WorldWideScience

Sample records for supervised learning problems

  1. Using Supervised Deep Learning for Human Age Estimation Problem

    Science.gov (United States)

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

    2017-05-01

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

  2. Inductive Supervised Quantum Learning

    Science.gov (United States)

    Monràs, Alex; Sentís, Gael; Wittek, Peter

    2017-05-01

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

  3. Supervised Learning in Multilayer Spiking Neural Networks

    CERN Document Server

    Sporea, Ioana

    2012-01-01

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

  4. Multi-Instance Learning from Supervised View

    Institute of Scientific and Technical Information of China (English)

    Zhi-Hua Zhou

    2006-01-01

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

  5. Learning Dynamics in Doctoral Supervision

    DEFF Research Database (Denmark)

    Kobayashi, Sofie

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

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

    Science.gov (United States)

    Carter, Susan

    2016-01-01

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

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

    Science.gov (United States)

    Carter, Susan

    2016-01-01

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

  8. Balancing Design Project Supervision and Learning Facilitation

    DEFF Research Database (Denmark)

    Nielsen, Louise Møller

    2012-01-01

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

  9. A new supervised learning algorithm for spiking neurons.

    Science.gov (United States)

    Xu, Yan; Zeng, Xiaoqin; Zhong, Shuiming

    2013-06-01

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

  10. Supervised Dictionary Learning

    CERN Document Server

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

    2008-01-01

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

  11. Balancing Design Project Supervision and Learning Facilitation

    DEFF Research Database (Denmark)

    Nielsen, Louise Møller

    2012-01-01

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

  12. 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

  13. Supervised Dictionary Learning

    Science.gov (United States)

    2008-11-01

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

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

    OpenAIRE

    Wang, DeLiang; Chen, Jitong

    2017-01-01

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

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

    Institute of Scientific and Technical Information of China (English)

    蒋艳凰; 周海芳; 杨学军

    2003-01-01

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

  16. Learning Problems

    Science.gov (United States)

    ... de los dientes Video: Getting an X-ray Learning Problems KidsHealth > For Kids > Learning Problems Print A ... for how to make it better. What Are Learning Disabilities? Learning disabilities aren't contagious, but they ...

  17. 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...

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

    Directory of Open Access Journals (Sweden)

    R. Sathya

    2013-02-01

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

  19. Incremental Supervised Subspace Learning for Face Recognition

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

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

  20. Learning Dynamics in Doctoral Supervision

    DEFF Research Database (Denmark)

    Kobayashi, Sofie

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

  1. Semi-supervised Eigenvectors for Locally-biased Learning

    DEFF Research Database (Denmark)

    Hansen, Toke Jansen; Mahoney, Michael W.

    2012-01-01

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

  2. An AdaBoost algorithm for multiclass semi-supervised learning

    NARCIS (Netherlands)

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

    2012-01-01

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

  3. Action learning in undergraduate engineering thesis supervision

    Directory of Open Access Journals (Sweden)

    Brad Stappenbelt

    2017-03-01

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

  4. Equality of Opportunity in Supervised Learning

    OpenAIRE

    Hardt, Moritz; Price, Eric; Srebro, Nathan

    2016-01-01

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

  5. Missing Data Imputation for Supervised Learning

    OpenAIRE

    Poulos, Jason; Valle, Rafael

    2016-01-01

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

  6. The Supervised Learning Gaussian Mixture Model

    Institute of Scientific and Technical Information of China (English)

    马继涌; 高文

    1998-01-01

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

  7. Opportunities to Learn Scientific Thinking in Joint Doctoral Supervision

    Science.gov (United States)

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

    2015-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Kishor Kumar Reddy C

    2014-10-01

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

  9. 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

  10. 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.

  11. Genetic classification of populations using supervised learning.

    LENUS (Irish Health Repository)

    Bridges, Michael

    2011-01-01

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

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

    Science.gov (United States)

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

    2017-09-01

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

  13. 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.

  14. Generalization of Supervised Learning for Binary Mask Estimation

    DEFF Research Database (Denmark)

    May, Tobias; Gerkmann, Timo

    2014-01-01

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

  15. The Learning Alliance: Ethics in Doctoral Supervision

    Science.gov (United States)

    Halse, Christine; Bansel, Peter

    2012-01-01

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

  16. Function approximation using combined unsupervised and supervised learning.

    Science.gov (United States)

    Andras, Peter

    2014-03-01

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

  17. Integrating the Supervised Information into Unsupervised Learning

    Directory of Open Access Journals (Sweden)

    Ping Ling

    2013-01-01

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

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

    Science.gov (United States)

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

    2014-12-01

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

  19. Robust head pose estimation via supervised manifold learning.

    Science.gov (United States)

    Wang, Chao; Song, Xubo

    2014-05-01

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

  20. Opportunities to learn scientific thinking in joint doctoral supervision

    DEFF Research Database (Denmark)

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

    2015-01-01

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

  1. Subsampled Hessian Newton Methods for Supervised Learning.

    Science.gov (United States)

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

    2015-08-01

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

  2. 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.

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

    Directory of Open Access Journals (Sweden)

    Ardjan Zwartjes

    2016-10-01

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

  4. A New Method for Solving Supervised Data Classification Problems

    Directory of Open Access Journals (Sweden)

    Parvaneh Shabanzadeh

    2014-01-01

    Full Text Available Supervised data classification is one of the techniques used to extract nontrivial information from data. Classification is a widely used technique in various fields, including data mining, industry, medicine, science, and law. This paper considers a new algorithm for supervised data classification problems associated with the cluster analysis. The mathematical formulations for this algorithm are based on nonsmooth, nonconvex optimization. A new algorithm for solving this optimization problem is utilized. The new algorithm uses a derivative-free technique, with robustness and efficiency. To improve classification performance and efficiency in generating classification model, a new feature selection algorithm based on techniques of convex programming is suggested. Proposed methods are tested on real-world datasets. Results of numerical experiments have been presented which demonstrate the effectiveness of the proposed algorithms.

  5. 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.

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

    CERN Document Server

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

    2016-01-01

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

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

    Science.gov (United States)

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

    2016-06-08

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

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

    Science.gov (United States)

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

    2007-03-01

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

  9. 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.

  10. Enhancing Adult Learning in Clinical Supervision

    Science.gov (United States)

    Goldman, Stuart

    2011-01-01

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

  11. Supervised Filter Learning for Representation Based Face Recognition.

    Directory of Open Access Journals (Sweden)

    Chao Bi

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

  12. Action Learning in Undergraduate Engineering Thesis Supervision

    Science.gov (United States)

    Stappenbelt, Brad

    2017-01-01

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

  13. 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...

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

    DEFF Research Database (Denmark)

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

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

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

    Science.gov (United States)

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

    2015-01-01

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

  16. 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

  17. 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...

  18. 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...

  19. Transfer learning improves supervised image segmentation across imaging protocols

    DEFF Research Database (Denmark)

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

    2015-01-01

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

  20. Combining Unsupervised and Supervised Learning for Discovering Disease Subclasses

    OpenAIRE

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

    2016-01-01

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

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

    Science.gov (United States)

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

    2017-04-01

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

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

    Science.gov (United States)

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

    2015-08-13

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

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

    Science.gov (United States)

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

    2015-04-01

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

  4. Collaborative Supervised Learning for Sensor Networks

    Science.gov (United States)

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

    2011-01-01

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

  5. 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.

  6. 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

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

    Science.gov (United States)

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

    2015-08-01

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

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

    DEFF Research Database (Denmark)

    Lund, Birthe; Jensen, Annie Aarup

    2009-01-01

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

  9. 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.

  10. Modeling Time Series Data for Supervised Learning

    Science.gov (United States)

    Baydogan, Mustafa Gokce

    2012-01-01

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

  11. Biomedical data analysis by supervised manifold learning.

    Science.gov (United States)

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

    2012-01-01

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

  12. Supervised Learning with Complex-valued Neural Networks

    CERN Document Server

    Suresh, Sundaram; Savitha, Ramasamy

    2013-01-01

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

  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. Very Short Literature Survey From Supervised Learning To Surrogate Modeling

    CERN Document Server

    Brusan, Altay

    2012-01-01

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

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

    DEFF Research Database (Denmark)

    Lund, Birthe; Jensen, Annie Aarup

    2009-01-01

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

  16. Pulsar Search Using Supervised Machine Learning

    Science.gov (United States)

    Ford, John M.

    2017-05-01

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

  17. Problems of Rural Food Safety and Strategies of Constructing Supervision System

    Institute of Scientific and Technical Information of China (English)

    2011-01-01

    This paper expounds the practical necessity of constructing diversified rural food safety supervision system as follows: it is the necessary requirements of guaranteeing people’s health and life safety; it is an important component of governmental function of social management and the logical extension of administrative responsibilities; it is the basis of maintaining order of rural society and constructing harmonious society. The main problems existing in the supervision of rural food safety are analyzed as follows: first, the legislative work of rural food safety lags behind to some extent; second, the supervision of governmental departments on rural food safety is insufficient; third, the industrial supervision mechanism of rural food security is not perfect; fourth, the role of rural social organizations in supervising food safety is limited; fifth, the farmers’ awareness of food safety supervision is not strong. Based on these problems, the targeted strategies of constructing diversified rural food safety supervision system are put forward as follows: accelerate the legislation of rural food safety, and ensure that there are laws to go by; give play to the dominant role of government, and strengthen administrative supervision on rural food safety; perfect industrial convention of rural food safety, and improve industrial supervision mechanism; actively support the fostering of social organizations, and give play to the role of supervision of organizations; cultivate correct concept of rights and obligations of farmers, and form awareness of food safety supervision.

  18. 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...

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

    Science.gov (United States)

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

    2015-06-01

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

  20. The Wicked Problem of the Intersection between Supervision and Evaluation

    Directory of Open Access Journals (Sweden)

    Ian M. METTE

    2017-03-01

    Full Text Available The purpose of this research was to explore how principals in eight high-functioning elementary schools in one American school district balanced teacher supervision and evaluation in their role as an instructional leader. Using the theoretical framework of ‘wicked problems’, to unpack the circular used to problematize teacher supervision and evaluation, the findings analyse how elementary principals in these eight buildings acknowledge the tensions and conflicts between supervision and evaluation, specifically as they relate to improving teacher instruction. Specifically, the results of this study highlight not only the differences between supervision and evaluation, but also the intersection between the two functions, as well as how high-performing elementary school principals serve as an instructional coach rather than a manager of teachers. While the two functions of supervision and evaluation are inherently different, it is the acknowledgement of the intersection between the two functions that can allow building principals to progress as instructional coaches who can better develop human resources and create higher-functioning school systems. Overall, this study points toward the importance of elementary principals having the instructional leadership skills to differentiate supervision and professional development need for teachers, which in turn influences the evaluation of a teacher is in her/his respective career.

  1. 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.

  2. Problem Based Learning

    Directory of Open Access Journals (Sweden)

    Paola Cappola

    2014-01-01

    Full Text Available In this current work, I am proposing a general close examination of Problem Based Learning as a student centered educational method in which a problem constitutes  the starting point of the learning process. Such a method provides students with the suitable knowledge for problem solving and presents numerous and significant differences compared to traditional education.In particular, I analyze the theoretic aspects of problem learning by tracing a history and presenting its structure, clarifying the role of the tutor in the various phases of the learning process. The method has found a wide diffusion since the beginning of the 70s and numerous studies have confirmed the advantages. The effectiveness of PBL is construable and is based on principles of constructivism and cognitivism.

  3. 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.

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

    DEFF Research Database (Denmark)

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

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

  5. Supervision in School Psychology: The Developmental/Ecological/Problem-Solving Model

    Science.gov (United States)

    Simon, Dennis J.; Cruise, Tracy K.; Huber, Brenda J.; Swerdlik, Mark E.; Newman, Daniel S.

    2014-01-01

    Effective supervision models guide the supervisory relationship and supervisory tasks leading to reflective and purposeful practice. The Developmental/Ecological/Problem-Solving (DEP) Model provides a contemporary framework for supervision specific to school psychology. Designed for the school psychology internship, the DEP Model is also…

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

    National Research Council Canada - National Science Library

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

    2016-01-01

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

  7. Essential processes for cognitive behavioral clinical supervision: Agenda setting, problem-solving, and formative feedback.

    Science.gov (United States)

    Cummings, Jorden A; Ballantyne, Elena C; Scallion, Laura M

    2015-06-01

    Clinical supervision should be a proactive and considered endeavor, not a reactive one. To that end, supervisors should choose supervision processes that are driven by theory, best available research, and clinical experience. These processes should be aimed at helping trainees develop as clinicians. We highlight 3 supervision processes we believe should be used at each supervision meeting: agenda setting, encouraging trainee problem-solving, and formative feedback. Although these are primarily cognitive-behavioral skills, they can be helpful in combination with other supervision models. We provide example dialogue from supervision exchanges, and discuss theoretical and research support for these processes. Using these processes not only encourages trainee development but also models for them how to use the same processes and approaches with clients.

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

    Directory of Open Access Journals (Sweden)

    T. Hamsapriya

    2011-12-01

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

  9. Problem Based Learning

    DEFF Research Database (Denmark)

    de Graaff, Erik; Guerra, Aida

    , the key principles remain the same everywhere. Graaff & Kolmos (2003) identify the main PBL principles as follows: 1. Problem orientation 2. Project organization through teams or group work 3. Participant-directed 4. Experiental learning 5. Activity-based learning 6. Interdisciplinary learning and 7......Problem-Based Learning (PBL) is an innovative method to organize the learning process in such a way that the students actively engage in finding answers by themselves. During the past 40 years PBL has evolved and diversified resulting in a multitude in variations in models and practices. However....... Exemplary practice. The University of Aalborg in Denmark started with PBL right from the start when the school was founded merging several educational institutes in Northern Denmark in 1974. The Aalborg PBL model is recognized around the world as an example or a source of inspiration, in particular...

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

    Directory of Open Access Journals (Sweden)

    Gao Chi

    2013-07-01

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

  11. SUPERVISED LEARNING METHODS FOR BANGLA WEB DOCUMENT CATEGORIZATION

    Directory of Open Access Journals (Sweden)

    Ashis Kumar Mandal

    2014-09-01

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

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

    Science.gov (United States)

    Yuan, Junsong; Wu, Ying

    2012-04-01

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

  13. 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.

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

    Science.gov (United States)

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

    2011-01-01

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

  15. Phenotype classification of zebrafish embryos by supervised learning.

    Directory of Open Access Journals (Sweden)

    Nathalie Jeanray

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    1993-07-01

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

  17. Using Supervised Learning to Improve Monte Carlo Integral Estimation

    CERN Document Server

    Tracey, Brendan; Alonso, Juan J

    2011-01-01

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

  18. Phenotype classification of zebrafish embryos by supervised learning.

    Science.gov (United States)

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

    2015-01-01

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

  19. Supervised dictionary learning for inferring concurrent brain networks.

    Science.gov (United States)

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

    2015-10-01

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

  20. Problem Solving and Learning

    CERN Document Server

    Singh, Chandralekha

    2016-01-01

    One finding of cognitive research is that people do not automatically acquire usable knowledge by spending lots of time on task. Because students' knowledge hierarchy is more fragmented, "knowledge chunks" are smaller than those of experts. The limited capacity of short term memory makes the cognitive load high during problem solving tasks, leaving few cognitive resources available for metacognition. The abstract nature of the laws of physics and the chain of reasoning required to draw meaningful inferences makes these issues critical. In order to help students, it is crucial to consider the difficulty of a problem from the perspective of students. We are developing and evaluating interactive problem-solving tutorials to help students in the introductory physics courses learn effective problem-solving strategies while solidifying physics concepts. The self-paced tutorials can provide guidance and support for a variety of problem solving techniques, and opportunity for knowledge and skill acquisition.

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

    DEFF Research Database (Denmark)

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

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

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

    Science.gov (United States)

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

    2015-12-01

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

  3. 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

  4. 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

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

    Science.gov (United States)

    Köpsén, Susanne; Nyström, Sofia

    2015-01-01

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

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

    Science.gov (United States)

    Köpsén, Susanne; Nyström, Sofia

    2015-01-01

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

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

    Science.gov (United States)

    Chen, Yu; Xu, Xiao-Hong

    2014-02-01

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

  8. An Ambient Awareness Tool for Supporting Supervised Collaborative Problem Solving

    Science.gov (United States)

    Alavi, H. S.; Dillenbourg, P.

    2012-01-01

    We describe an ambient awareness tool, named "Lantern", designed for supporting the learning process in recitation sections, (i.e., when students work in small teams on the exercise sets with the help of tutors). Each team is provided with an interactive lamp that displays their work status: the exercise they are working on, if they have…

  9. On Training Targets for Supervised Speech Separation

    OpenAIRE

    Wang, Yuxuan; Narayanan, Arun; Wang, DeLiang

    2014-01-01

    Formulation of speech separation as a supervised learning problem has shown considerable promise. In its simplest form, a supervised learning algorithm, typically a deep neural network, is trained to learn a mapping from noisy features to a time-frequency representation of the target of interest. Traditionally, the ideal binary mask (IBM) is used as the target because of its simplicity and large speech intelligibility gains. The supervised learning framework, however, is not restricted to the...

