael

    2009-01-01

    The objectives of this slide presentation are to: review the basic design criteria for fuel cells (FC's), review design considerations during developmental phase that affected Block I and Block II vehicles, summarize the conditions that led to the failure of components in the FC's, and state the solution implemented for each failure. It reviews the location of the fuel cells, the fuel cell theory the design criteria going into development phase and coming from the development phase, failures and solutions of Block I and II, and the lessons learned.

  5. Iterative learning control with applications in energy generation, lasers and health care

    Science.gov (United States)

    Tutty, O. R.

    2016-01-01

    Many physical systems make repeated executions of the same finite time duration task. One example is a robot in a factory or warehouse whose task is to collect an object in sequence from a location, transfer it over a finite duration, place it at a specified location or on a moving conveyor and then return for the next one and so on. Iterative learning control was especially developed for systems with this mode of operation and this paper gives an overview of this control design method using relatively recent relevant applications in wind turbines, free-electron lasers and health care, as exemplars to demonstrate its applicability. PMID:27713654

  6. Harnessing user generated multimedia content in the creation of collaborative classification structures and retrieval learning games

    Science.gov (United States)

    Borchert, Otto Jerome

    This paper describes a software tool to assist groups of people in the classification and identification of real world objects called the Classification, Identification, and Retrieval-based Collaborative Learning Environment (CIRCLE). A thorough literature review identified current pedagogical theories that were synthesized into a series of five tasks: gathering, elaboration, classification, identification, and reinforcement through game play. This approach is detailed as part of an included peer reviewed paper. Motivation is increased through the use of formative and summative gamification; getting points completing important portions of the tasks and playing retrieval learning based games, respectively, which is also included as a peer-reviewed conference proceedings paper. Collaboration is integrated into the experience through specific tasks and communication mediums. Implementation focused on a REST-based client-server architecture. The client is a series of web-based interfaces to complete each of the tasks, support formal classroom interaction through faculty accounts and student tracking, and a module for peers to help each other. The server, developed using an in-house JavaMOO platform, stores relevant project data and serves data through a series of messages implemented as a JavaScript Object Notation Application Programming Interface (JSON API). Through a series of two beta tests and two experiments, it was discovered the second, elaboration, task requires considerable support. While students were able to properly suggest experiments and make observations, the subtask involving cleaning the data for use in CIRCLE required extra support. When supplied with more structured data, students were enthusiastic about the classification and identification tasks, showing marked improvement in usability scores and in open ended survey responses. CIRCLE tracks a variety of educationally relevant variables, facilitating support for instructors and researchers. Future

  7. Iterative learning control with applications in energy generation, lasers and health care

    Science.gov (United States)

    Rogers, E.; Tutty, O. R.

    2016-09-01

    Many physical systems make repeated executions of the same finite time duration task. One example is a robot in a factory or warehouse whose task is to collect an object in sequence from a location, transfer it over a finite duration, place it at a specified location or on a moving conveyor and then return for the next one and so on. Iterative learning control was especially developed for systems with this mode of operation and this paper gives an overview of this control design method using relatively recent relevant applications in wind turbines, free-electron lasers and health care, as exemplars to demonstrate its applicability.

  8. KernelADASYN: Kernel Based Adaptive Synthetic Data Generation for Imbalanced Learning

    Science.gov (United States)

    2015-08-17

    classification methods have also been proposed recently [20][21][22][23][24]. To address the “ curse of dimensionality” in high-dimensional data spaces...Networks, pp. 1322–1328, 2008. [2] H. He and E. A. Garcia, “Learning from imbalanced data,” IEEE Transactions on Knowledge and Data Engineering, vol. 21, no... Knowledge Discovery in Databases: PKDD 2003, pp. 107–119, 2003. [10] M. Gao, X. Hong, S. Chen, C. J. Harris, and E. Khalaf, “PDFOS: PDF estimation based over

  9. Evaluation of Computer Tools for Idea Generation and Team Formation in Project-Based Learning

    Science.gov (United States)