  10. 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...

  11. 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...

  12. Dimensions of problem based learning

    DEFF Research Database (Denmark)

    Nielsen, Jørgen Lerche; Andreasen, Lars Birch

    2013-01-01

    The article contributes to the literature on problem based learning and problem-oriented project work, building on and reflecting the experiences of the authors through decades of work with problem-oriented project pedagogy. The article explores different dimensions of problem based learning...

  13. Efficient supervised learning in networks with binary synapses

    CERN Document Server

    Baldassi, Carlo; Brunel, Nicolas; Zecchina, Riccardo

    2007-01-01

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

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

    DEFF Research Database (Denmark)

    Kobayashi, Sofie; Rump, Camilla Østerberg

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

  15. 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.

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

    Science.gov (United States)

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

    2010-05-01

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

  17. 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...

  18. 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.

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

    Science.gov (United States)

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

    2016-06-01

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

  20. Team Learning on Messy Problems

    NARCIS (Netherlands)

    Rouwette, E.A.J.A.; Vennix, J.A.M.

    2007-01-01

    Concentrates on procedures used to foster group learning for a particular type of problem, the so-called messy problem. The distinctive feature of messy problems is that the people involved have different ideas on what the problem is or whether there even is a problem. Nevertheless, those involved

  1. Team Learning on Messy Problems

    NARCIS (Netherlands)

    Rouwette, E.A.J.A.; Vennix, J.A.M.

    2007-01-01

    Concentrates on procedures used to foster group learning for a particular type of problem, the so-called messy problem. The distinctive feature of messy problems is that the people involved have different ideas on what the problem is or whether there even is a problem. Nevertheless, those involved i

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

    DEFF Research Database (Denmark)

    Kobayashi, Sofie; Rump, Camilla Østerberg

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

  3. 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...

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

    Science.gov (United States)

    Mollon, P

    1989-06-01

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

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

    Directory of Open Access Journals (Sweden)

    Ravikumar KE

    2012-10-01

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

  6. Problem Order Implications for Learning

    Science.gov (United States)

    Li, Nan; Cohen, William W.; Koedinger, Kenneth R.

    2013-01-01

    The order of problems presented to students is an important variable that affects learning effectiveness. Previous studies have shown that solving problems in a blocked order, in which all problems of one type are completed before the student is switched to the next problem type, results in less effective performance than does solving the problems…

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

    DEFF Research Database (Denmark)

    Stevanovic, Matija; Pedersen, Jens Myrup

    2014-01-01

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

  8. 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 ...

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

    Science.gov (United States)

    Hunt, Brandon; Gilmore, Genevieve Weber

    2011-01-01

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

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

    CSIR Research Space (South Africa)

    Twala, B

    2010-10-01

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

  11. Problem Based Learning and sustainability

    DEFF Research Database (Denmark)

    Pizzol, Massimo; Løkke, Søren; Schmidt, Jannick Højrup

    Problem-Based Learning (PBL) is a style of active learning based on problem solving. PBL aims at providing university students with flexible knowledge, capacity to self-learning, and skills in problem solving and collaboration. In this context, the present study explores the advantages...... University (AAU), Denmark, and focuses on the teaching of Life Cycle Assessment as a method for sustainability assessment. The objective is providing recommendations for future LCA teaching and learning. PBL activites performed at AAU were evaluated critically to detemine to what extent they addressed...

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

    Science.gov (United States)

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

    2014-08-01

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

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

    OpenAIRE

    2012-01-01

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

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

    KAUST Repository

    Zerrouki, Nabil

    2017-01-05

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

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

    Science.gov (United States)

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

    2017-01-01

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

  16. 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.

  17. 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...

  18. Types of Learning Problems

    Science.gov (United States)

    ... Ear Nose & Throat Emotional Problems Eyes Fever From Insects or Animals Genitals and Urinary Tract Glands & Growth ... important distinction is that this is not a vision problem ; rather, the brain is reversing, inverting or ...

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

    OpenAIRE

    2012-01-01

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

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

    OpenAIRE

    González Acuña, Ana Isabel

    2014-01-01

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

  1. 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

  2. Problem Based Learning and sustainability

    DEFF Research Database (Denmark)

    Pizzol, Massimo; Løkke, Søren; Schmidt, Jannick Højrup

    Problem-Based Learning (PBL) is a style of active learning based on problem solving. PBL aims at providing university students with flexible knowledge, capacity to self-learning, and skills in problem solving and collaboration. In this context, the present study explores the advantages and challe......Problem-Based Learning (PBL) is a style of active learning based on problem solving. PBL aims at providing university students with flexible knowledge, capacity to self-learning, and skills in problem solving and collaboration. In this context, the present study explores the advantages...... University (AAU), Denmark, and focuses on the teaching of Life Cycle Assessment as a method for sustainability assessment. The objective is providing recommendations for future LCA teaching and learning. PBL activites performed at AAU were evaluated critically to detemine to what extent they addressed...... of how PBL-approaches were used to develop five specific competences in sustainability. It is concluded that -for the case fo LCA teaching at AAU- the PBL model included activities to develop system thinking, interpersonal competence, and normative competence. However, the PBL approach should...

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

    CERN Document Server

    Grüning, André

    2011-01-01

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

  4. 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.

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

    Science.gov (United States)

    Geurts, Pierre; Irrthum, Alexandre; Wehenkel, Louis

    2009-12-01

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

  6. 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

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

    Science.gov (United States)

    Butler, Brandon M.; Diacopoulos, Mark M.

    2016-01-01

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

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

    Science.gov (United States)

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

    2017-01-01

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

  9. 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

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

    NARCIS (Netherlands)

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

    2016-01-01

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

  11. Problem-Based Learning Online

    DEFF Research Database (Denmark)

    Kolbæk, Ditte; Nortvig, Anne-Mette

    2017-01-01

    Problem- and Project-Based Learning (PBL) is a widely used pedagogical method in higher education. Although PBL encourages self-directed learning and works with the students’ own projects and problems, it also includes teacher presentations, discussions and group reflections, both on......-campus and online. Therefore, the teacher’s plans might be relevant to the students’ projects, but that is not always the case. This study investigates how master’s students interact with an online Problem-Based Learning design and examines how technology influences these interactions. The empirical data stem from...... lessons at an online master’s course, and they were collected and analyzed using a netnographic approach. The study finds that concepts like self-directed learning and active involvement of everyone can have very different meanings from the teachers’ and the students’ points of view. If the students do...

  12. Supervised learning for neural manifold using spatiotemporal brain activity

    Science.gov (United States)

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

    2015-12-01

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

  13. Collective academic supervision

    DEFF Research Database (Denmark)

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

    2013-01-01

    are interconnected. Collective Academic Supervision provides possibilities for systematic interaction between individual master students in their writing process. In this process they learn core academic competencies, such as the ability to assess theoretical and practical problems in their practice and present them...

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

    OpenAIRE

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

    2012-01-01

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

  15. [Ethical problems experienced in a supervised curricular internship in nursing in an integrated curriculum].

    Science.gov (United States)

    Burgatti, Juliane Cristina; Bracialli, Luzmarina Aparecida Doretto; Oliveira, Maria Amélia de Campos

    2013-08-01

    An exploratory, qualitative study with the objective of analyzing repercussions of the supervised curricular internship in the development of the ethical dimension of competency in undergraduate nursing students. Semistructured interviews were performed with 28 students, professors and nurse preceptors of a public institution of higher education in the state of São Paulo, during the period of October of 2010 to March of 2011. The empirical result was subjected to the technique of discourse analysis and resulted in the empirical categories: preservation of autonomy; social responsibility and respect in interpersonal relations in health care delivery and in the teaching - learning process; treatment and care from the ethical dimension; and, public responsibility and social justice. It was concluded that the phase that uses problematization as a method of teaching and learning provides critical reflection about professional practice in the services and system of health.

  16. 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.

  17. Conducting Supervised Experiential Learning/Field Experiences for Students' Development and Career Reinforcement.

    Science.gov (United States)

    Leventhal, Jerome I.

    A major problem in the educational system of the United States is that a great number of students and graduates lack a career objective, and, therefore, many workers are unhappy. Offering a variety of supervised field experiences, paid or unpaid, in which students see workers in their occupations will help students identify career choices.…

  18. Characteristics of Problem-Based Learning

    DEFF Research Database (Denmark)

    Kolmos, Anette

    2003-01-01

    Problem BAsed LEarning (PBL) is widely regarded as a successful and innovative method for engineering education. The article highlights the Dutch approach of directing the learning process throuogh problem analysis and the Danish model of project-organised learning...

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

    Science.gov (United States)

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

    2013-01-01

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

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

    Science.gov (United States)

    Osborne, Cara; Burton, Sheila

    2014-01-01

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

  1. 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…

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

    DEFF Research Database (Denmark)

    Tanggaard, Lene; Wegener, Charlotte

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

  3. A Comparison of Supervised Machine Learning Algorithms and Feature Vectors for MS Lesion Segmentation Using Multimodal Structural MRI

    Science.gov (United States)

    Sweeney, Elizabeth M.; Vogelstein, Joshua T.; Cuzzocreo, Jennifer L.; Calabresi, Peter A.; Reich, Daniel S.; Crainiceanu, Ciprian M.; Shinohara, Russell T.

    2014-01-01

    Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance. PMID:24781953

  4. No Free Lunch versus Occam's Razor in Supervised Learning

    CERN Document Server

    Lattimore, Tor

    2011-01-01

    The No Free Lunch theorems are often used to argue that domain specific knowledge is required to design successful algorithms. We use algorithmic information theory to argue the case for a universal bias allowing an algorithm to succeed in all interesting problem domains. Additionally, we give a new algorithm for off-line classification, inspired by Solomonoff induction, with good performance on all structured problems under reasonable assumptions. This includes a proof of the efficacy of the well-known heuristic of randomly selecting training data in the hope of reducing misclassification rates.

  5. Java problem-based learning

    Directory of Open Access Journals (Sweden)

    Goran P, Šimić

    2012-01-01

    Full Text Available The paper describes the self-directed problem-based learning system (PBL named Java PBL. The expert module is the kernel of Java PBL. It involves a specific domain model, a problem generator and a solution generator. The overall system architecture is represented in the paper. Java PBL can act as the stand-alone system, but it is also designed to provide support to learning management systems (LMSs. This is provided by a modular design of the system. An LMS can offer the declarative knowledge only. Java PBL offers the procedural knowledge and the progress of the learner programming skills. The free navigation, unlimited numbers of problems and recommendations represent the main pedagogical strategies and tactics implemented into the system.

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

    Science.gov (United States)

    Xu, Lei; Klasa, Stan

    1992-07-01

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

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

    DEFF Research Database (Denmark)

    Alstrøm, Tommy Sonne

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

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

    Science.gov (United States)

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

    2008-09-01

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

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

    Science.gov (United States)

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

    2015-02-01

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

  10. 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.

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

    Science.gov (United States)

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

    2010-09-01

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

  12. Problems on the Enforcement Judicature and Legal Supervision of Animal Welfare Laws

    Directory of Open Access Journals (Sweden)

    Song Wei

    2007-12-01

    Full Text Available AbstratAlong with the economic development and social progress, the idea of animal protection has gone deep into people’s hearts all over the world. Animal protection includes not only reserving species resources and nursing the diversification of creatures, but also animal health care and welfare. Compared with some other countries, there exists severe imperfection on legislation for animal protection,and the related legal system is nearly left blank. In view of such unfavorable state and the rising appeal for animal protection, it is an inevitable trend to enact China Animal Welfare Laws. Since the final purpose of legislation has to be reached through the enforcement and realization of the law, there involve the problems onthe enforcement, judicature and legal supervision of China Animal Welfare Laws.

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

    Science.gov (United States)

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

    2017-05-01

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

  14. Characteristics of Problems in Problem-based Learning

    NARCIS (Netherlands)

    N. Sockalingam (Nachamma)

    2010-01-01

    textabstractThis thesis focuses on characteristics of problems in Problem-based Learning (PBL). Problems initiate the students’ learning process in PBL. Several studies have shown that the quality of problems has a positive influence on students’ academic achievement and interest. In addition, the q

  15. Characteristics of Problems in Problem-based Learning

    NARCIS (Netherlands)

    N. Sockalingam (Nachamma)

    2010-01-01

    textabstractThis thesis focuses on characteristics of problems in Problem-based Learning (PBL). Problems initiate the students’ learning process in PBL. Several studies have shown that the quality of problems has a positive influence on students’ academic achievement and interest. In addition, the

  16. A Supervised Learning Approach to Search of Definitions

    Institute of Scientific and Technical Information of China (English)

    Jun Xu; Yun-Bo Cao; Hang Li; Min Zhao; Ya-Lou Huang

    2006-01-01

    This paper addresses the issue of search of definitions. Specifically, for a given term, we are to find out its definition candidates and rank the candidates according to their likelihood of being good definitions. This is in contrast to the traditional methods of either generating a single combined definition or outputting all retrieved definitions. Definition ranking is essential for tasks. A specification for judging the goodness of a definition is given. In the specification, a definition is categorized into one of the three levels: good definition, indifferent definition, or bad definition. Methods of performing definition ranking are also proposed in this paper, which formalize the problem as either classification or ordinal regression.We employ SVM (Support Vector Machines) as the classification model and Ranking SVM as the ordinal regression model respectively, and thus they rank definition candidates according to their likelihood of being good definitions. Features for constructing the SVM and Ranking SVM models are defined, which represent the characteristics of terms, definition candidate, and their relationship. Experimental results indicate that the use of SVM and Ranking SVM can significantly outperform the baseline methods such as heuristic rules, the conventional information retrieval-Okapi, or SVM regression.This is true when both the answers are paragraphs and they are sentences. Experimental results also show that SVM or Ranking SVM models trained in one domain can be adapted to another domain, indicating that generic models for definition ranking can be constructed.

  17. 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.

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

    Science.gov (United States)

    Onder, Devrim; Sarioglu, Sulen; Karacali, Bilge

    2014-12-01

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

  19. Learning a Markov Logic network for supervised gene regulatory network inference.

    Science.gov (United States)

    Brouard, Céline; Vrain, Christel; Dubois, Julie; Castel, David; Debily, Marie-Anne; d'Alché-Buc, Florence

    2013-09-12

    Gene regulatory network inference remains a challenging problem in systems biology despite the numerous approaches that have been proposed. When substantial knowledge on a gene regulatory network is already available, supervised network inference is appropriate. Such a method builds a binary classifier able to assign a class (Regulation/No regulation) to an ordered pair of genes. Once learnt, the pairwise classifier can be used to predict new regulations. In this work, we explore the framework of Markov Logic Networks (MLN) that combine features of probabilistic graphical models with the expressivity of first-order logic rules. We propose to learn a Markov Logic network, e.g. a set of weighted rules that conclude on the predicate "regulates", starting from a known gene regulatory network involved in the switch proliferation/differentiation of keratinocyte cells, a set of experimental transcriptomic data and various descriptions of genes all encoded into first-order logic. As training data are unbalanced, we use asymmetric bagging to learn a set of MLNs. The prediction of a new regulation can then be obtained by averaging predictions of individual MLNs. As a side contribution, we propose three in silico tests to assess the performance of any pairwise classifier in various network inference tasks on real datasets. A first test consists of measuring the average performance on balanced edge prediction problem; a second one deals with the ability of the classifier, once enhanced by asymmetric bagging, to update a given network. Finally our main result concerns a third test that measures the ability of the method to predict regulations with a new set of genes. As expected, MLN, when provided with only numerical discretized gene expression data, does not perform as well as a pairwise SVM in terms of AUPR. However, when a more complete description of gene properties is provided by heterogeneous sources, MLN achieves the same performance as a black-box model such as a

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

    Directory of Open Access Journals (Sweden)

    R.Rui

    2013-04-01

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

  1. 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.

  2. Semi-supervised Machine Learning for Analysis of Hydrogeochemical Data and Models

    Science.gov (United States)

    Vesselinov, Velimir; O'Malley, Daniel; Alexandrov, Boian; Moore, Bryan

    2017-04-01

    Data- and model-based analyses such as uncertainty quantification, sensitivity analysis, and decision support using complex physics models with numerous model parameters and typically require a huge number of model evaluations (on order of 10^6). Furthermore, model simulations of complex physics may require substantial computational time. For example, accounting for simultaneously occurring physical processes such as fluid flow and biogeochemical reactions in heterogeneous porous medium may require several hours of wall-clock computational time. To address these issues, we have developed a novel methodology for semi-supervised machine learning based on Non-negative Matrix Factorization (NMF) coupled with customized k-means clustering. The algorithm allows for automated, robust Blind Source Separation (BSS) of groundwater types (contamination sources) based on model-free analyses of observed hydrogeochemical data. We have also developed reduced order modeling tools, which coupling support vector regression (SVR), genetic algorithms (GA) and artificial and convolutional neural network (ANN/CNN). SVR is applied to predict the model behavior within prior uncertainty ranges associated with the model parameters. ANN and CNN procedures are applied to upscale heterogeneity of the porous medium. In the upscaling process, fine-scale high-resolution models of heterogeneity are applied to inform coarse-resolution models which have improved computational efficiency while capturing the impact of fine-scale effects at the course scale of interest. These techniques are tested independently on a series of synthetic problems. We also present a decision analysis related to contaminant remediation where the developed reduced order models are applied to reproduce groundwater flow and contaminant transport in a synthetic heterogeneous aquifer. The tools are coded in Julia and are a part of the MADS high-performance computational framework (https://github.com/madsjulia/Mads.jl).