    Ardaiz-Villanueva, Oscar; Nicuesa-Chacon, Xabier; Brene-Artazcoz, Oscar; Sanz de Acedo Lizarraga, Maria Luisa; Sanz de Acedo Baquedano, Maria Teresa

    2011-01-01

    The main objective of this research was to validate the effectiveness of Wikideas and Creativity Connector tools to stimulate the generation of ideas and originality by university students organized into groups according to their indexes of creativity and affinity. Another goal of the study was to evaluate the classroom climate created by these…

  10. Net Generation at Social Software: Challenging Assumptions, Clarifying Relationships and Raising Implications for Learning

    Science.gov (United States)

    Valtonen, Teemu; Dillon, Patrick; Hacklin, Stina; Vaisanen, Pertti

    2010-01-01

    This paper takes as its starting point assumptions about use of information and communication technology (ICT) by people born after 1983, the so called net generation. The focus of the paper is on social networking. A questionnaire survey was carried out with 1070 students from schools in Eastern Finland. Data are presented on students' ICT-skills…

  11. Endangered Academic Talent: Lessons Learned from Gifted First-Generation College Males.

    Science.gov (United States)

    Olenchak, F. Richard; Hebert, Thomas P.

    2002-01-01

    Two case studies of men from diverse cultures, African American and Vietnamese American, illustrate the potential for underachievement among first-generation gifted students at comprehensive universities. Amplifying previous studies, this research provides an examination of attrition and highlights influences on underachievement. Conclusions…

  12. Messaging, Gaming, Peer-to-Peer Sharing: Language Learning Strategies & Tools for the Millennial Generation

    Science.gov (United States)

    Godwin-Jones, Bob

    2005-01-01

    The next generation's enthusiasm for instant messaging, videogames, and peer-to-peer file swapping is likely to be dismissed by their elders as so many ways to waste time and avoid the real worlds of work or school. But these activities may not be quite as vapid as they may seem from the perspective of outsiders--or educators. Researchers point…

  13. Workplaces that Support High-Performing Teaching and Learning: Insights from Generation Y Teachers

    Science.gov (United States)

    Coggshall, Jane G.; Behrstock-Sherratt, Ellen; Drill, Karen

    2011-01-01

    Generation Y public school teachers--those born between 1977 and 1995-- who have been serving students for nearly a decade now, represent an increasingly large proportion of the teaching workforce, and, with concerted support, promise to help bring needed transformation to schools that too often remain stuck in an earlier age. Members of this…

  14. Internally generated sequences in learning and executing goal-directed behavior

    NARCIS (Netherlands)

    Pezzulo, G.; van der Meer, M.A.A.; Lansink, C.S.; Pennartz, C.M.A.

    2014-01-01

    A network of brain structures including hippocampus (HC), prefrontal cortex, and striatum controls goal-directed behavior and decision making. However, the neural mechanisms underlying these functions are unknown. Here, we review the role of 'internally generated sequences': structured, multi-neuron

  15. Crossing the Generations: Learning to Lead Across the Leadership Life Cycle

    Science.gov (United States)

    Phelan, Daniel J.

    2005-01-01

    To fully understand the continuing evolution of the community college movement, one must also consider the evolution of its leadership, its changing environment, as well as changing societal needs. This monograph provides some experiential insight into the leadership aspect of the community college, particularly with regard to generational change.…

  16. Adapting Training to Meet the Preferred Learning Styles of Different Generations

    Science.gov (United States)

    Urick, Michael

    2017-01-01

    This article considers how training professionals can respond to differences in training preferences between generational groups. It adopts two methods. First, it surveys the existing research and finds generally that preferences for training approaches can differ between groups and specifically that younger employees are perceived to leverage…

  17. Learning preferences for Referring Expression Generation: Effects of domain, language and algorithm

    NARCIS (Netherlands)

    Koolen, Ruud; Krahmer, Emiel; Theune, Mariët

    2012-01-01

    One important subtask of Referring Expression Generation (REG) algorithms is to select the attributes in a definite description for a given object. In this paper, we study how much training data is required for algorithms to do this properly. We compare two REG algorithms in terms of their performan

  18. A Bold Experiment: Teachers Team with Scientists to Learn Next Generation Science Standards

    Science.gov (United States)

    Gilman, Sharon L.; Fout, Martha C.

    2017-01-01

    The "Next Generation Science Standards" place an emphasis on the practices of science and engineering, where ensuring that students understand and experience how science works is as important as, or maybe more important than, memorizing facts. The idea is that, while some facts may change, the practices will always be applicable, and it…

  19. Machine Learning of Protein Interactions in Fungal Secretory Pathways

    Science.gov (United States)

    Kludas, Jana; Arvas, Mikko; Castillo, Sandra; Pakula, Tiina; Oja, Merja; Brouard, Céline; Jäntti, Jussi; Penttilä, Merja

    2016-01-01

    In this paper we apply machine learning methods for predicting protein interactions in fungal secretion pathways. We assume an inter-species transfer setting, where training data is obtained from a single species and the objective is to predict protein interactions in other, related species. In our methodology, we combine several state of the art machine learning approaches, namely, multiple kernel learning (MKL), pairwise kernels and kernelized structured output prediction in the supervised graph inference framework. For MKL, we apply recently proposed centered kernel alignment and p-norm path following approaches to integrate several feature sets describing the proteins, demonstrating improved performance. For graph inference, we apply input-output kernel regression (IOKR) in supervised and semi-supervised modes as well as output kernel trees (OK3). In our experiments simulating increasing genetic distance, Input-Output Kernel Regression proved to be the most robust prediction approach. We also show that the MKL approaches improve the predictions compared to uniform combination of the kernels. We evaluate the methods on the task of predicting protein-protein-interactions in the secretion pathways in fungi, S.cerevisiae, baker’s yeast, being the source, T. reesei being the target of the inter-species transfer learning. We identify completely novel candidate secretion proteins conserved in filamentous fungi. These proteins could contribute to their unique secretion capabilities. PMID:27441920

  20. Machine Learning of Protein Interactions in Fungal Secretory Pathways.

    Directory of Open Access Journals (Sweden)

    Jana Kludas

    Full Text Available In this paper we apply machine learning methods for predicting protein interactions in fungal secretion pathways. We assume an inter-species transfer setting, where training data is obtained from a single species and the objective is to predict protein interactions in other, related species. In our methodology, we combine several state of the art machine learning approaches, namely, multiple kernel learning (MKL, pairwise kernels and kernelized structured output prediction in the supervised graph inference framework. For MKL, we apply recently proposed centered kernel alignment and p-norm path following approaches to integrate several feature sets describing the proteins, demonstrating improved performance. For graph inference, we apply input-output kernel regression (IOKR in supervised and semi-supervised modes as well as output kernel trees (OK3. In our experiments simulating increasing genetic distance, Input-Output Kernel Regression proved to be the most robust prediction approach. We also show that the MKL approaches improve the predictions compared to uniform combination of the kernels. We evaluate the methods on the task of predicting protein-protein-interactions in the secretion pathways in fungi, S.cerevisiae, baker's yeast, being the source, T. reesei being the target of the inter-species transfer learning. We identify completely novel candidate secretion proteins conserved in filamentous fungi. These proteins could contribute to their unique secretion capabilities.

  1. Participatory learning and knowledge assessment with a game-based method relying on student-generated questions

    CERN Document Server

    Abad, Enrique; Gil, Julia

    2015-01-01

    A game based on student-generated multiple-choice questions (MCQs) was used to promote participatory learning and as a knowledge assessment tool in the framework of an elementary course in Photonics. Under the instructor's guidance, students were asked to author MCQs, including both question stems and four possible answers (three distractors and one correct answer). They were told that good enough questions would enter a repository from which MCQs for the final exam could be drawn. The student-generated MCQs were then reviewed by the instructor, who discarded unsuitable questions and made amendments to ensure quality standards. The resulting repository (with the key to the correct answers) was made available to the students, whereupon a subset of questions were selected by the instructor to set the MCQ test for the final exam (consisting of a MCQ test based on student-generated questions and a problem-solving part set entirely by the instructor). The MCQ repository was large enough to ensure that rote learnin...