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

    Science.gov (United States)

    Wu, Lin; Wang, Yang; Pan, Shirui

    2016-10-04

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

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

    Science.gov (United States)

    Fujita, Masahiko

    2013-06-01

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

  5. 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.

  6. Problem-based learning: rationale and description

    NARCIS (Netherlands)

    H.G. Schmidt (Henk)

    1983-01-01

    textabstractProblem-based learning is an instructional method that is said to provide students with knowledge suitable for problem solving. In order to test this assertion the process of problem-based learning is described and measured against three principles of learning: activation of prior knowle

  7. Problem effectiveness in a course using problem-based learning

    NARCIS (Netherlands)

    D.H.J.M. Dolmans (Diana); W.H. Gijselaers (Wim); H.G. Schmidt (Henk); S.B. van der Meer

    1993-01-01

    textabstractBackground. Problem-based learning (PBL) emphasizes active generation of learning issues by students. Both students and reachers, however, tend to worry that not all important knowledge will be acquired. To explore this question, problem effectiveness (i.e., for each problem, the degree

  8. Problem effectiveness in a course using problem-based learning

    NARCIS (Netherlands)

    D.H.J.M. Dolmans (Diana); W.H. Gijselaers (Wim); H.G. Schmidt (Henk); S.B. van der Meer

    1993-01-01

    textabstractBackground. Problem-based learning (PBL) emphasizes active generation of learning issues by students. Both students and reachers, however, tend to worry that not all important knowledge will be acquired. To explore this question, problem effectiveness (i.e., for each problem, the degree

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

    Directory of Open Access Journals (Sweden)

    Ritaban Dutta

    2013-09-01

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

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

    Science.gov (United States)

    Gustafsson, Margareta; Blomberg, Karin; Holmefur, Marie

    2015-07-01

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

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

    Directory of Open Access Journals (Sweden)

    Zhiling Guo

    2016-03-01

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

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

    Science.gov (United States)

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

    2017-04-01

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

  13. Learning from the problems of problem-based learning

    Directory of Open Access Journals (Sweden)

    Epstein Richard

    2004-01-01

    Full Text Available Abstract Background The last decade has witnessed a rapid expansion of biomedical knowledge. Despite this, fashions in medical education over the same period have shifted away from factual (didactic teaching and towards contextual, or problem-based, learning (PBL. This paradigm shift has been justified by studies showing that PBL improves reasoning and communication while being associated with few if any detectable knowledge deficits. Discussion Analysis of the literature indicates that the recent rapid rise of PBL has closely paralleled the timing of the information explosion. The growing dominance of PBL could thus worsen the problems of information management in medical education via several mechanisms: first, by creating the impression that a defined spectrum of core factual knowledge suffices for clinical competence despite ongoing knowledge expansion (quality cost; second, by dissuading teachers from refining the educational utility of didactic modalities (improvement cost; and third, by reducing faculty time for developing reusable resources to impart factual knowledge more efficiently (opportunity cost. Summary These costs of PBL imply a need for strengthening the knowledge base of 21st-century medical graduates. New initiatives towards this end could include the development of more integrated cognitive techniques for facilitating the comprehension of complex data; the design of differentiated medical curricula for producing graduates with defined high-priority skill sets; and the encouragement of more cost-effective faculty teaching activities focused on the prototyping and testing of innovative commercializable educational tools.

  14. Problems and Solutions in Heterogeneous Cooperative Learning

    Institute of Scientific and Technical Information of China (English)

    易彦池

    2007-01-01

    Heterogeneous cooperative learning,as the best kind of cooperative learning,is quite often adopted to make utmost use of the human power in second language learning. On the other hand ,some problems existing in it make it far from satisfactory. In this thesis ,the writer will analyse the problems and put forward some solutions to perfect HCL.

  15. EFFECT OF PROBLEM BASED LEARNING IN COMPARISION WITH LECTURE BASED LEARNING IN FORENSIC MEDICINE

    Directory of Open Access Journals (Sweden)

    Padmakumar

    2015-09-01

    Full Text Available BACKGROUND: Problem based learning (PBL is an approach to learning and instruction in which students tackle problems in small groups under the supervision of a teacher. This style of learning assumed to foster increased retention of knowledge, improve student’s gene ral problem solving skills, enhance integration of basic science concepts in to clinical problems, foster the development of self - directed learning skills and strengthen student’s intrinsic motivation. AIM: The study was conducted to compare the effect of Problem based learning in comparison with lecture based learning. SETTING: A cross - sectional study was conducted among 2nd year MBBS students of Jubilee Mission Medical College and Research Institute, Thrissur during the period of December 2014 to March 20 15. METHODOLOGY: The batch is divided into two groups (A & B, 45 in each group. By using PBL method, blunt force injuries were taught to Group - A and sharp weapon injuries to group - B. By using lecture based learning (LBL method blunt force injuries were t aught to Group - B and sharp weapon injuries to group - A. At the end of the session a test in the form of MCQ was conducted on the students to evaluate their learning outcome. OBSERVATION AND RESU LTS: In session I, the average test score of LBL group was 8.16 and PBL group was 12. The difference was statistically significant. In session - II also 45 students has participated each in LBL and PBL classes. The average of test score of LBL group was 7.267 and PBL was 11.289, which was highly significant statistical ly . CONCLUSION: Study has proven that problem based learning is an effective teaching learning method when compared to conventional lecture based learning.

  16. 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.

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

    CERN Document Server

    Mohammed, Irshad

    2015-01-01

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

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

    Science.gov (United States)

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

    2014-01-01

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

  19. Technology and Problem-Based Learning

    Science.gov (United States)

    Uden, Lorna; Beaumont, Chris

    2006-01-01

    Problem-based learning (PBL) has been the focus of many developments in teaching and learning facilitation in recent years. It has been claimed that PBL produces independent learners who are motivated, engaged in deep learning, work as a team, and develop effective strategies, skills and knowledge for life-long learning and professional work.…

  20. Problem based learning in acoustics at Aalborg University

    DEFF Research Database (Denmark)

    Hammershøi, Dorte; Ordoñez, Rodrigo Pizarro; Christensen, Flemming

    2010-01-01

    semesters, the learning objectives are distributed between courses with independent examination, and a semester project. Each semester has a theme the projects must comply with. Either supervisors, students or industry propose the problem that become the basis for the project work. Under supervision......, the students narrow down the problem, address possible solutions, and typically implement one or more of the options for further evaluation. The courses supplement the project work by adding specific and general knowledge of the subject areas of each semester. The courses either have direct application......The master program in Acoustics (M. Sc.) from Aalborg University is taught at the Department of Electronic system. The M. Sc. program consists of three semesters with course units and problem based project work organized in groups, and a final semester for a master thesis. During the first three...

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

    OpenAIRE

    Kataria, Saurabh; Gaultier, Clément; Deleforge, Antoine

    2016-01-01

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

  2. Problem based learning: Cognitive and metacognitive processes during problem analysis

    NARCIS (Netherlands)

    W.S. de Grave; H.P.A. Boshuizen (Henny); H.G. Schmidt (Henk)

    1996-01-01

    textabstractAn important phase of problem-based learning in a tutorial group is problem analysis. This article describes a study investigating the ongoing cognitive and metacognitive processes during problem analysis, by analysing the verbal communication among group members, and their thinking proc

  3. Problem based learning: Cognitive and metacognitive processes during problem analysis

    NARCIS (Netherlands)

    W.S. de Grave; H.P.A. Boshuizen (Henny); H.G. Schmidt (Henk)

    1996-01-01

    textabstractAn important phase of problem-based learning in a tutorial group is problem analysis. This article describes a study investigating the ongoing cognitive and metacognitive processes during problem analysis, by analysing the verbal communication among group members, and their thinking

  4. Are qualitative and quantitative sleep problems associated with delinquency when controlling for psychopathic features and parental supervision?

    Science.gov (United States)

    Backman, Heidi; Laajasalo, Taina; Saukkonen, Suvi; Salmi, Venla; Kivivuori, Janne; Aronen, Eeva T

    2015-10-01

    The aim of this study was to explore the relationship between sleep, including both qualitative and quantitative aspects, and delinquent behaviour while controlling for psychopathic features of adolescents and parental supervision at bedtime. We analysed data from a nationally representative sample of 4855 Finnish adolescents (mean age 15.3 years, 51% females). Sleep problems, hours of sleep and delinquency were evaluated via self-report. Psychopathic features were measured with the Antisocial Process Screening Device - Self-Report. In negative binomial regressions, gender and sleep-related variables acted as predictors for both property and violent crime after controlling for psychopathic features and parental supervision at bedtime. The results suggest that both sleep problems (at least three times per week, at least for a year) and an insufficient amount of sleep (less than 7 h) are associated with property crime and violent behaviour, and the relationship is not explained by gender, degree of parental supervision at bedtime or co-occurring psychopathic features. These results suggest that sleep difficulties and insufficient amount of sleep are associated with delinquent behaviour in adolescents. The significance of addressing sleep-related problems, both qualitative and quantitative, among adolescents is thus highlighted. Implications for a prevention technique of delinquent behaviour are discussed. © 2015 European Sleep Research Society.

  5. 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.

  6. 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

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

    Directory of Open Access Journals (Sweden)

    Zeenia Jagga

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

  8. Is learning in problem-based learning cumulative?

    NARCIS (Netherlands)

    E.H.J. Yew (Elaine); E. Chng (Esther); H.G. Schmidt (Henk)

    2011-01-01

    textabstractProblem-based learning (PBL) is generally organized in three phases, involving collaborative and self-directed learning processes. The hypothesis tested here is whether learning in the different phases of PBL is cumulative, with learning in each phase depending on that of the previous ph

  9. 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.

  10. 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

  11. 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.

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

    Science.gov (United States)

    Zheng, Wu; Blake, Catherine

    2015-10-01

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

  13. Problem Finding in Professional Learning Communities: A Learning Study Approach

    Science.gov (United States)

    Tan, Yuen Sze Michelle; Caleon, Imelda Santos

    2016-01-01

    This study marries collaborative problem solving and learning study in understanding the onset of a cycle of teacher professional development process within school-based professional learning communities (PLCs). It aimed to explore how a PLC carried out collaborative problem finding--a key process involved in collaborative problem solving--that…

  14. SAR Target Recognition via Supervised Discriminative Dictionary Learning and Sparse Representation of the SAR-HOG Feature

    Directory of Open Access Journals (Sweden)

    Shengli Song

    2016-08-01

    Full Text Available Automatic target recognition (ATR in synthetic aperture radar (SAR images plays an important role in both national defense and civil applications. Although many methods have been proposed, SAR ATR is still very challenging due to the complex application environment. Feature extraction and classification are key points in SAR ATR. In this paper, we first design a novel feature, which is a histogram of oriented gradients (HOG-like feature for SAR ATR (called SAR-HOG. Then, we propose a supervised discriminative dictionary learning (SDDL method to learn a discriminative dictionary for SAR ATR and propose a strategy to simplify the optimization problem. Finally, we propose a SAR ATR classifier based on SDDL and sparse representation (called SDDLSR, in which both the reconstruction error and the classification error are considered. Extensive experiments are performed on the MSTAR database under standard operating conditions and extended operating conditions. The experimental results show that SAR-HOG can reliably capture the structures of targets in SAR images, and SDDL can further capture subtle differences among the different classes. By virtue of the SAR-HOG feature and SDDLSR, the proposed method achieves the state-of-the-art performance on MSTAR database. Especially for the extended operating conditions (EOC scenario “Training 17 ∘ —Testing 45 ∘ ”, the proposed method improves remarkably with respect to the previous works.

  15. Learning from Dealing with Real World Problems

    Science.gov (United States)

    Akcay, Hakan

    2017-01-01

    The purpose of this article is to provide an example of using real world issues as tools for science teaching and learning. Using real world issues provides students with experiences in learning in problem-based environments and encourages them to apply their content knowledge to solving current and local problems.

  16. Bridging disciplines through problem based learning

    DEFF Research Database (Denmark)

    Stentoft, Diana

    2011-01-01

    of how a problem based approach to learning will be implemented in the programs to support students in their engagement with the complexities of amalgamating and transgressing the disciplines of technology and anthropology. The paper is concluded by a brief discussion of problem based learning...

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

    Institute of Scientific and Technical Information of China (English)

    Hao Cheng; Na Yu,; Tai-Jun Wang

    2015-01-01

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

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

    CERN Document Server

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

    2014-01-01

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

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

    Science.gov (United States)

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

    2016-03-01

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

  20. 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

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

    Science.gov (United States)

    Ramsey, Jon W.; Edwards, M. Craig

    2012-01-01

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

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

    Science.gov (United States)

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

    2011-01-01

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

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

    NARCIS (Netherlands)

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

    2014-01-01

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

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

    NARCIS (Netherlands)

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

    2014-01-01

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

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

    Science.gov (United States)

    Ramsey, Jon W.; Edwards, M. Craig

    2012-01-01

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

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

    Science.gov (United States)

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

    2013-01-01

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

  7. English Learning Strategies & Problems of College Students

    Institute of Scientific and Technical Information of China (English)

    宋捷

    2013-01-01

    The paper aims to study the learning strategies deployed by a sample of college students with the hope of finding certain learning pattern and typical problems and hence help students hone language skill in a more efficient way. The report focuses on the students’learning strategies in different respects of liste-ning, speaking, reading, writing, cultural background, vocabulary and etc. by the frequent use of statistical analysis, trying to explain learning obstacles according to cognitive theory. At last part, common advice for English learning is given as an approach for efficient learning.

  8. Whither Supervision?

    Directory of Open Access Journals (Sweden)

    Duncan Waite

    2006-11-01

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

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

    Directory of Open Access Journals (Sweden)

    Ricardo Andres Pizarro

    2016-12-01

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

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

    Science.gov (United States)

    Guo, Shuang; Koelsch, Stefan

    2015-11-11

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

  11. Transformation to problem and project based learning

    DEFF Research Database (Denmark)

    2006-01-01

    . However, the internal factors at the institutional level may be unknown. Institutions have developed many different pedagogical models, using very different strategies for development. Nearly all Danish engineering institutions have implemented elements of Problem Based and Project Based Learning (PBL...

  12. Extendable supervised dictionary learning for exploring diverse and concurrent brain activities in task-based fMRI.

    Science.gov (United States)

    Zhao, Shijie; Han, Junwei; Hu, Xintao; Jiang, Xi; Lv, Jinglei; Zhang, Tuo; Zhang, Shu; Guo, Lei; Liu, Tianming

    2017-06-09

    Recently, a growing body of studies have demonstrated the simultaneous existence of diverse brain activities, e.g., task-evoked dominant response activities, delayed response activities and intrinsic brain activities, under specific task conditions. However, current dominant task-based functional magnetic resonance imaging (tfMRI) analysis approach, i.e., the general linear model (GLM), might have difficulty in discovering those diverse and concurrent brain responses sufficiently. This subtraction-based model-driven approach focuses on the brain activities evoked directly from the task paradigm, thus likely overlooks other possible concurrent brain activities evoked during the information processing. To deal with this problem, in this paper, we propose a novel hybrid framework, called extendable supervised dictionary learning (E-SDL), to explore diverse and concurrent brain activities under task conditions. A critical difference between E-SDL framework and previous methods is that we systematically extend the basic task paradigm regressor into meaningful regressor groups to account for possible regressor variation during the information processing procedure in the brain. Applications of the proposed framework on five independent and publicly available tfMRI datasets from human connectome project (HCP) simultaneously revealed more meaningful group-wise consistent task-evoked networks and common intrinsic connectivity networks (ICNs). These results demonstrate the advantage of the proposed framework in identifying the diversity of concurrent brain activities in tfMRI datasets.

  13. Problems of Level in Distance Learning

    Directory of Open Access Journals (Sweden)

    Zhanna T. Balmagambetova

    2013-01-01

    Full Text Available . The article is focused on the problems of level approach to communicative competence formation in the course of the second language learning. The change of educational paradigm demands turn of linguists to differentiated learner-centered education – level system of language learning

  14. Solving Wicked Problems through Action Learning

    Science.gov (United States)

    Crul, Liselore

    2014-01-01

    This account of practice outlines the Oxyme Action Learning Program which was conducted as part of the Management Challenge in my final year of the MSc in Coaching and Behavioral Change at Henley Business School. The central research questions were: (1) how action learning can help to solve wicked problems and (2) what the effect of an action…

  15. Problems and Inconsistencies with Kolb's Learning Styles.

    Science.gov (United States)

    Garner, Iain

    2000-01-01

    Argues that there are substantial problems with the theoretical foundations of David Kolb's Learning Style Inventory (LSI). Notes anomalies with the relationship between Carl Jung's style and Kolb's use of possibility processing. Argues that these anomalies make it impossible for defining firm conclusions about the nature of Kolb's learning style.…

  16. Solving Wicked Problems through Action Learning

    Science.gov (United States)

    Crul, Liselore

    2014-01-01

    This account of practice outlines the Oxyme Action Learning Program which was conducted as part of the Management Challenge in my final year of the MSc in Coaching and Behavioral Change at Henley Business School. The central research questions were: (1) how action learning can help to solve wicked problems and (2) what the effect of an action…

  17. Enhanced learning through design problems

    DEFF Research Database (Denmark)

    Jensen, Bogi Bech; Högberg, Stig; Jensen, Frida av Fløtum

    2012-01-01

    This paper describes a teaching method used in an electrical machines course, where the students learn about electrical machines by designing them. The aim of the course is not to teach design, albeit this is a side product, but rather to teach the fundamentals and the function of electrical mach...