  2. Wind Turbine Driving a PM Synchronous Generator Using Novel Recurrent Chebyshev Neural Network Control with the Ideal Learning Rate

    Directory of Open Access Journals (Sweden)

    Chih-Hong Lin

    2016-06-01

    Full Text Available A permanent magnet (PM synchronous generator system driven by wind turbine (WT, connected with smart grid via AC-DC converter and DC-AC converter, are controlled by the novel recurrent Chebyshev neural network (NN and amended particle swarm optimization (PSO to regulate output power and output voltage in two power converters in this study. Because a PM synchronous generator system driven by WT is an unknown non-linear and time-varying dynamic system, the on-line training novel recurrent Chebyshev NN control system is developed to regulate DC voltage of the AC-DC converter and AC voltage of the DC-AC converter connected with smart grid. Furthermore, the variable learning rate of the novel recurrent Chebyshev NN is regulated according to discrete-type Lyapunov function for improving the control performance and enhancing convergent speed. Finally, some experimental results are shown to verify the effectiveness of the proposed control method for a WT driving a PM synchronous generator system in smart grid.

  3. Eddy Current Signature Classification of Steam Generator Tube Defects Using A Learning Vector Quantization Neural Network

    Energy Technology Data Exchange (ETDEWEB)

    Gabe V. Garcia

    2005-01-03

    A major cause of failure in nuclear steam generators is degradation of their tubes. Although seven primary defect categories exist, one of the principal causes of tube failure is intergranular attack/stress corrosion cracking (IGA/SCC). This type of defect usually begins on the secondary side surface of the tubes and propagates both inwards and laterally. In many cases this defect is found at or near the tube support plates.

  4. GUDM: Automatic Generation of Unified Datasets for Learning and Reasoning in Healthcare.

    Science.gov (United States)

    Ali, Rahman; Siddiqi, Muhammad Hameed; Idris, Muhammad; Ali, Taqdir; Hussain, Shujaat; Huh, Eui-Nam; Kang, Byeong Ho; Lee, Sungyoung

    2015-07-02

    A wide array of biomedical data are generated and made available to healthcare experts. However, due to the diverse nature of data, it is difficult to predict outcomes from it. It is therefore necessary to combine these diverse data sources into a single unified dataset. This paper proposes a global unified data model (GUDM) to provide a global unified data structure for all data sources and generate a unified dataset by a "data modeler" tool. The proposed tool implements user-centric priority based approach which can easily resolve the problems of unified data modeling and overlapping attributes across multiple datasets. The tool is illustrated using sample diabetes mellitus data. The diverse data sources to generate the unified dataset for diabetes mellitus include clinical trial information, a social media interaction dataset and physical activity data collected using different sensors. To realize the significance of the unified dataset, we adopted a well-known rough set theory based rules creation process to create rules from the unified dataset. The evaluation of the tool on six different sets of locally created diverse datasets shows that the tool, on average, reduces 94.1% time efforts of the experts and knowledge engineer while creating unified datasets.

  5. GUDM: Automatic Generation of Unified Datasets for Learning and Reasoning in Healthcare

    Directory of Open Access Journals (Sweden)

    Rahman Ali

    2015-07-01

    Full Text Available A wide array of biomedical data are generated and made available to healthcare experts. However, due to the diverse nature of data, it is difficult to predict outcomes from it. It is therefore necessary to combine these diverse data sources into a single unified dataset. This paper proposes a global unified data model (GUDM to provide a global unified data structure for all data sources and generate a unified dataset by a “data modeler” tool. The proposed tool implements user-centric priority based approach which can easily resolve the problems of unified data modeling and overlapping attributes across multiple datasets. The tool is illustrated using sample diabetes mellitus data. The diverse data sources to generate the unified dataset for diabetes mellitus include clinical trial information, a social media interaction dataset and physical activity data collected using different sensors. To realize the significance of the unified dataset, we adopted a well-known rough set theory based rules creation process to create rules from the unified dataset. The evaluation of the tool on six different sets of locally created diverse datasets shows that the tool, on average, reduces 94.1% time efforts of the experts and knowledge engineer while creating unified datasets.

  6. Semi-Supervised Bayesian Classification of Materials with Impact-Echo Signals

    Directory of Open Access Journals (Sweden)

    Jorge Igual

    2015-05-01

    Full Text Available The detection and identification of internal defects in a material require the use of some technology that translates the hidden interior damages into observable signals with different signature-defect correspondences. We apply impact-echo techniques for this purpose. The materials are classified according to their defective status (homogeneous, one defect or multiple defects and kind of defect (hole or crack, passing through or not. Every specimen is impacted by a hammer, and the spectrum of the propagated wave is recorded. This spectrum is the input data to a Bayesian classifier that is based on the modeling of the conditional probabilities with a mixture of Gaussians. The parameters of the Gaussian mixtures and the class probabilities are estimated using an extended expectation-maximization algorithm. The advantage of our proposal is that it is flexible, since it obtains good results for a wide range of models even under little supervision; e.g., it obtains a harmonic average of precision and recall value of 92.38% given only a 10% supervision ratio. We test the method with real specimens made of aluminum alloy. The results show that the algorithm works very well. This technique could be applied in many industrial problems, such as the optimization of the marble cutting process.

  7. Semi-supervised Bayesian classification of materials with impact-echo signals.

    Science.gov (United States)

    Igual, Jorge; Salazar, Addisson; Safont, Gonzalo; Vergara, Luis

    2015-05-19

    The detection and identification of internal defects in a material require the use of some technology that translates the hidden interior damages into observable signals with different signature-defect correspondences. We apply impact-echo techniques for this purpose. The materials are classified according to their defective status (homogeneous, one defect or multiple defects) and kind of defect (hole or crack, passing through or not). Every specimen is impacted by a hammer, and the spectrum of the propagated wave is recorded. This spectrum is the input data to a Bayesian classifier that is based on the modeling of the conditional probabilities with a mixture of Gaussians. The parameters of the Gaussian mixtures and the class probabilities are estimated using an extended expectation-maximization algorithm. The advantage of our proposal is that it is flexible, since it obtains good results for a wide range of models even under little supervision; e.g., it obtains a harmonic average of precision and recall value of 92.38% given only a 10% supervision ratio. We test the method with real specimens made of aluminum alloy. The results show that the algorithm works very well. This technique could be applied in many industrial problems, such as the optimization of the marble cutting process.

  8. Semi-supervised word polarity identification in resource-lean languages.

    Science.gov (United States)

    Dehdarbehbahani, Iman; Shakery, Azadeh; Faili, Heshaam

    2014-10-01

    Sentiment words, as fundamental constitutive parts of subjective sentences, have a substantial effect on analysis of opinions, emotions and beliefs. Most of the proposed methods for identifying the semantic orientations of words exploit rich linguistic resources such as WordNet, subjectivity corpora, or polarity tagged words. Shortage of such linguistic resources in resource-lean languages affects the performance of word polarity identification in these languages. In this paper, we present a method which exploits a language with rich subjectivity analysis resources (English) to identify the polarity of words in a resource-lean foreign language. The English WordNet and a sparse foreign WordNet infrastructure are used to create a heterogeneous, multilingual and weighted semantic network. To identify the semantic orientation of foreign words, a random walk based method is applied to the semantic network along with a set of automatically weighted English positive and negative seeds. In a post-processing phase, synonym and antonym relations in the foreign WordNet are used to filter the random walk results. Our experiments on English and Persian languages show that the proposed method can outperform state-of-the-art word polarity identification methods in both languages.