  18. Problem Based Learning - Linking Students and Industry

    DEFF Research Database (Denmark)

    Fink, Flemming K.

    2006-01-01

    -oriented project work: students work in teams to solve externally defined problem areas in co-operation with businesses, organisations and public institutions. Project Organised Problem Based Learning (POPBL) has generated a high degree of cooperation with the society and private companies. There are all the time...... is essentially problem solving. The presentation looks into on campus POPBL and the Facilitated Work Based Learning (FBL) for continuing education. It also presents case examples of POPBL work.......WG2_G4 Problem based learning – linking students and industry: a case study from Aalborg, Denmark Flemming K. Flink ELITE Aalborg University In Aalborg University, Denmark, all study programmes are organised around inter-disciplinary project work in groups. Up to 50% of the study work is problem...

  19. Does the Extent of Problem Familiarity Influence Students' Learning in Problem-Based Learning?

    Science.gov (United States)

    Sockalingam, Nachamma; Schmidt, Henk G.

    2013-01-01

    This study investigated the relationship between problem familiarity and students' learning in a problem-based course. Problem familiarity in this study refers to the extent to which a problem fits with students' prior knowledge and experiences. As part of regular course work, 172 students were given two problems on different occasions.…

  20. Impact of problem-based learning in a large classroom setting: student perception and problem-solving skills.

    Science.gov (United States)

    Klegeris, Andis; Hurren, Heather

    2011-12-01

    Problem-based learning (PBL) can be described as a learning environment where the problem drives the learning. This technique usually involves learning in small groups, which are supervised by tutors. It is becoming evident that PBL in a small-group setting has a robust positive effect on student learning and skills, including better problem-solving skills and an increase in overall motivation. However, very little research has been done on the educational benefits of PBL in a large classroom setting. Here, we describe a PBL approach (using tutorless groups) that was introduced as a supplement to standard didactic lectures in University of British Columbia Okanagan undergraduate biochemistry classes consisting of 45-85 students. PBL was chosen as an effective method to assist students in learning biochemical and physiological processes. By monitoring student attendance and using informal and formal surveys, we demonstrated that PBL has a significant positive impact on student motivation to attend and participate in the course work. Student responses indicated that PBL is superior to traditional lecture format with regard to the understanding of course content and retention of information. We also demonstrated that student problem-solving skills are significantly improved, but additional controlled studies are needed to determine how much PBL exercises contribute to this improvement. These preliminary data indicated several positive outcomes of using PBL in a large classroom setting, although further studies aimed at assessing student learning are needed to further justify implementation of this technique in courses delivered to large undergraduate classes.

  1. Newton Methods for Large Scale Problems in Machine Learning

    Science.gov (United States)

    Hansen, Samantha Leigh

    2014-01-01

    The focus of this thesis is on practical ways of designing optimization algorithms for minimizing large-scale nonlinear functions with applications in machine learning. Chapter 1 introduces the overarching ideas in the thesis. Chapters 2 and 3 are geared towards supervised machine learning applications that involve minimizing a sum of loss…

  2. Newton Methods for Large Scale Problems in Machine Learning

    Science.gov (United States)

    Hansen, Samantha Leigh

    2014-01-01

    The focus of this thesis is on practical ways of designing optimization algorithms for minimizing large-scale nonlinear functions with applications in machine learning. Chapter 1 introduces the overarching ideas in the thesis. Chapters 2 and 3 are geared towards supervised machine learning applications that involve minimizing a sum of loss…

  3. Challenges and benefits for teachers in online Problem-Based learning

    DEFF Research Database (Denmark)

    Kolbæk, Ditte; Nortvig, Anne-Mette

    2017-01-01

    Research topic/aim Original problem based and project organized learning (PBL) was developed for collaboration between physically present students, but political decisions at many universities also require that collaboration, dialogues and other PBL activities take place online. We are interested...... University, and we are interested in categorising strategies that support teaching and supervision activities in online PBL settings. Our research question is: What challenges and benefits do teachers face when PBL is to take place online?...

  4. Counselor Supervision: A Consumer's Guide.

    Science.gov (United States)

    Yager, Geoffrey G.; Littrell, John M.

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

  5. Problem based learning in medical education.

    Science.gov (United States)

    Mansur, D I; Kayastha, S R; Makaju, R; Dongol, M

    2012-01-01

    Problem based learning, originally introduced in the Medical School at Mc-Master University in Canada in the late 1960s, and is now being used as a learning method in many medical schools in the United Kingdom and worldwide. Problem based learning have been adapted in many medical colleges of Nepal be used either as the mainstay of an entire curriculum or for the delivery of individual courses. Institution of Medicine, Tribhuvan University in 1980, BP Koirala Institute of Health Sciences at Dharan in 1999, KUSMS with the great support of faculties from Harvard University in 2001, Patan Academy of Health Sciences (PAHS), and lately all the affiliated medical colleges of Kathmandu University have adapted Problem based learning.

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

    Science.gov (United States)

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

    2014-08-01

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

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

    Directory of Open Access Journals (Sweden)

    Alaeddin Almubayed

    2015-06-01

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

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

    Directory of Open Access Journals (Sweden)

    Jingsha He

    2017-03-01

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

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

    CERN Document Server

    Acquaviva, Viviana

    2015-01-01

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

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

    Science.gov (United States)

    Millholland, Sarah; Laughlin, Gregory

    2017-09-01

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

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

    Science.gov (United States)

    Wang, He; Wong, K. Y. Michael

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

  12. 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.

  13. 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.

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

    Science.gov (United States)

    Young, Sarah

    2009-01-01

    This brief provides an overview of the knowledge and skills that administrators need in order to support and supervise teachers of adult English language learners. It begins with a review of resources and literature related to teacher supervision in general and to adult ESL education. It continues with information on the background and…

  15. Understanding Trust as an Essential Element of Trainee Supervision and Learning in the Workplace

    Science.gov (United States)

    Hauer, Karen E.; ten Cate, Olle; Boscardin, Christy; Irby, David M.; Iobst, William; O'Sullivan, Patricia S.

    2014-01-01

    Clinical supervision requires that supervisors make decisions about how much independence to allow their trainees for patient care tasks. The simultaneous goals of ensuring quality patient care and affording trainees appropriate and progressively greater responsibility require that the supervising physician trusts the trainee. Trust allows the…

  16. Enhancing the Doctoral Journey: The Role of Group Supervision in Supporting Collaborative Learning and Creativity

    Science.gov (United States)

    Fenge, Lee-Ann

    2012-01-01

    This article explores the role of group supervision within doctoral education, offering an exploration of the experience of group supervision processes through a small-scale study evaluating both student and staff experience across three cohorts of one professional doctorate programme. There has been very little research to date exploring…

  17. Is Direct Supervision in Clinical Education for Athletic Training Students Always Necessary to Enhance Student Learning?

    Science.gov (United States)

    Scriber, Kent; Trowbridge, Cindy

    2009-01-01

    Objective: To present an alternative model of supervision within clinical education experiences. Background: Several years ago direct supervision was defined more clearly in the accreditation standards for athletic training education programs (ATEPs). Currently, athletic training students may not gain any clinical experience without their clinical…

  18. Clinical group supervision in yoga therapy: model effects, and lessons learned.

    Science.gov (United States)

    Forbes, Bo; Volpe Horii, Cassandra; Earls, Bethany; Mashek, Stephanie; Akhtar, Fiona

    2012-01-01

    Clinical supervision is an integral component of therapist training and professional development because of its capacity for fostering knowledge, self-awareness, and clinical acumen. Individual supervision is part of many yoga therapy training programs and is referenced in the IAYT Standards as "mentoring." Group supervision is not typically used in the training of yoga therapists. We propose that group supervision effectively supports the growth and development of yoga therapists-in-training. We present a model of group supervision for yoga therapist trainees developed by the New England School of Integrative Yoga Therapeutics™ (The NESIYT Model) that includes the background, structure, format, and development of our inaugural 18-month supervision group. Pre-and post-supervision surveys and analyzed case notes, which captured key didactic and process themes, are discussed. Clinical issues, such as boundaries, performance anxiety, sense of self efficacy, the therapeutic alliance, transference and counter transference, pacing of yoga therapy sessions, evaluation of client progress, and adjunct therapist interaction are reviewed. The timing and sequence of didactic and process themes and benefits for yoga therapist trainees' professional development, are discussed. The NESIYT group supervision model is offered as an effective blueprint for yoga therapy training programs.

  19. Knowledge Work Supervision: Transforming School Systems into High Performing Learning Organizations.

    Science.gov (United States)

    Duffy, Francis M.

    1997-01-01

    This article describes a new supervision model conceived to help a school system redesign its anatomy (structures), physiology (flow of information and webs of relationships), and psychology (beliefs and values). The new paradigm (Knowledge Work Supervision) was constructed by reviewing the practices of several interrelated areas: sociotechnical…

  20. Classification and Diagnostic Output Prediction of Cancer Using Gene Expression Profiling and Supervised Machine Learning Algorithms

    DEFF Research Database (Denmark)

    Yoo, C.; Gernaey, Krist

    2008-01-01

    In this paper, a new supervised clustering and classification method is proposed. First, the application of discriminant partial least squares (DPLS) for the selection of a minimum number of key genes is applied on a gene expression microarray data set. Second, supervised hierarchical clustering ...

  1. Using Isomorphic Problems to Learn Introductory Physics

    CERN Document Server

    Lin, Shih-Yin

    2016-01-01

    In this study, we examine introductory physics students' ability to perform analogical reasoning between two isomorphic problems which employ the same underlying physics principles but have different surface features. Three hundred and sixty two students from a calculus-based and an algebra-based introductory physics course were given a quiz in the recitation in which they had to first learn from a solved problem provided and take advantage of what they learned from it to solve another problem (which we call the quiz problem) which was isomorphic. Previous research suggests that the multiple-concept quiz problem is challenging for introductory students. Students in different recitation classes received different interventions in order to help them discern and exploit the underlying similarities of the isomorphic solved and quiz problems. We also conducted think-aloud interviews with four introductory students in order to understand in-depth the difficulties they had and explore strategies to provide better sc...

  2. Material classification and automatic content enrichment of images using supervised learning and knowledge bases

    Science.gov (United States)

    Mallepudi, Sri Abhishikth; Calix, Ricardo A.; Knapp, Gerald M.

    2011-02-01

    In recent years there has been a rapid increase in the size of video and image databases. Effective searching and retrieving of images from these databases is a significant current research area. In particular, there is a growing interest in query capabilities based on semantic image features such as objects, locations, and materials, known as content-based image retrieval. This study investigated mechanisms for identifying materials present in an image. These capabilities provide additional information impacting conditional probabilities about images (e.g. objects made of steel are more likely to be buildings). These capabilities are useful in Building Information Modeling (BIM) and in automatic enrichment of images. I2T methodologies are a way to enrich an image by generating text descriptions based on image analysis. In this work, a learning model is trained to detect certain materials in images. To train the model, an image dataset was constructed containing single material images of bricks, cloth, grass, sand, stones, and wood. For generalization purposes, an additional set of 50 images containing multiple materials (some not used in training) was constructed. Two different supervised learning classification models were investigated: a single multi-class SVM classifier, and multiple binary SVM classifiers (one per material). Image features included Gabor filter parameters for texture, and color histogram data for RGB components. All classification accuracy scores using the SVM-based method were above 85%. The second model helped in gathering more information from the images since it assigned multiple classes to the images. A framework for the I2T methodology is presented.

  3. Learning Physics by Creating Problems: An Experiment

    CERN Document Server

    Kolarkar, Ameya S

    2016-01-01

    We investigated the effects of student-generated problems on exams. The process was gradual with some training throughout the semester. Initial results were highly positive with the students involved performing significantly better, and showing statistically significant improvement (t = 5.04) compared to the rest of the class, on average. Overall, performance improved when students generated problems. Motivation was a limiting factor. There is significant potential for improving student learning of physics and other problem-based topics.

  4. Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning.

    Science.gov (United States)

    Cole-Lewis, Heather; Varghese, Arun; Sanders, Amy; Schwarz, Mary; Pugatch, Jillian; Augustson, Erik

    2015-08-25

    Electronic cigarettes (e-cigarettes) continue to be a growing topic among social media users, especially on Twitter. The ability to analyze conversations about e-cigarettes in real-time can provide important insight into trends in the public's knowledge, attitudes, and beliefs surrounding e-cigarettes, and subsequently guide public health interventions. Our aim was to establish a supervised machine learning algorithm to build predictive classification models that assess Twitter data for a range of factors related to e-cigarettes. Manual content analysis was conducted for 17,098 tweets. These tweets were coded for five categories: e-cigarette relevance, sentiment, user description, genre, and theme. Machine learning classification models were then built for each of these five categories, and word groupings (n-grams) were used to define the feature space for each classifier. Predictive performance scores for classification models indicated that the models correctly labeled the tweets with the appropriate variables between 68.40% and 99.34% of the time, and the percentage of maximum possible improvement over a random baseline that was achieved by the classification models ranged from 41.59% to 80.62%. Classifiers with the highest performance scores that also achieved the highest percentage of the maximum possible improvement over a random baseline were Policy/Government (performance: 0.94; % improvement: 80.62%), Relevance (performance: 0.94; % improvement: 75.26%), Ad or Promotion (performance: 0.89; % improvement: 72.69%), and Marketing (performance: 0.91; % improvement: 72.56%). The most appropriate word-grouping unit (n-gram) was 1 for the majority of classifiers. Performance continued to marginally increase with the size of the training dataset of manually annotated data, but eventually leveled off. Even at low dataset sizes of 4000 observations, performance characteristics were fairly sound. Social media outlets like Twitter can uncover real-time snapshots of

  5. Attend in groups: a weakly-supervised deep learning framework for learning from web data

    OpenAIRE

    Zhuang, Bohan; Liu, Lingqiao; Li, Yao; Shen, Chunhua; Reid, Ian

    2016-01-01

    Large-scale datasets have driven the rapid development of deep neural networks for visual recognition. However, annotating a massive dataset is expensive and time-consuming. Web images and their labels are, in comparison, much easier to obtain, but direct training on such automatically harvested images can lead to unsatisfactory performance, because the noisy labels of Web images adversely affect the learned recognition models. To address this drawback we propose an end-to-end weakly-supervis...

  6. Creative Problem Solving as a Learning Process

    Directory of Open Access Journals (Sweden)

    Andreas Ninck

    2013-12-01

    Full Text Available The Business School at the Bern University of Applied Sciences is offering a new MScBA degree program in business development. The paper presents a practical report about the action learning approach in the course 'Business Analysis and Design'. Our problem-based approach is more than simply 'learning by doing'. In a world of increasing complexity, taking action alone will not result in a learning effect per se. What is imperative is to structure and facilitate the learning process on different levels: individual construction of mental models; understanding needs and developing adequate solutions; critical reflection of methods and processes. Reflective practice, where individuals are learning from their own professional experiences rather than from formal teaching or knowledge transfer, may be the most important source for lifelong learning.

  7. Evaluation of Four Supervised Learning Methods for Benthic Habitat Mapping Using Backscatter from Multi-Beam Sonar

    Directory of Open Access Journals (Sweden)

    Jacquomo Monk

    2012-11-01

    Full Text Available An understanding of the distribution and extent of marine habitats is essential for the implementation of ecosystem-based management strategies. Historically this had been difficult in marine environments until the advancement of acoustic sensors. This study demonstrates the applicability of supervised learning techniques for benthic habitat characterization using angular backscatter response data. With the advancement of multibeam echo-sounder (MBES technology, full coverage datasets of physical structure over vast regions of the seafloor are now achievable. Supervised learning methods typically applied to terrestrial remote sensing provide a cost-effective approach for habitat characterization in marine systems. However the comparison of the relative performance of different classifiers using acoustic data is limited. Characterization of acoustic backscatter data from MBES using four different supervised learning methods to generate benthic habitat maps is presented. Maximum Likelihood Classifier (MLC, Quick, Unbiased, Efficient Statistical Tree (QUEST, Random Forest (RF and Support Vector Machine (SVM were evaluated to classify angular backscatter response into habitat classes using training data acquired from underwater video observations. Results for biota classifications indicated that SVM and RF produced the highest accuracies, followed by QUEST and MLC, respectively. The most important backscatter data were from the moderate incidence angles between 30° and 50°. This study presents initial results for understanding how acoustic backscatter from MBES can be optimized for the characterization of marine benthic biological habitats.

  8. Cognitive Agents and Learning Problems

    Directory of Open Access Journals (Sweden)

    Goran Zaharija

    2017-03-01

    Full Text Available Goals, Operators, Methods, and Selection rules (GOMS model is a widely recognised concept in Human-Computer Interaction (HCI. Since the initial idea, several GOMS techniques were developed that were used for analysis, differing in their form defined by the logical structure and prediction power. Through defined operators and methods and following the certain rules, the user can reach a specific goal. This work represents an effort to apply GOMS method in the field of artificial intelligence, specifically on a state-space search problems. Card, Morgan, Newman GOMS (CMNGOMS model has been chosen, since it represents ground-floor of the GOMS idea that solves the given task through a sequence of operators. Compared with the informed search algorithms for solving the given task, CMN-GOMS model gave better results. Moreover, it was shown that this model could be used in any other space motion problem in the natural environment. LEGO® MINDSTORMS® EV3 robot was used to demonstrate the application of GOMS model in real world pathfinding problems and as a test-bed for comparing proposed model with well-known search algorithms.

  9. Multiple-instance learning as a classifier combining problem

    DEFF Research Database (Denmark)

    Li, Yan; Tax, David M. J.; Duin, Robert P. W.

    2013-01-01

    In multiple-instance learning (MIL), an object is represented as a bag consisting of a set of feature vectors called instances. In the training set, the labels of bags are given, while the uncertainty comes from the unknown labels of instances in the bags. In this paper, we study MIL with the ass......In multiple-instance learning (MIL), an object is represented as a bag consisting of a set of feature vectors called instances. In the training set, the labels of bags are given, while the uncertainty comes from the unknown labels of instances in the bags. In this paper, we study MIL...... with the assumption that instances are drawn from a mixture distribution of the concept and the non-concept, which leads to a convenient way to solve MIL as a classifier combining problem. It is shown that instances can be classified with any standard supervised classifier by re-weighting the classification...... posteriors. Given the instance labels, the label of a bag can be obtained as a classifier combining problem. An optimal decision rule is derived that determines the threshold on the fraction of instances in a bag that is assigned to the concept class. We provide estimators for the two parameters in the model...

  10. 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.

  11. 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.

  12. 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...

  13. 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...

  14. Using isomorphic problems to learn introductory physics

    Directory of Open Access Journals (Sweden)

    Shih-Yin Lin

    2011-08-01

    Full Text Available In this study, we examine introductory physics students’ ability to perform analogical reasoning between two isomorphic problems which employ the same underlying physics principles but have different surface features. Three hundred sixty-two students from a calculus-based and an algebra-based introductory physics course were given a quiz in the recitation in which they had to first learn from a solved problem provided and take advantage of what they learned from it to solve another problem (which we call the quiz problem which was isomorphic. Previous research suggests that the multiple-concept quiz problem is challenging for introductory students. Students in different recitation classes received different interventions in order to help them discern and exploit the underlying similarities of the isomorphic solved and quiz problems. We also conducted think-aloud interviews with four introductory students in order to understand in depth the difficulties they had and explore strategies to provide better scaffolding. We found that most students were able to learn from the solved problem to some extent with the scaffolding provided and invoke the relevant principles in the quiz problem. However, they were not necessarily able to apply the principles correctly. Research suggests that more scaffolding is needed to help students in applying these principles appropriately. We outline a few possible strategies for future investigation.

  15. Kollegial supervision

    DEFF Research Database (Denmark)

    Andersen, Ole Dibbern; Petersson, Erling

    Publikationen belyser, hvordan kollegial supervision i en kan organiseres i en uddannelsesinstitution......Publikationen belyser, hvordan kollegial supervision i en kan organiseres i en uddannelsesinstitution...

  16. Problem-Based Learning in Formal and Informal Learning Environments

    Science.gov (United States)

    Shimic, Goran; Jevremovic, Aleksandar

    2012-01-01

    Problem-based learning (PBL) is a student-centered instructional strategy in which students solve problems and reflect on their experiences. Different domains need different approaches in the design of PBL systems. Therefore, we present one case study in this article: A Java Programming PBL. The application is developed as an additional module for…

  17. Problem-Based Learning in Formal and Informal Learning Environments

    Science.gov (United States)

    Shimic, Goran; Jevremovic, Aleksandar

    2012-01-01

    Problem-based learning (PBL) is a student-centered instructional strategy in which students solve problems and reflect on their experiences. Different domains need different approaches in the design of PBL systems. Therefore, we present one case study in this article: A Java Programming PBL. The application is developed as an additional module for…

  18. Supervision of two fundamental problems in modern fluid dynamics for sparing energy

    Institute of Scientific and Technical Information of China (English)

    Jurgen Zierep

    2009-01-01

    The normal shock at a curved surface in transonic flow leads to a superdetermined boundary value problem. A bump behind the shock decreases the drag of the airfoil and reduces the necessary energy.Flows of non-Newtonian media lead in the contrary to subdetermined boundary initial problems. The energy bal-ance for these fluids is of great interest for chemical engineering.

  19. SU-E-J-107: Supervised Learning Model of Aligned Collagen for Human Breast Carcinoma Prognosis

    Energy Technology Data Exchange (ETDEWEB)

    Bredfeldt, J; Liu, Y; Conklin, M; Keely, P; Eliceiri, K; Mackie, T [University of Wisconsin, Madison, WI (United States)

    2014-06-01

    Purpose: Our goal is to develop and apply a set of optical and computational tools to enable large-scale investigations of the interaction between collagen and tumor cells. Methods: We have built a novel imaging system for automating the capture of whole-slide second harmonic generation (SHG) images of collagen in registry with bright field (BF) images of hematoxylin and eosin stained tissue. To analyze our images, we have integrated a suite of supervised learning tools that semi-automatically model and score collagen interactions with tumor cells via a variety of metrics, a method we call Electronic Tumor Associated Collagen Signatures (eTACS). This group of tools first segments regions of epithelial cells and collagen fibers from BF and SHG images respectively. We then associate fibers with groups of epithelial cells and finally compute features based on the angle of interaction and density of the collagen surrounding the epithelial cell clusters. These features are then processed with a support vector machine to separate cancer patients into high and low risk groups. Results: We validated our model by showing that eTACS produces classifications that have statistically significant correlation with manual classifications. In addition, our system generated classification scores that accurately predicted breast cancer patient survival in a cohort of 196 patients. Feature rank analysis revealed that TACS positive fibers are more well aligned with each other, generally lower density, and terminate within or near groups of epithelial cells. Conclusion: We are working to apply our model to predict survival in larger cohorts of breast cancer patients with a diversity of breast cancer types, predict response to treatments such as COX2 inhibitors, and to study collagen architecture changes in other cancer types. In the future, our system may be used to provide metastatic potential information to cancer patients to augment existing clinical assays.

  20. Investigating the control of climatic oscillations over global terrestrial evaporation using a simple supervised learning method

    Science.gov (United States)

    Martens, Brecht; Miralles, Diego; Waegeman, Willem; Dorigo, Wouter; Verhoest, Niko

    2017-04-01

    Intra-annual and multi-decadal variations in the Earth's climate are to a large extent driven by periodic oscillations in the coupled state of atmosphere and ocean. These oscillations alter not only the climate in nearby regions, but also have an important impact on the local climate in remote areas, a phenomenon that is often referred to as 'teleconnection'. Because changes in local climate immediately impact terrestrial ecosystems through a series of complex processes and feedbacks, ocean-atmospheric teleconnections are expected to influence land evaporation - i.e. the return flux of water from land to atmosphere. In this presentation, the effects of these intra-annual and multi-decadal climate oscillations on global terrestrial evaporation are analysed. To this end, we use satellite observations of different essential climate variables in combination with a simple supervised learning method, the lasso regression. A total of sixteen Climate Oscillation Indices (COIs) - which are routinely used to diagnose the major ocean-atmospheric oscillations - are selected. Multi-decadal data of terrestrial evaporation are retrieved from the Global Land Evaporation Amsterdam Model (GLEAM, www.gleam.eu). Using the lasso regression, it is shown that more than 30% of the inter-annual variations in terrestrial evaporation can be explained by ocean-atmospheric oscillations. In addition, the impact in different regions across the globe can typically be attributed to a small subset of the sixteen COIs. For instance, the dynamics in terrestrial evaporation over Australia are substantially impacted by both the El Niño Southern Oscillation (here diagnosed using the Southern Oscillation Index, SOI) and the Indian Ocean Dipole Oscillation (here diagnosed using the Indian Dipole Mode Index, DMI). Subsequently, using the same learning method but regressing terrestrial evaporation to its local climatic drivers (air temperature, precipitation, radiation), allows us to discern through which

  1. Hypothetical Pattern Recognition Design Using Multi-Layer Perceptorn Neural Network For Supervised Learning

    Directory of Open Access Journals (Sweden)

    Md. Abdullah-al-mamun

    2015-08-01

    Full Text Available Abstract Humans are capable to identifying diverse shape in the different pattern in the real world as effortless fashion due to their intelligence is grow since born with facing several learning process. Same way we can prepared an machine using human like brain called Artificial Neural Network that can be recognize different pattern from the real world object. Although the various techniques is exists to implementation the pattern recognition but recently the artificial neural network approaches have been giving the significant attention. Because the approached of artificial neural network is like a human brain that is learn from different observation and give a decision the previously learning rule. Over the 50 years research now a days pattern recognition for machine learning using artificial neural network got a significant achievement. For this reason many real world problem can be solve by modeling the pattern recognition process. The objective of this paper is to present the theoretical concept for pattern recognition design using Multi-Layer Perceptorn neural networkin the algorithm of artificial Intelligence as the best possible way of utilizing available resources to make a decision that can be a human like performance.

  2. Improving Achievement through Problem-Based Learning

    Science.gov (United States)

    Sungur, Semra; Tekkaya, Ceren; Geban, Omer

    2006-01-01

    In this study, the effect of problem-based learning on students' academic achievement and performance skills in a unit on the human excretory system was investigated. Sixty-one 10th grade students, from two full classes instructed by the same biology teacher, were involved in the study. Classes were randomly assigned as either the experimental or…

  3. Unlearning, Critical Action Learning and Wicked Problems

    Science.gov (United States)

    Pedler, Mike; Hsu, Shih-wei

    2014-01-01

    This paper explores the idea of unlearning in Critical Action Learning (CAL) as applied to the wicked problems of organisations and societies. It draws on data and ideas developed during a research project conducted for "Skills for Care" by Pedler, Abbott, Brook and Burgoyne ("Skills for Care" 2014) and from experiences on…

  4. Power, Democracy and Problem-Based Learning

    DEFF Research Database (Denmark)

    Du, Xiangyun; Stentoft, Diana; Dahms, Mona-Lisa

    2007-01-01

      Problem Based Learning (PBL) as an educational approach has been increasingly applied in educational settings around the world. Given that PBL - as well as any other educational approach - is rooted in a given cultural context and thus carries the ‘fingerprint' of the specific context, an inter...

  5. Staff Development in Problem-based Learning.

    Science.gov (United States)

    Murray, Ian; Savin-Baden, Maggi

    2000-01-01

    Argues that, for problem-based learning (PBL) to succeed, a sound program of staff development is required and describes the introduction of PBL into Nursing and Midwifery curricula at the University of Dundee (Scotland) including key components and evaluatory evidence to support its efficacy. (DB)

  6. Producing and Scrounging during Problem Based Learning

    Science.gov (United States)

    Vickery, William L.

    2013-01-01

    When problem based learning occurs in a social context it is open to a common social behaviour, scrounging. In the animal behaviour literature, scroungers do not attempt to find resources themselves but rather exploit resources found by other group members (referred to as producers). We know from studies of animal behaviour (including humans) that…

  7. Problem Based Learning in Engineering Education

    DEFF Research Database (Denmark)

    Dahms, Mona-Lisa; Sauerbier, Gabriele; Stubbe, Korinna

    2006-01-01

    This paper describes a recent EU-project from five European Institutions. The aim was the development and implementation of a new international Master’s programme for staff development, directed towards the introduction of Problem Based Learning methods in the field of engineering education...

  8. Learning Matlab a problem solving approach

    CERN Document Server

    Gander, Walter

    2015-01-01

    This comprehensive and stimulating introduction to Matlab, a computer language now widely used for technical computing, is based on an introductory course held at Qian Weichang College, Shanghai University, in the fall of 2014.  Teaching and learning a substantial programming language aren’t always straightforward tasks. Accordingly, this textbook is not meant to cover the whole range of this high-performance technical programming environment, but to motivate first- and second-year undergraduate students in mathematics and computer science to learn Matlab by studying representative problems, developing algorithms and programming them in Matlab. While several topics are taken from the field of scientific computing, the main emphasis is on programming. A wealth of examples are completely discussed and solved, allowing students to learn Matlab by doing: by solving problems, comparing approaches and assessing the proposed solutions.

  9. Supervised Learning Approach for Spam Classification Analysis using Data Mining Tools

    Directory of Open Access Journals (Sweden)

    R.Deepa Lakshmi

    2010-12-01

    Full Text Available E-mail is one of the most popular and frequently used ways of communication due to its worldwide accessibility, relatively fast message transfer, and low sending cost. The flaws in the e-mail protocols and the increasing amount of electronic business and financial transactions directly contribute to the increase in e-mail-based threats. Email spam is one of the major problems of the today’s Internet, bringing financial damage to companies and annoying individual users. Among the approaches developed to stop spam, filtering is the one of the most important technique. Many researches in spam filtering have been centered on the more sophisticated classifierrelated issues. In recent days, Machine learning for spamclassification is an important research issue. This paper exploresand identifies the use of different learning algorithms for classifying spam messages from e-mail. A comparative analysisamong the algorithms has also been presented.

  10. Supervised Learning Approach for Spam Classification Analysis using Data Mining Tools

    Directory of Open Access Journals (Sweden)

    R.Deepa Lakshmi

    2010-11-01

    Full Text Available E-mail is one of the most popular and frequently used ways of communication due to its worldwide accessibility, relatively fast message transfer, and low sending cost. The flaws in the e-mail protocols and the increasing amount of electronic business and financial transactions directly contribute to the increase in e-mail-based threats. Email spam is one of the major problems of the today’s Internet, bringing financial damage to companies and annoying individual users. Among the approaches developed to stop spam, filtering is the one of the most important technique. Many researches in spam filtering have been centered on the more sophisticated classifierrelated issues. In recent days, Machine learning for spamclassification is an important research issue. This paper exploresand identifies the use of different learning algorithms for classifying spam messages from e-mail. A comparative analysisamong the algorithms has also been presented.

  11. Producing and scrounging during Problem Based Learning

    Directory of Open Access Journals (Sweden)

    William L. Vickery

    2013-08-01

    Full Text Available When problem based learning occurs in a social context it is open to a common social behaviour, scrounging. In the animal behaviour literature, scroungers do not attempt to find resources themselves but rather exploit resources found by other group members (referred to as producers. We know from studies of animal behaviour (including humans that scrounging can be expected whenever animals exploit resources in groups. We also know that scrounging can have deleterious effects on the group. We can expect scrounging to occur during social learning because the exchange of information (which I will consider here as a resource is essential to social learning. This exchange can be seen as each individual scrounging from the other members of the group whenever the individual learns from the work of others. However, there is a danger if some individuals learn mostly through their own efforts while others indulge in “social loafing” relying heavily on colleagues to provide knowledge. Here I propose that game theory models developed to analyse feeding in animal societies may also apply to social learning. We know from studies of birds feeding in groups that scrounging behaviour depends on the extent to which resources can be shared. Further, when scrounging is prevalent groups tend to obtain fewer resources. By contrast, in social learning we attempt to facilitate sharing of knowledge. We thus encourage scrounging and run the risk of reducing learning within study groups. Here I analyse the role of scrounging in problem based learning. I argue that scrounging is inherent and necessary to any social learning process. However, it can have perverse effects if the acquisition of facts rather than understanding comes to dominate learning objectives. Further, disparities among individuals within a group can lead certain individuals to specialise in scrounging thus undermining the functioning of the group. I suggest that motivation, problem structure

  12. Distance learning, problem based learning and dynamic knowledge networks.

    Science.gov (United States)

    Giani, U; Martone, P

    1998-06-01

    This paper is an attempt to develop a distance learning model grounded upon a strict integration of problem based learning (PBL), dynamic knowledge networks (DKN) and web tools, such as hypermedia documents, synchronous and asynchronous communication facilities, etc. The main objective is to develop a theory of distance learning based upon the idea that learning is a highly dynamic cognitive process aimed at connecting different concepts in a network of mutually supporting concepts. Moreover, this process is supposed to be the result of a social interaction that has to be facilitated by the web. The model was tested by creating a virtual classroom of medical and nursing students and activating a learning session on the concept of knowledge representation in health sciences.