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

    OpenAIRE

    Perumal, Krish

    2016-01-01

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

  10. Learning progressions from a sociocultural perspective: response to "co-constructing cultural landscapes for disciplinary learning in and out of school: the next generation science standards and learning progressions in action"

    Science.gov (United States)

    Tytler, Russell

    2016-10-01

    This article discusses a case for a different, socio-cultural way of looking at learning progressions as treated in the next generation science standards (NGSS) as described by Ralph Cordova and Phyllis Balcerzak's paper "Co-constructing cultural landscapes for disciplinary learning in and out of school: the next generation science standards and learning progressions in action". The paper is interesting for a number of reasons, and in this response I will identify different aspects of the paper and link the points made to my own research, and that of colleagues, as complementary perspectives. First, the way that the science curriculum is conceived as an expanding experience that moves from the classroom into the community, across subjects, and across time, links to theoretical positions on disciplinary literacies and notions of learning as apprenticeship into the discursive tools, or `habits of mind' as the authors put it, that underpin disciplinary practice. Second, the formulation of progression through widening communities of practice is a strong feature of the paper, and shows how children take on the role of scientists through this expanding exposure. I will link this approach to some of our own work with school—community science partnerships, drawing on the construct of boundary crossing to tease out relations between school science and professional practice. Third, the demonstration of the expansion of the children's view of what scientists do is well documented in the paper, illustrated by Figure 13 for instance. However I will, in this response, try to draw out and respond to what the paper is saying about the nature of progression; what the progression consists of, over what temporal or spatial dimensions it progresses, and how it can productively frame curriculum processes.

  11. Generating workplace accommodations: lessons learned from the integrated case management study.

    Science.gov (United States)

    Shaw, William S; Feuerstein, Michael

    2004-09-01

    Modified duty and other accommodations by employers have been shown to be helpful in managing workplace disability associated with injuries and illnesses. Benefits of accommodation have been attributed to both reduced physical and psychosocial exposures. Although many employers have adopted proactive return to work policies that emphasize temporary work modifications, standardized methods for specifying appropriate accommodations have been elusive. On the basis of the experiences and results of a randomized controlled study of case management services for work-related upper extremity disorders, we describe issues pertaining to the application of self-report measures of function and exposure assessment for generating accommodations. Challenges of this approach are 1) including specific work tasks on measures of physical function; 2) improving concordance between ergonomic exposure categories and methods of accommodation; and 3) providing a structured process for negotiating employee and employer preferences. To improve the effectiveness and efficiency of accommodation efforts, new tools for assessing function and ergonomic exposures in the workplace should be developed to specify accommodations more directly.

  12. Automatic generation control of multi-area power systems with diverse energy sources using Teaching Learning Based Optimization algorithm

    Directory of Open Access Journals (Sweden)

    Rabindra Kumar Sahu

    2016-03-01

    Full Text Available This paper presents the design and analysis of Proportional-Integral-Double Derivative (PIDD controller for Automatic Generation Control (AGC of multi-area power systems with diverse energy sources using Teaching Learning Based Optimization (TLBO algorithm. At first, a two-area reheat thermal power system with appropriate Generation Rate Constraint (GRC is considered. The design problem is formulated as an optimization problem and TLBO is employed to optimize the parameters of the PIDD controller. The superiority of the proposed TLBO based PIDD controller has been demonstrated by comparing the results with recently published optimization technique such as hybrid Firefly Algorithm and Pattern Search (hFA-PS, Firefly Algorithm (FA, Bacteria Foraging Optimization Algorithm (BFOA, Genetic Algorithm (GA and conventional Ziegler Nichols (ZN for the same interconnected power system. Also, the proposed approach has been extended to two-area power system with diverse sources of generation like thermal, hydro, wind and diesel units. The system model includes boiler dynamics, GRC and Governor Dead Band (GDB non-linearity. It is observed from simulation results that the performance of the proposed approach provides better dynamic responses by comparing the results with recently published in the literature. Further, the study is extended to a three unequal-area thermal power system with different controllers in each area and the results are compared with published FA optimized PID controller for the same system under study. Finally, sensitivity analysis is performed by varying the system parameters and operating load conditions in the range of ±25% from their nominal values to test the robustness.