  13. Problem-Based Learning in Higher Education: Untold Stories.

    Science.gov (United States)

    Savin-Baden, Maggi

    The central argument of this book is that the potential of problem-based learning is yet to be realized in higher education. Problem-based learning is an important approach to learning, based in the experiential learning tradition, that needs to be more centrally located in higher education curricula. Part 1 of this book explores problem-based…

  14. Comparison of supervised machine learning algorithms for waterborne pathogen detection using mobile phone fluorescence microscopy

    Science.gov (United States)

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

    2017-06-01

    Giardia lamblia is a waterborne parasite that affects millions of people every year worldwide, causing a diarrheal illness known as giardiasis. Timely detection of the presence of the cysts of this parasite in drinking water is important to prevent the spread of the disease, especially in resource-limited settings. Here we provide extended experimental testing and evaluation of the performance and repeatability of a field-portable and cost-effective microscopy platform for automated detection and counting of Giardia cysts in water samples, including tap water, non-potable water, and pond water. This compact platform is based on our previous work, and is composed of a smartphone-based fluorescence microscope, a disposable sample processing cassette, and a custom-developed smartphone application. Our mobile phone microscope has a large field of view of 0.8 cm2 and weighs only 180 g, excluding the phone. A custom-developed smartphone application provides a user-friendly graphical interface, guiding the users to capture a fluorescence image of the sample filter membrane and analyze it automatically at our servers using an image processing algorithm and training data, consisting of >30,000 images of cysts and >100,000 images of other fluorescent particles that are captured, including, e.g. dust. The total time that it takes from sample preparation to automated cyst counting is less than an hour for each 10 ml of water sample that is tested. We compared the sensitivity and the specificity of our platform using multiple supervised classification models, including support vector machines and nearest neighbors, and demonstrated that a bootstrap aggregating (i.e. bagging) approach using raw image file format provides the best performance for automated detection of Giardia cysts. We evaluated the performance of this machine learning enabled pathogen detection device with water samples taken from different sources (e.g. tap water, non-potable water, pond water) and achieved a

  15. Comparison of supervised machine learning algorithms for waterborne pathogen detection using mobile phone fluorescence microscopy

    Directory of Open Access Journals (Sweden)

    Ceylan Koydemir Hatice

    2017-06-01

    Full Text Available Giardia lamblia is a waterborne parasite that affects millions of people every year worldwide, causing a diarrheal illness known as giardiasis. Timely detection of the presence of the cysts of this parasite in drinking water is important to prevent the spread of the disease, especially in resource-limited settings. Here we provide extended experimental testing and evaluation of the performance and repeatability of a field-portable and cost-effective microscopy platform for automated detection and counting of Giardia cysts in water samples, including tap water, non-potable water, and pond water. This compact platform is based on our previous work, and is composed of a smartphone-based fluorescence microscope, a disposable sample processing cassette, and a custom-developed smartphone application. Our mobile phone microscope has a large field of view of ~0.8 cm2 and weighs only ~180 g, excluding the phone. A custom-developed smartphone application provides a user-friendly graphical interface, guiding the users to capture a fluorescence image of the sample filter membrane and analyze it automatically at our servers using an image processing algorithm and training data, consisting of >30,000 images of cysts and >100,000 images of other fluorescent particles that are captured, including, e.g. dust. The total time that it takes from sample preparation to automated cyst counting is less than an hour for each 10 ml of water sample that is tested. We compared the sensitivity and the specificity of our platform using multiple supervised classification models, including support vector machines and nearest neighbors, and demonstrated that a bootstrap aggregating (i.e. bagging approach using raw image file format provides the best performance for automated detection of Giardia cysts. We evaluated the performance of this machine learning enabled pathogen detection device with water samples taken from different sources (e.g. tap water, non-potable water, pond

  16. Comparison of supervised machine learning algorithms for waterborne pathogen detection using mobile phone fluorescence microscopy

    KAUST Repository

    Ceylan Koydemir, Hatice

    2017-06-14

    Giardia lamblia is a waterborne parasite that affects millions of people every year worldwide, causing a diarrheal illness known as giardiasis. Timely detection of the presence of the cysts of this parasite in drinking water is important to prevent the spread of the disease, especially in resource-limited settings. Here we provide extended experimental testing and evaluation of the performance and repeatability of a field-portable and cost-effective microscopy platform for automated detection and counting of Giardia cysts in water samples, including tap water, non-potable water, and pond water. This compact platform is based on our previous work, and is composed of a smartphone-based fluorescence microscope, a disposable sample processing cassette, and a custom-developed smartphone application. Our mobile phone microscope has a large field of view of ~0.8 cm2 and weighs only ~180 g, excluding the phone. A custom-developed smartphone application provides a user-friendly graphical interface, guiding the users to capture a fluorescence image of the sample filter membrane and analyze it automatically at our servers using an image processing algorithm and training data, consisting of >30,000 images of cysts and >100,000 images of other fluorescent particles that are captured, including, e.g. dust. The total time that it takes from sample preparation to automated cyst counting is less than an hour for each 10 ml of water sample that is tested. We compared the sensitivity and the specificity of our platform using multiple supervised classification models, including support vector machines and nearest neighbors, and demonstrated that a bootstrap aggregating (i.e. bagging) approach using raw image file format provides the best performance for automated detection of Giardia cysts. We evaluated the performance of this machine learning enabled pathogen detection device with water samples taken from different sources (e.g. tap water, non-potable water, pond water) and achieved

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

    Science.gov (United States)

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

    2017-07-01

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

  18. Use of auditory learning to manage listening problems in children

    National Research Council Canada - National Science Library

    David R Moore; Lorna F Halliday; Sygal Amitay

    2009-01-01

    .... It considers the auditory contribution to developmental listening and language problems and the underlying principles of auditory learning that may drive further refinement of auditory learning applications...

  19. Concept mapping as learning tool in problem-oriented learning

    NARCIS (Netherlands)

    Fürstenau, B.; Kneppers, L.; Sánchez, J.; Cañas, A.J.; Novak, J.D.

    2010-01-01

    In two studies we investigated whether concept mapping or summary writing is more effective in supporting students’ learning from authentic problems in the field of business. We interpret concept mapping and summary writing as elaboration tools aiming at helping students to understand new informatio

  20. Don't Leave Teaching to Chance: Learning Objectives for Psychodynamic Psychotherapy Supervision

    Science.gov (United States)

    Rojas, Alicia; Arbuckle, Melissa; Cabaniss, Deborah

    2010-01-01

    Objective: The way in which the competencies for psychodynamic psychotherapy specified by the Psychiatry Residency Review Committee of the Accreditation Council for Graduate Medical Education translate into the day-to-day work of individual supervision remains unstudied and unspecified. The authors hypothesized that despite the existence of…

  1. 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.

  2. Fieldwork online: a GIS-based electronic learning environment for supervising fieldwork

    NARCIS (Netherlands)

    Alberti, K.; Marra, W.A.; Baarsma, R.J.; Karssenberg, D.J.

    2016-01-01

    Fieldwork comes in many forms: individual research projects in unique places, large groups of students on organized fieldtrips, and everything in between those extremes. Supervising students in often distant places can be a logistical challenge and requires a significant time investment of their

  3. Enabling Connections in Postgraduate Supervision for an Applied eLearning Professional Development Programme

    Science.gov (United States)

    Donnelly, Roisin

    2013-01-01

    This article describes the practice of postgraduate supervision on a blended professional development programme for academics, and discusses how connectivism has been a useful lens to explore a complex form of instruction. By examining the processes by which supervisors and their students on a two-year part-time masters in Applied eLearning…

  4. An Early Historical Examination of the Educational Intent of Supervised Agricultural Experiences (SAEs) and Project-Based Learning in Agricultural Education

    Science.gov (United States)

    Smith, Kasee L.; Rayfield, John

    2016-01-01

    Project-based learning has been a component of agricultural education since its inception. In light of the current call for additional emphasis of the Supervised Agricultural Experience (SAE) component of agricultural education, there is a need to revisit the roots of project-based learning. This early historical research study was conducted to…

  5. Problem based Learning in surveying Education

    DEFF Research Database (Denmark)

    Enemark, Stig

    The challenge of the future will be that the only constant is change. Therefore, the educational base must be flexible. The graduates must possess skills to adapt to a rapidly changing labour market and they must possess skills to deal with even the unknown problems of the future. The point is......, that professional and technical skills can be acquired and updated at a later stage in ones career while skills for problem-solving and skills for learning to learn can only be achieved through the process of academic training at he universities. The concept of project-organised education provides just...... that opportunity. The basis principles of this educational model are presented using the surveying programme at Aalborg University as an example....

  6. Educational Change towards Problem Based Learning

    DEFF Research Database (Denmark)

    Li, Huichun

    at the university, depending on which strategy the mangers have employed. Further, the organizational members’ understandings of PBL are more concerned with teaching and curriculum design rather than learning, which should be highlighted within PBL context. Besides, staff members have to struggle between different......As a promising educational approach, PBL (Problem Based Learning) has been adopted by an increasing number of higher education institutions worldwide to replace the traditional lectured based educational approach. However, the organizational change towards PBL is not easy for higher education...... institutions, especially for those with a long history of Lecture Based Learning. Therefore, it is necessary to investigate the challenges and obstacles for higher education institutions which are implementing PBL. In order to address the research concern, this book involves in an intensive exploration of two...

  7. 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.

  8. Conscientization, Dialogue and Collaborative Problem Based Learning

    Directory of Open Access Journals (Sweden)

    Conscientization, Dialogue and Collaborative Problem Based Learning

    2013-08-01

    Full Text Available It has been argued that Paulo Freire’s concept of conscientization, where critical awareness and engagement are central to a problem-posing pedagogy, provides the philosophical principles to underpin Problem Based Learning (PBL. By using dialogue groups and a combination of learning strategies to discover the nature of a problem, understand its constraints, options, and multi-voiced perspectives, students can negotiate the sociological nature of its resolution and how competing perspectives may inform decision-making. This paper will first present the background of PBL, before it introduces and argues for reflective and reflexive learning environments founded within dialogical practices. It then provides tales from the field that illustrate how conscientization is enacted in the classroom, before considering implications and the Ten Principles of Critical Learning’ for reflective and reflexive practice. It concludes by arguing that conscientization and the dialogical process are central to PBL in order to engage the individual voice, foster democratic practices, and for the creation of shared meanings and understandings.

  9. Clinical supervision in a community setting.

    Science.gov (United States)

    Evans, Carol; Marcroft, Emma

    Clinical supervision is a formal process of professional support, reflection and learning that contributes to individual development. First Community Health and Care is committed to providing clinical supervision to nurses and allied healthcare professionals to support the provision and maintenance of high-quality care. In 2012, we developed new guidelines for nurses and AHPs on supervision, incorporating a clinical supervision framework. This offers a range of options to staff so supervision accommodates variations in work settings and individual learning needs and styles.

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

    Science.gov (United States)

    Premaladha, J; Ravichandran, K S

    2016-04-01

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

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

    Directory of Open Access Journals (Sweden)

    Che Ngufor

    2013-01-01

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

  12. Development of a Late-Life Dementia Prediction Index with Supervised Machine Learning in the Population-Based CAIDE Study

    Science.gov (United States)

    Pekkala, Timo; Hall, Anette; Lötjönen, Jyrki; Mattila, Jussi; Soininen, Hilkka; Ngandu, Tiia; Laatikainen, Tiina; Kivipelto, Miia; Solomon, Alina

    2016-01-01

    Background and objective: This study aimed to develop a late-life dementia prediction model using a novel validated supervised machine learning method, the Disease State Index (DSI), in the Finnish population-based CAIDE study. Methods: The CAIDE study was based on previous population-based midlife surveys. CAIDE participants were re-examined twice in late-life, and the first late-life re-examination was used as baseline for the present study. The main study population included 709 cognitively normal subjects at first re-examination who returned to the second re-examination up to 10 years later (incident dementia n = 39). An extended population (n = 1009, incident dementia 151) included non-participants/non-survivors (national registers data). DSI was used to develop a dementia index based on first re-examination assessments. Performance in predicting dementia was assessed as area under the ROC curve (AUC). Results: AUCs for DSI were 0.79 and 0.75 for main and extended populations. Included predictors were cognition, vascular factors, age, subjective memory complaints, and APOE genotype. Conclusion: The supervised machine learning method performed well in identifying comprehensive profiles for predicting dementia development up to 10 years later. DSI could thus be useful for identifying individuals who are most at risk and may benefit from dementia prevention interventions. PMID:27802228

  13. Development of a Late-Life Dementia Prediction Index with Supervised Machine Learning in the Population-Based CAIDE Study.

    Science.gov (United States)

    Pekkala, Timo; Hall, Anette; Lötjönen, Jyrki; Mattila, Jussi; Soininen, Hilkka; Ngandu, Tiia; Laatikainen, Tiina; Kivipelto, Miia; Solomon, Alina

    2017-01-01

    This study aimed to develop a late-life dementia prediction model using a novel validated supervised machine learning method, the Disease State Index (DSI), in the Finnish population-based CAIDE study. The CAIDE study was based on previous population-based midlife surveys. CAIDE participants were re-examined twice in late-life, and the first late-life re-examination was used as baseline for the present study. The main study population included 709 cognitively normal subjects at first re-examination who returned to the second re-examination up to 10 years later (incident dementia n = 39). An extended population (n = 1009, incident dementia 151) included non-participants/non-survivors (national registers data). DSI was used to develop a dementia index based on first re-examination assessments. Performance in predicting dementia was assessed as area under the ROC curve (AUC). AUCs for DSI were 0.79 and 0.75 for main and extended populations. Included predictors were cognition, vascular factors, age, subjective memory complaints, and APOE genotype. The supervised machine learning method performed well in identifying comprehensive profiles for predicting dementia development up to 10 years later. DSI could thus be useful for identifying individuals who are most at risk and may benefit from dementia prevention interventions.

  14. A problem-based learning curriculum and undergraduate ...

    African Journals Online (AJOL)

    learning.[3] Central to the approach is the use of problems, which are posed to .... collected through surveys or interviews soliciting information from .... McParland M, Noble ML, Livingston G. The effectiveness of problem based learning.

  15. Cloud detection in all-sky images via multi-scale neighborhood features and multiple supervised learning techniques

    Science.gov (United States)

    Cheng, Hsu-Yung; Lin, Chih-Lung

    2017-01-01

    Cloud detection is important for providing necessary information such as cloud cover in many applications. Existing cloud detection methods include red-to-blue ratio thresholding and other classification-based techniques. In this paper, we propose to perform cloud detection using supervised learning techniques with multi-resolution features. One of the major contributions of this work is that the features are extracted from local image patches with different sizes to include local structure and multi-resolution information. The cloud models are learned through the training process. We consider classifiers including random forest, support vector machine, and Bayesian classifier. To take advantage of the clues provided by multiple classifiers and various levels of patch sizes, we employ a voting scheme to combine the results to further increase the detection accuracy. In the experiments, we have shown that the proposed method can distinguish cloud and non-cloud pixels more accurately compared with existing works.

  16. Poster abstract: Water level estimation in urban ultrasonic/passive infrared flash flood sensor networks using supervised learning

    KAUST Repository

    Mousa, Mustafa

    2014-04-01

    This article describes a machine learning approach to water level estimation in a dual ultrasonic/passive infrared urban flood sensor system. We first show that an ultrasonic rangefinder alone is unable to accurately measure the level of water on a road due to thermal effects. Using additional passive infrared sensors, we show that ground temperature and local sensor temperature measurements are sufficient to correct the rangefinder readings and improve the flood detection performance. Since floods occur very rarely, we use a supervised learning approach to estimate the correction to the ultrasonic rangefinder caused by temperature fluctuations. Preliminary data shows that water level can be estimated with an absolute error of less than 2 cm. © 2014 IEEE.

  17. 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

  18. Cavity contour segmentation in chest radiographs using supervised learning and dynamic programming

    Energy Technology Data Exchange (ETDEWEB)

    Maduskar, Pragnya, E-mail: pragnya.maduskar@radboudumc.nl; Hogeweg, Laurens; Sánchez, Clara I.; Ginneken, Bram van [Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, 6525 GA (Netherlands); Jong, Pim A. de [Department of Radiology, University Medical Center Utrecht, 3584 CX (Netherlands); Peters-Bax, Liesbeth [Department of Radiology, Radboud University Medical Center, Nijmegen, 6525 GA (Netherlands); Dawson, Rodney [University of Cape Town Lung Institute, Cape Town 7700 (South Africa); Ayles, Helen [Department of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London WC1E 7HT (United Kingdom)

    2014-07-15

    Purpose: Efficacy of tuberculosis (TB) treatment is often monitored using chest radiography. Monitoring size of cavities in pulmonary tuberculosis is important as the size predicts severity of the disease and its persistence under therapy predicts relapse. The authors present a method for automatic cavity segmentation in chest radiographs. Methods: A two stage method is proposed to segment the cavity borders, given a user defined seed point close to the center of the cavity. First, a supervised learning approach is employed to train a pixel classifier using texture and radial features to identify the border pixels of the cavity. A likelihood value of belonging to the cavity border is assigned to each pixel by the classifier. The authors experimented with four different classifiers:k-nearest neighbor (kNN), linear discriminant analysis (LDA), GentleBoost (GB), and random forest (RF). Next, the constructed likelihood map was used as an input cost image in the polar transformed image space for dynamic programming to trace the optimal maximum cost path. This constructed path corresponds to the segmented cavity contour in image space. Results: The method was evaluated on 100 chest radiographs (CXRs) containing 126 cavities. The reference segmentation was manually delineated by an experienced chest radiologist. An independent observer (a chest radiologist) also delineated all cavities to estimate interobserver variability. Jaccard overlap measure Ω was computed between the reference segmentation and the automatic segmentation; and between the reference segmentation and the independent observer's segmentation for all cavities. A median overlap Ω of 0.81 (0.76 ± 0.16), and 0.85 (0.82 ± 0.11) was achieved between the reference segmentation and the automatic segmentation, and between the segmentations by the two radiologists, respectively. The best reported mean contour distance and Hausdorff distance between the reference and the automatic segmentation were

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

    Science.gov (United States)

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

    2011-12-01

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

  20. Structured collaboration versus individual learning in solving physics problems

    NARCIS (Netherlands)

    Harskamp, Egbert; Ding, Ning

    2006-01-01

    The research issue in this study is how to structure collaborative learning so that it improves solving physics problems more than individual learning. Structured collaborative learning has been compared with individual learning environments with Schoenfeld's problem-solving episodes. Students took

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

    Institute of Scientific and Technical Information of China (English)

    申传刚; 马红宇; 杨璟; 刘腾飞

    2012-01-01

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

  2. PROBLEMS OF FORMATION OF OPTIMAL MODEL OF GOVERNMENTAL REGULATION AND SUPERVISION IN THE FINANCIAL SECTOR OF UKRAINE IN THE CONTEXT OF CREDIT COOPERATION

    Directory of Open Access Journals (Sweden)

    Oleksandra OSADETS

    2016-07-01

    Full Text Available In the presented scientific practical research we considered and explored the role and problems of state regulation and supervision of the financial sector of Ukraine, in particular of credit unions because they are inalienable participants in the financial market. In current conditions that have been established during the inancial crisis, one of the most urgent problems is the effective functioning of the regulation and supervision of the financial sector. The crisis that negatively affected the activities of financial sector participants discovered the discrepancy of regulation and supervision of them, which caused the negative trend of development of financial market of Ukraine, as evidenced by official data of the National Commission, which performs state regulation in the sphere of financial services. In the presented scientific practical research we identified the strengths and weaknesses of the modern system of state regulation and supervision in the financial sector of Ukraine, particularly in the sector of credit cooperation, and we formed suggestions on improving its model in the context of the concentration of regulatory and supervisory efforts in state financial regulators

  3. PROBLEMS OF FORMATION OF OPTIMAL MODEL OF GOVERNMENTAL REGULATION AND SUPERVISION IN THE FINANCIAL SECTOR OF UKRAINE IN THE CONTEXT OF CREDIT COOPERATION

    Directory of Open Access Journals (Sweden)

    Oleksandra OSADETS

    2016-07-01

    Full Text Available In the presented scientific practical research we considered and explored the role and problems of state regulation and supervision of the financial sector of Ukraine, in particular of credit unions because they are inalienable participants in the financial market. In current conditions that have been established during the financial crisis, one of the most urgent problems is the effective functioning of the regulation and supervision of the financial sector. The crisis that negatively affected the activities of financial sector participants discovered the discrepancy of regulation and supervision of them, which caused the negative trend of development of financial market of Ukraine, as evidenced by official data of the National Commission, which performs state regulation in the sphere of financial services. In the presented scientific practical research we identified the strengths and weaknesses of the modern system of state regulation and supervision in the financial sector of Ukraine, particularly in the sector of credit cooperation, and we formed suggestions on improving its model in the context of the concentration of regulatory and supervisory efforts in state financial regulators.

  4. Development of Learning Resources to Promote Knowledge Sharing in Problem Based Learning

    Science.gov (United States)

    Uden, Lorna; Page, Tom

    2008-01-01

    Problem Based Learning offers many benefits to students' learning, however, the design and implementation of effective problem based learning (PBL) is not trivial. Central to effective implementation of PBL are the problem design and group working of the students. Design of good problems requires that the learning outcomes of the subject are…

  5. Learning via problem solving in mathematics education

    Directory of Open Access Journals (Sweden)

    Piet Human

    2009-09-01

    Full Text Available Three forms of mathematics education at school level are distinguished: direct expository teaching with an emphasis on procedures, with the expectation that learners will at some later stage make logical and functional sense of what they have learnt and practised (the prevalent form, mathematically rigorous teaching in terms of fundamental mathematical concepts, as in the so-called “modern mathematics” programmes of the sixties, teaching and learning in the context of engaging with meaningful problems and focused both on learning to become good problem solvers (teaching for problem solving andutilising problems as vehicles for the development of mathematical knowledge andproficiency by learners (problem-centred learning, in conjunction with substantialteacher-led social interaction and mathematical discourse in classrooms.Direct expository teaching of mathematical procedures dominated in school systems after World War II, and was augmented by the “modern mathematics” movement in the period 1960-1970. The latter was experienced as a major failure, and was soon abandoned. Persistent poor outcomes of direct expository procedural teaching of mathematics for the majority of learners, as are still being experienced in South Africa, triggered a world-wide movement promoting teaching mathematics for and via problem solving in the seventies and eighties of the previous century. This movement took the form of a variety of curriculum experiments in which problem solving was the dominant classroom activity, mainly in the USA, Netherlands, France and South Africa. While initially focusing on basic arithmetic (computation with whole numbers and elementary calculus, the problem-solving movement started to address other mathematical topics (for example, elementary statistics, algebra, differential equations around the turn of the century. The movement also spread rapidly to other countries, including Japan, Singapore and Australia. Parallel with the

  6. Deep and surface learning in problem-based learning: a review of the literature

    NARCIS (Netherlands)

    D.H.J.M. Dolmans (Diana); S.M.M. Loyens (Sofie); H. Marcq (Hélène); D. Gijbels (David)

    2015-01-01

    textabstractIn problem-based learning (PBL), implemented worldwide, students learn by discussing professionally relevant problems enhancing application and integration of knowledge, which is assumed to encourage students towards a deep learning approach in which students are intrinsically interested

  7. Deep and surface learning in problem-based learning: a review of the literature

    NARCIS (Netherlands)

    D.H.J.M. Dolmans (Diana); S.M.M. Loyens (Sofie); Marcq, H. (Hélène); D. Gijbels (David)

    2016-01-01

    textabstractIn problem-based learning (PBL), implemented worldwide, students learn by discussing professionally relevant problems enhancing application and integration of knowledge, which is assumed to encourage students towards a deep learning approach in which students are intrinsically interested

  8. Educational Change towards Problem Based Learning

    DEFF Research Database (Denmark)

    Li, Huichun

    As a promising educational approach, PBL (Problem Based Learning) has been adopted by an increasing number of higher education institutions worldwide to replace the traditional lectured based educational approach. However, the organizational change towards PBL is not easy for higher education...... institutions, especially for those with a long history of Lecture Based Learning. Therefore, it is necessary to investigate the challenges and obstacles for higher education institutions which are implementing PBL. In order to address the research concern, this book involves in an intensive exploration of two...... universities which are transforming their traditional educational approaches to PBL. Specifically, this book is concerned with how managers, staff members, and students interpret PBL and its implementation. It reveals that the challenges for implementing PBL are closely linked to organizational members...

  9. Adaptation and validation of the instrument Clinical Learning Environment and Supervision for medical students in primary health care.

    Science.gov (United States)

    Öhman, Eva; Alinaghizadeh, Hassan; Kaila, Päivi; Hult, Håkan; Nilsson, Gunnar H; Salminen, Helena

    2016-12-01

    Clinical learning takes place in complex socio-cultural environments that are workplaces for the staff and learning places for the students. In the clinical context, the students learn by active participation and in interaction with the rest of the community at the workplace. Clinical learning occurs outside the university, therefore is it important for both the university and the student that the student is given opportunities to evaluate the clinical placements with an instrument that allows evaluation from many perspectives. The instrument Clinical Learning Environment and Supervision (CLES) was originally developed for evaluation of nursing students' clinical learning environment. The aim of this study was to adapt and validate the CLES instrument to measure medical students' perceptions of their learning environment in primary health care. In the adaptation process the face validity was tested by an expert panel of primary care physicians, who were also active clinical supervisors. The adapted CLES instrument with 25 items and six background questions was sent electronically to 1,256 medical students from one university. Answers from 394 students were eligible for inclusion. Exploratory factor analysis based on principal component methods followed by oblique rotation was used to confirm the adequate number of factors in the data. Construct validity was assessed by factor analysis. Confirmatory factor analysis was used to confirm the dimensions of CLES instrument. The construct validity showed a clearly indicated four-factor model. The cumulative variance explanation was 0.65, and the overall Cronbach's alpha was 0.95. All items loaded similarly with the dimensions in the non-adapted CLES except for one item that loaded to another dimension. The CLES instrument in its adapted form had high construct validity and high reliability and internal consistency. CLES, in its adapted form, appears to be a valid instrument to evaluate medical students' perceptions of

  10. Adaptation and validation of the instrument Clinical Learning Environment and Supervision for medical students in primary health care

    Directory of Open Access Journals (Sweden)

    Eva Öhman

    2016-12-01

    Full Text Available Abstract Background Clinical learning takes place in complex socio-cultural environments that are workplaces for the staff and learning places for the students. In the clinical context, the students learn by active participation and in interaction with the rest of the community at the workplace. Clinical learning occurs outside the university, therefore is it important for both the university and the student that the student is given opportunities to evaluate the clinical placements with an instrument that allows evaluation from many perspectives. The instrument Clinical Learning Environment and Supervision (CLES was originally developed for evaluation of nursing students’ clinical learning environment. The aim of this study was to adapt and validate the CLES instrument to measure medical students’ perceptions of their learning environment in primary health care. Methods In the adaptation process the face validity was tested by an expert panel of primary care physicians, who were also active clinical supervisors. The adapted CLES instrument with 25 items and six background questions was sent electronically to 1,256 medical students from one university. Answers from 394 students were eligible for inclusion. Exploratory factor analysis based on principal component methods followed by oblique rotation was used to confirm the adequate number of factors in the data. Construct validity was assessed by factor analysis. Confirmatory factor analysis was used to confirm the dimensions of CLES instrument. Results The construct validity showed a clearly indicated four-factor model. The cumulative variance explanation was 0.65, and the overall Cronbach’s alpha was 0.95. All items loaded similarly with the dimensions in the non-adapted CLES except for one item that loaded to another dimension. The CLES instrument in its adapted form had high construct validity and high reliability and internal consistency. Conclusion CLES, in its adapted form, appears

  11. Fieldwork online: a GIS-based electronic learning environment for supervising fieldwork

    Science.gov (United States)

    Alberti, Koko; Marra, Wouter; Baarsma, Rein; Karssenberg, Derek

    2016-04-01

    Fieldwork comes in many forms: individual research projects in unique places, large groups of students on organized fieldtrips, and everything in between those extremes. Supervising students in often distant places can be a logistical challenge and requires a significant time investment of their supervisors. We developed an online application for remote supervision of students on fieldwork. In our fieldworkonline webapp, which is accessible through a web browser, students can upload their field data in the form of a spreadsheet with coordinates (in a system of choice) and data-fields. Field data can be any combination of quantitative or qualitative data, and can contain references to photos or other documents uploaded to the app. The student's data is converted to a map with data-points that contain all the data-fields and links to photos and documents associated with that location. Supervisors can review the data of their students and provide feedback on observations, or geo-referenced feedback on the map. Similarly, students can ask geo-referenced questions to their supervisors. Furthermore, supervisors can choose different basemaps or upload their own. Fieldwork online is a useful tool for supervising students at a distant location in the field and is most suitable for first-order feedback on students' observations, can be used to guide students to interesting locations, and allows for short discussions on phenomena observed in the field. We seek user that like to use this system, we are able to provide support and add new features if needed. The website is built and controlled using Flask, an open-source Python Framework. The maps are generated and controlled using MapServer and OpenLayers, and the database is built in PostgreSQL with PostGIS support. Fieldworkonline and all tools used to create it are open-source. Experience fieldworkonline at our demo during this session, or online at fieldworkonline.geo.uu.nl (username: EGU2016, password: Vienna).

  12. On the Nature of Problems in Action Learning

    Science.gov (United States)

    Edmonstone, John

    2014-01-01

    The article aims to explore the nature of problems in action learning. Beginning with Revans' distinction between problems and puzzles, it draws parallels with the notion of wicked and tame problems. It offers four means of considering problems in action learning--in terms of the locus of a set's work; from the viewpoint of an…

  13. Development and psychometric testing of the Clinical Learning Environment, Supervision and Nurse Teacher evaluation scale (CLES+T): the Spanish version.

    Science.gov (United States)

    Vizcaya-Moreno, M Flores; Pérez-Cañaveras, Rosa M; De Juan, Joaquín; Saarikoski, Mikko

    2015-01-01

    The Clinical Learning Environment, Supervision and Nurse Teacher scale is a reliable and valid instrument to evaluate the quality of the clinical learning process in international nursing education contexts. This paper reports the development and psychometric testing of the Spanish version of the Clinical Learning Environment, Supervision and Nurse Teacher scale. Cross-sectional validation study of the scale. 10 public and private hospitals in the Alicante area, and the Faculty of Health Sciences (University of Alicante, Spain). 370 student nurses on clinical placement (January 2011-March 2012). The Clinical Learning Environment, Supervision and Nurse Teacher scale was translated using the modified direct translation method. Statistical analyses were performed using PASW Statistics 18 and AMOS 18.0.0 software. A multivariate analysis was conducted in order to assess construct validity. Cronbach's alpha coefficient was used to evaluate instrument reliability. An exploratory factorial analysis identified the five dimensions from the original version, and explained 66.4% of the variance. Confirmatory factor analysis supported the factor structure of the Spanish version of the instrument. Cronbach's alpha coefficient for the scale was .95, ranging from .80 to .97 for the subscales. This version of the Clinical Learning Environment, Supervision and Nurse Teacher scale instrument showed acceptable psychometric properties for use as an assessment scale in Spanish-speaking countries. Copyright © 2014 Elsevier Ltd. All rights reserved.

  14. On Training Targets for Supervised Speech Separation

    Science.gov (United States)

    Wang, Yuxuan; Narayanan, Arun; Wang, DeLiang

    2014-01-01

    Formulation of speech separation as a supervised learning problem has shown considerable promise. In its simplest form, a supervised learning algorithm, typically a deep neural network, is trained to learn a mapping from noisy features to a time-frequency representation of the target of interest. Traditionally, the ideal binary mask (IBM) is used as the target because of its simplicity and large speech intelligibility gains. The supervised learning framework, however, is not restricted to the use of binary targets. In this study, we evaluate and compare separation results by using different training targets, including the IBM, the target binary mask, the ideal ratio mask (IRM), the short-time Fourier transform spectral magnitude and its corresponding mask (FFT-MASK), and the Gammatone frequency power spectrum. Our results in various test conditions reveal that the two ratio mask targets, the IRM and the FFT-MASK, outperform the other targets in terms of objective intelligibility and quality metrics. In addition, we find that masking based targets, in general, are significantly better than spectral envelope based targets. We also present comparisons with recent methods in non-negative matrix factorization and speech enhancement, which show clear performance advantages of supervised speech separation. PMID:25599083

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

    NARCIS (Netherlands)

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

    2014-01-01

    BACKGROUND: Self-directed learning is an educational concept that has received increasing attention. The recent workplace literature, however, reports problems with the facilitation of self-directed learning in clinical practice. We developed the Midwifery Assessment and Feedback Instrument (MAFI) a

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

    NARCIS (Netherlands)

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

    2014-01-01

    BACKGROUND: Self-directed learning is an educational concept that has received increasing attention. The recent workplace literature, however, reports problems with the facilitation of self-directed learning in clinical practice. We developed the Midwifery Assessment and Feedback Instrument (MAFI) a

  17. Dysfunctional problem-based learning curricula: resolving the problem

    Directory of Open Access Journals (Sweden)

    Lim William K

    2012-09-01

    Full Text Available Abstract Background Problem-based learning (PBL has become the most significant innovation in medical education of the past 40 years. In contrast to exam-centered, lecture-based conventional curricula, PBL is a comprehensive curricular strategy that fosters student-centred learning and the skills desired in physicians. The rapid spread of PBL has produced many variants. One of the most common is 'hybrid PBL' where conventional teaching methods are implemented alongside PBL. This paper contends that the mixing of these two opposing educational philosophies can undermine PBL and nullify its positive benefits. Schools using hybrid PBL and lacking medical education expertise may end up with a dysfunctional curriculum worse off than the traditional approach. Discussion For hybrid PBL schools with a dysfunctional curriculum, standard PBL is a cost-feasible option that confers the benefits of the PBL approach. This paper describes the signs of a dysfunctional PBL curriculum to aid hybrid PBL schools in recognising curricular breakdown. Next it discusses alternative curricular strategies and costs associated with PBL. It then details the four critical factors for successful conversion to standard PBL: dealing with staff resistance, understanding the role of lectures, adequate time for preparation and support from the administrative leadership. Summary Hybrid PBL curricula without oversight by staff with medical education expertise can degenerate into dysfunctional curricula inferior even to the traditional approach from which PBL emerged. Such schools should inspect their curriculum periodically for signs of dysfunction to enable timely corrective action. A decision to convert fully to standard PBL is cost feasible but will require time, expertise and commitment which is only sustainable with supportive leadership.