  13. Engineering Mechanical Gradients in Next Generation Biomaterials - Lessons Learned from Medical Textile Design.

    Science.gov (United States)

    Ng, Joanna L; Collins, Ciara E; Knothe Tate, Melissa L

    2017-03-05

    Nonwoven and textile membranes have been applied both externally and internally to prescribe boundary conditions for medical conditions as diverse as oedema and tissue defects. Incorporation of mechanical gradients in next generation medical membrane design offers great potential to enhance function in a dynamic, physiological context. Yet the gradient properties and resulting mechanical performance of current membranes are not well described. To bridge this knowledge gap, we tested and compared the mechanical properties of bounding membranes used in both external (compression sleeves for oedema, exercise bands) and internal (surgical membranes) physiological contexts. We showed that anisotropic compression garment textiles, isotropic exercise bands and surgical membranes exhibit similar ranges of resistance to tension under physiologic strains. However, their mechanical gradients and resulting stress-strain relationships show differences in work capacity and energy expenditure. Exercise bands' moduli of elasticity and respective thicknesses allow for controlled, incremental increases in loading to facilitate healing as injured tissues return to normal structure and function. In contrast, the gradients intrinsic to compression sleeve design exhibit gaps in the middle range of physiological strains and also inconsistencies along the length of the sleeve, resulting in less than optimal performance of these devices. These current shortcomings in compression textile and garment design may be addressed in the future through implementation of novel approaches. For example, patterns, fibre compositions, and fibre anisotropy can be incorporated into biomaterial design to achieve seamless mechanical gradients in structure and resulting dynamic function, which would be particularly useful in physiological contexts. These concepts can be applied further to biomaterial design to deliver pressure gradients during movement of oedematous limbs (compression garments) and

  14. Disturbance of endogenous hydrogen sulfide generation and endoplasmic reticulum stress in hippocampus are involved in homocysteine-induced defect in learning and memory of rats.

    Science.gov (United States)

    Li, Man-Hong; Tang, Ji-Ping; Zhang, Ping; Li, Xiang; Wang, Chun-Yan; Wei, Hai-Jun; Yang, Xue-Feng; Zou, Wei; Tang, Xiao-Qing

    2014-04-01

    Homocysteine (Hcy) is a risk factor for Alzheimer's disease (AD). Hydrogen sulfide (H2S) acts as an endogenous neuromodulator and neuroprotectant. It has been shown that endoplasmic reticulum (ER) stress is involved in the pathological mechanisms of the learning and memory dysfunctions and that H2S exerts its neuroprotective role via suppressing ER stress. In the present work, we explored the effects of intracerebroventricular injection of Hcy on the formation of learning and memory, the generation of endogenous H2S, and the expression of ER stress in the hippocampus of rats. We found that intracerebroventricular injection of Hcy in rats leads to learning and memory dysfunctions in the Morris water maze and novel of object recognition test and decreases in the expression of cystathionine-β-synthase, the major enzyme responsible for endogenous H2S generation, and the generation of endogenous H2S in the hippocampus of rats. We also showed that exposure of Hcy could up-regulate the expressions of glucose-regulated protein 78 (GRP78), CHOP, and cleaved caspase-12, which are the major mark proteins of ER stress, in the hippocampus of rats. Taken together, these results suggest that the disturbance of hippocampal endogenous H2S generation and the increase in ER stress in the hippocampus are related to Hcy-induced defect in learning and memory. Copyright © 2014 Elsevier B.V. All rights reserved.

  15. Improving Mathematics Learning by Integrating Curricular Activities with Innovative and Developmentally Appropriate Digital Apps: Findings from the Next Generation Preschool Math Evaluation

    Science.gov (United States)

    Presser, Ashley Lewis; Vahey, Philip; Dominguez, Ximena

    2015-01-01

    This paper describes findings from a blocked randomized design (BRD) field study conducted to examine the "Next Generation Preschool Math" (NGPM) program's implementation in preschool classrooms and promise in improving young children's mathematic learning. NGPM integrates traditional preschool activities with developmentally appropriate…

  16. User-Generated Content, YouTube and Participatory Culture on the Web: Music Learning and Teaching in Two Contrasting Online Communities

    Science.gov (United States)

    Waldron, Janice

    2013-01-01

    In this paper, I draw on seminal literature from new media researchers to frame the broader implications that user-generated content (UGC), YouTube, and participatory culture have for music learning and teaching in online communities; to illustrate, I use examples from two contrasting online music communities, the Online Academy of Irish…

  17. 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.

  18. Distributed Generation Planning using Peer Enhanced Multi-objective Teaching-Learning based Optimization in Distribution Networks

    Science.gov (United States)

    Selvam, Kayalvizhi; Vinod Kumar, D. M.; Siripuram, Ramakanth

    2017-04-01

    In this paper, an optimization technique called peer enhanced teaching learning based optimization (PeTLBO) algorithm is used in multi-objective problem domain. The PeTLBO algorithm is parameter less so it reduced the computational burden. The proposed peer enhanced multi-objective based TLBO (PeMOTLBO) algorithm has been utilized to find a set of non-dominated optimal solutions [distributed generation (DG) location and sizing in distribution network]. The objectives considered are: real power loss and the voltage deviation subjected to voltage limits and maximum penetration level of DG in distribution network. Since the DG considered is capable of injecting real and reactive power to the distribution network the power factor is considered as 0.85 lead. The proposed peer enhanced multi-objective optimization technique provides different trade-off solutions in order to find the best compromise solution a fuzzy set theory approach has been used. The effectiveness of this proposed PeMOTLBO is tested on IEEE 33-bus and Indian 85-bus distribution system. The performance is validated with Pareto fronts and two performance metrics (C-metric and S-metric) by comparing with robust multi-objective technique called non-dominated sorting genetic algorithm-II and also with the basic TLBO.

  19. Distributed Generation Planning using Peer Enhanced Multi-objective Teaching-Learning based Optimization in Distribution Networks

    Science.gov (United States)

    Selvam, Kayalvizhi; Vinod Kumar, D. M.; Siripuram, Ramakanth

    2016-06-01

    In this paper, an optimization technique called peer enhanced teaching learning based optimization (PeTLBO) algorithm is used in multi-objective problem domain. The PeTLBO algorithm is parameter less so it reduced the computational burden. The proposed peer enhanced multi-objective based TLBO (PeMOTLBO) algorithm has been utilized to find a set of non-dominated optimal solutions [distributed generation (DG) location and sizing in distribution network]. The objectives considered are: real power loss and the voltage deviation subjected to voltage limits and maximum penetration level of DG in distribution network. Since the DG considered is capable of injecting real and reactive power to the distribution network the power factor is considered as 0.85 lead. The proposed peer enhanced multi-objective optimization technique provides different trade-off solutions in order to find the best compromise solution a fuzzy set theory approach has been used. The effectiveness of this proposed PeMOTLBO is tested on IEEE 33-bus and Indian 85-bus distribution system. The performance is validated with Pareto fronts and two performance metrics (C-metric and S-metric) by comparing with robust multi-objective technique called non-dominated sorting genetic algorithm-II and also with the basic TLBO.

  20. How the Young Generation Uses Digital Textbooks via Mobile Learning Terminals: Measurement of Elementary School Students in China

    Science.gov (United States)

    Sun, Zhong; Jiang, Yuzhen

    2015-01-01

    Digital textbooks that offer multimedia features, interactive controls, e-annotation and learning process tracking are gaining increasing attention in today's mobile learning era, particularly with the rapid development of mobile learning terminals such as Apple's iPad series and Android-based models. Accordingly, this study explores how…