  18. Learning in the Absence of Direct Supervision: Person-Dependent Scaffolding

    Science.gov (United States)

    Palesy, Debra

    2017-01-01

    Contemporary accounts of learning emphasise the importance of immediate social partners such as teachers and co-workers. Yet, much of our learning for work occurs without such experts. This paper provides an understanding of how and why new home care workers use scaffolding to learn and enact safe manual handling techniques in their workplaces,…

  19. Problem Based Learning (PBL): a conundrum.

    Science.gov (United States)

    Wells, Samantha H; Warelow, Philip J; Jackson, Karen L

    2009-10-01

    Problem Based Learning (PBL) using minimal guided instruction is used as an educational strategy across a broad variety of disciplines in the tertiary sector. This paper includes some of the strengths and weaknesses of PBL, both in general and in relation to the health care setting, encompassing some of its philosophical underpinnings and its methodological approach. In an effort to explore some of the benefits and problems with PBL in the work setting, this account will comprise a realistic rather than idealistic focus and will include a range of perspectives from both a facilitator and student standpoint. We suggest that PBL is a useful strategy across a comprehensive nursing degree programme (as the ideal) provided the learning programme is supported financially and that its ideal creed which supports a small group approach are adhered to. What we find is that reality is often different, with individual facilitators condensing their PBL programmes to incorporate a modified PBL approach with this personalized approach often taking strength away from the original conceptions of PBL. What we suggest here is that these circumstances constitute a conundrum.

  20. Problem Based Learning in Nursing Education

    Directory of Open Access Journals (Sweden)

    Weihua Zhang

    2014-01-01

    Full Text Available Background. McMaster University first introduced Problem Based Learning (PBL in the mid 1960s. However, measuring the relationship between PBL for undergraduate nursing programs and students test performance has not yet been assessed in the USA. Purpose. The main purpose of this paper is to describe the effectiveness of PBL on senior student test performance on content related to PBL in a BSN program. Diabetes mellitus and renal insufficiency were taught by traditional lecture format in the previous years. This was the first year we taught this content by the problem based learning method. Method. Historical control group was used to compare the test performances between the PBL groups and the traditional group using Student’s t-test. Result. The mean of diabetes mellitus related questions missed by the PBL group was less than the traditional group (t = 4.51, and P=.00. The mean of renal insufficiency related questions missed by the PBL group was more than the traditional group (t=-6.44, and P=.00. Discussion. This study produced inconclusive findings. Factors that could be attributed to their performance will be discussed.

  1. 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.

  2. 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.

  3. Algorithm of Supervised Learning on Outlier Manifold%有监督的噪音流形学习算法

    Institute of Scientific and Technical Information of China (English)

    黄添强; 李凯; 郑之

    2011-01-01

    流形学习算法是维度约简与数据可视化领域的重要工具,提高算法的效率与健壮性对其实际应用有积极意义.经典的流形学习算法普遍的对噪音点较为敏感,现有的改进算法尚存在不足.本文提出一种基于监督学习与核函数的健壮流形学习算法,把核方法与监督学习引入降维过程,利用已知标签数据信息与核函数特性,使得同类样本变得紧密,不同类样本变成分散,提高后续分类任务的效果,降低算法对流形上噪音的敏感性.在UCI数据与白血病拉曼光谱数据上的实验表明本文改进的算法具有更高的抗噪性.%Manifold learning algorithm is an important tool in the field of dimension reduction and data visualization. Improving the algorithm's efficiency and robustness is of positive significance to its practical application. Classical manifold learning algorithm is sensitive to noise points,and its improved algorithms have been imperfect. This paper presents a robust manifold learning algorithm based on supervised learning and kernel function. It introduces nuclear methods and supervised learning into the dimensionality reduction ,and takes full advantage of the label of some data and the property of kernel function. The proposed algorithm can make close and same types of samples and distribute different types of samples,thus to improves the effect of the classification task and reduce the noise sensitivity of outliers on manifold. The experiments on the UCI data and Raman data of leukemia reveal that the algorithm has better noise immunity.

  4. From Saying to Doing Interdisciplinary Learning: Is Problem-Based Learning the Answer?

    Science.gov (United States)

    Stentoft, Diana

    2017-01-01

    Problem-based learning is often characterised as an approach encompassing interdisciplinary learning; however, little attention has been explicitly paid to what a claim of interdisciplinary problem-based learning means in practice. Even less attention has been given to address the consequences of interdisciplinary problem-based learning for…

  5. Problem based learning - A brief review

    Science.gov (United States)

    Nunes, Sandra; Oliveira, Teresa A.; Oliveira, Amílcar

    2017-07-01

    Teaching is a complex mission that requires not only the theoretical knowledge transmission, but furthermore requires to provide the students the necessary skills for solving real problems in their respective professional activities where complex issues and problems must be frequently faced. Over more than twenty years we have been experiencing an increase in scholar failure in the scientific area of mathematics, which means that Teaching Mathematics and related areas can be even a more complex and hard task. Scholar failure is a complex phenomenon that depends on various factors as social factors, scholar factors or biophysical factors. After numerous attempts made in order to reduce scholar failure our goal in this paper is to understand the role of "Problem Based Learning" and how this methodology can contribute to the solution of both: increasing mathematical courses success and increasing skills in the near future professionals in Portugal. Before designing a proposal for applying this technique in our institutions, we decided to conduct a survey to provide us with the necessary information about and the respective advantages and disadvantages of this methodology, so this is the brief review aim.

  6. Enhancing the Standard of Teaching and Learning in the 21st Century via Qualitative School-Based Supervision in Secondary Schools in Abuja Municipal Area Council (AMAC)

    Science.gov (United States)

    Ebele, Uju F.; Olofu, Paul A.

    2017-01-01

    The study focused on enhancing the standard of teaching and learning in the 21st century via qualitative school-based supervision in secondary schools in Abuja municipal area council. To guide the study, two null hypotheses were formulated. A descriptive survey research design was adopted. The sample of the study constituted of 270 secondary…

  7. Exploration of joint redundancy but not task space variability facilitates supervised motor learning.

    Science.gov (United States)

    Singh, Puneet; Jana, Sumitash; Ghosal, Ashitava; Murthy, Aditya

    2016-12-13

    The number of joints and muscles in a human arm is more than what is required for reaching to a desired point in 3D space. Although previous studies have emphasized how such redundancy and the associated flexibility may play an important role in path planning, control of noise, and optimization of motion, whether and how redundancy might promote motor learning has not been investigated. In this work, we quantify redundancy space and investigate its significance and effect on motor learning. We propose that a larger redundancy space leads to faster learning across subjects. We observed this pattern in subjects learning novel kinematics (visuomotor adaptation) and dynamics (force-field adaptation). Interestingly, we also observed differences in the redundancy space between the dominant hand and nondominant hand that explained differences in the learning of dynamics. Taken together, these results provide support for the hypothesis that redundancy aids in motor learning and that the redundant component of motor variability is not noise.

  8. Mapping Students Use of Technologies in Problem Based Learning Environments

    DEFF Research Database (Denmark)

    Rongbutsri, Nikorn; Khalid, Md. Saifuddin; Ryberg, Thomas

    2011-01-01

    This paper aims to understand how students use technology to enhance their learning in problem-based learning environments. The research methodology is based on both qualitative and quantitative studies. The results are based on students’ interviews, a survey and students’ reflections in course......-related blog posts; they show that students have positive perceptions toward using technologies in problem-based learning environments....

  9. Reflections on Doctoral Supervision: Drawing from the Experiences of Students with Additional Learning Needs in Two Universities

    Science.gov (United States)

    Collins, Bethan

    2015-01-01

    Supervision is an essential part of doctoral study, consisting of relationship and process aspects, underpinned by a range of values. To date there has been limited research specifically about disabled doctoral students' experiences of supervision. This paper draws on qualitative, narrative interviews about doctoral supervision with disabled…

  10. Evaluation of supervised machine-learning algorithms to distinguish between inflammatory bowel disease and alimentary lymphoma in cats.

    Science.gov (United States)

    Awaysheh, Abdullah; Wilcke, Jeffrey; Elvinger, François; Rees, Loren; Fan, Weiguo; Zimmerman, Kurt L

    2016-11-01

    Inflammatory bowel disease (IBD) and alimentary lymphoma (ALA) are common gastrointestinal diseases in cats. The very similar clinical signs and histopathologic features of these diseases make the distinction between them diagnostically challenging. We tested the use of supervised machine-learning algorithms to differentiate between the 2 diseases using data generated from noninvasive diagnostic tests. Three prediction models were developed using 3 machine-learning algorithms: naive Bayes, decision trees, and artificial neural networks. The models were trained and tested on data from complete blood count (CBC) and serum chemistry (SC) results for the following 3 groups of client-owned cats: normal, inflammatory bowel disease (IBD), or alimentary lymphoma (ALA). Naive Bayes and artificial neural networks achieved higher classification accuracy (sensitivities of 70.8% and 69.2%, respectively) than the decision tree algorithm (63%, p machine learning provided a method for distinguishing between ALA-IBD, ALA-normal, and IBD-normal. The naive Bayes and artificial neural networks classifiers used 10 and 4 of the CBC and SC variables, respectively, to outperform the C4.5 decision tree, which used 5 CBC and SC variables in classifying cats into the 3 classes. These models can provide another noninvasive diagnostic tool to assist clinicians with differentiating between IBD and ALA, and between diseased and nondiseased cats. © 2016 The Author(s).

  11. Analysed potential of big data and supervised machine learning techniques in effectively forecasting travel times from fused data

    Directory of Open Access Journals (Sweden)

    Ivana Šemanjski

    2015-12-01

    Full Text Available Travel time forecasting is an interesting topic for many ITS services. Increased availability of data collection sensors increases the availability of the predictor variables but also highlights the high processing issues related to this big data availability. In this paper we aimed to analyse the potential of big data and supervised machine learning techniques in effectively forecasting travel times. For this purpose we used fused data from three data sources (Global Positioning System vehicles tracks, road network infrastructure data and meteorological data and four machine learning techniques (k-nearest neighbours, support vector machines, boosting trees and random forest. To evaluate the forecasting results we compared them in-between different road classes in the context of absolute values, measured in minutes, and the mean squared percentage error. For the road classes with the high average speed and long road segments, machine learning techniques forecasted travel times with small relative error, while for the road classes with the small average speeds and segment lengths this was a more demanding task. All three data sources were proven itself to have a high impact on the travel time forecast accuracy and the best results (taking into account all road classes were achieved for the k-nearest neighbours and random forest techniques.

  12. Solution to reinforcement learning problems with artificial potential field

    Institute of Scientific and Technical Information of China (English)

    XIE Li-juan; XIE Guang-rong; CHEN Huan-wen; LI Xiao-li

    2008-01-01

    A novel method was designed to solve reinforcement learning problems with artificial potential field. Firstly a reinforcement learning problem was transferred to a path planning problem by using artificial potential field(APF), which was a very appropriate method to model a reinforcement learning problem. Secondly, a new APF algorithm was proposed to overcome the local minimum problem in the potential field methods with a virtual water-flow concept. The performance of this new method was tested by a gridworld problem named as key and door maze. The experimental results show that within 45 trials, good and deterministic policies are found in almost all simulations. In comparison with WIERING's HQ-learning system which needs 20 000 trials for stable solution, the proposed new method can obtain optimal and stable policy far more quickly than HQ-learning. Therefore, the new method is simple and effective to give an optimal solution to the reinforcement learning problem.

  13. 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.

  14. Knowledge formations in Problem Based Learning

    DEFF Research Database (Denmark)

    Larsen, Verner

    2013-01-01

    The paper presents some of the results from a recent completed Ph.D. program about disciplinarity and inter-disciplinarity in problem based learning (PBL). Disciplinary content in PBL-programs has been questioned during recent years, so stronger concepts of how knowledge is actually organized...... and structured in PBL are needed to qualify this discussion. This paper focuses on the research question: How has the structuring/organization of knowledge in curriculum changed over time and what kinds of connections and interrelations between disciplines/subjects can be identified in current PBL......-courses? The research has aimed to conceptualize how various knowledge areas blend in two educational contexts applying PBL. Interrelationships have often been referred to as inter-cross- or trans-disciplinarity. However, these terms are ambiguous. Thus I introduce the term transversality to suggests that knowledge...

  15. No Problem? No Research, Little Learning ... Big Problem!

    Directory of Open Access Journals (Sweden)

    Fernando Ornelas Marques

    2012-06-01

    Full Text Available The motivation to carry out this study stemmed from the generalized perception that nowadays youth lacks the skills for the 21st century. Especially the high-level competences like critical thinking, problem solving and autonomy. Several tools can help to improve these competences (e.g. the SCRATCH programming language, but, as researchers and educators, we are mostly concerned with the skill to recognize problems. What if we do not find problems to solve? What if we do not even feel the need to find or solve problems? The problem is to recognize the problem; the next step is to equate the problem; finally we have to feel the need to solve it. No need? No invention. Recognizing a problem is probably the biggest problem of everyday life, because we are permanently faced with problems (many ill-defined problems, which we need to identify, equate and solve.

  16. No Problem? No Research, Little Learning ... Big Problem!

    Directory of Open Access Journals (Sweden)

    Fernando Ornelas Marques

    2012-06-01

    Full Text Available The motivation to carry out this study stemmed from the generalized perception that nowadays youth lacks the skills for the 21st century. Especially the high-level competences like critical thinking, problem solving and autonomy. Several tools can help to improve these competences (e.g. the SCRATCH programming language, but, as researchers and educators, we are mostly concerned with the skill to recognize problems. What if we do not find problems to solve? What if we do not even feel the need to find or solve problems? The problem is to recognize the problem; the next step is to equate the problem; finally we have to feel the need to solve it. No need? No invention. Recognizing a problem is probably the biggest problem of everyday life, because we are permanently faced with problems (many ill-defined problems, which we need to identify, equate and solve.

  17. Student experiences in learning person-centred care of patients with Alzheimer's disease as perceived by nursing students and supervising nurses.

    Science.gov (United States)

    Skaalvik, Mari W; Normann, Hans Ketil; Henriksen, Nils

    2010-09-01

    The aims and objectives of this paper are to illuminate and discuss the experiences and perceptions of nursing students and supervising nurses regarding the students' learning of person- centred care of patients with Alzheimer's disease in a teaching nursing home. This information is then used to develop recommendations as to how student learning could be improved. The clinical experiences of nursing students are an important part of learning person-centred care. Caring for patients with Alzheimer's disease may cause frustration, sadness, fear and empathy. Person-centred care can be learned in clinical practice. A qualitative study. The study was performed in 2006 using field work with field notes and qualitative interviews with seven-fifth-semester nursing students and six supervising nurses. This study determined the variation in the perceptions of nursing students and supervising nurses with regards to the students' expertise in caring for patients with Alzheimer's disease. The nursing students experienced limited learning regarding person-centred approaches in caring for patients with Alzheimer's disease. However, the supervising nurses perceived the teaching nursing home as a site representing multiple learning opportunities in this area. Nursing students perceived limited learning outcomes because they did not observe or experience systematic person-centred approaches in caring for patients with Alzheimer's disease. It is important that measures of quality improvements in the care of patients with Alzheimer's disease are communicated and demonstrated for nursing students working in clinical practices in a teaching nursing home. Introduction of person-centred approaches is vital regarding learning outcomes for nursing students caring for patients with Alzheimer's disease. © 2010 The Authors. Journal compilation © 2010 Blackwell Publishing Ltd.

  18. Automatic learning rate adjustment for self-supervising autonomous robot control

    Science.gov (United States)

    Arras, Michael K.; Protzel, Peter W.; Palumbo, Daniel L.

    1992-01-01

    Described is an application in which an Artificial Neural Network (ANN) controls the positioning of a robot arm with five degrees of freedom by using visual feedback provided by two cameras. This application and the specific ANN model, local liner maps, are based on the work of Ritter, Martinetz, and Schulten. We extended their approach by generating a filtered, average positioning error from the continuous camera feedback and by coupling the learning rate to this error. When the network learns to position the arm, the positioning error decreases and so does the learning rate until the system stabilizes at a minimum error and learning rate. This abolishes the need for a predetermined cooling schedule. The automatic cooling procedure results in a closed loop control with no distinction between a learning phase and a production phase. If the positioning error suddenly starts to increase due to an internal failure such as a broken joint, or an environmental change such as a camera moving, the learning rate increases accordingly. Thus, learning is automatically activated and the network adapts to the new condition after which the error decreases again and learning is 'shut off'. The automatic cooling is therefore a prerequisite for the autonomy and the fault tolerance of the system.

  19. Dolanan Dance Learning on Supervising Pre-Service Teachers during Teaching Practicum Program

    Directory of Open Access Journals (Sweden)

    Nilam Cahyaningrum

    2015-01-01

    Full Text Available Taman Kanak- kanak Mekarsari (Mekarsari Kindergarten is a school that choses dolanan anak dance lesson which is taught using demonstration methods. This study aims to find, understand, and describe the process and learning outcomes of dolanan anak dance in Mekarsari Kindergarten, Kandeman District of Batang. This study uses qualitative research methods with a phenomenological approach to research sites in Mekarsari Kindergarten, Kandeman District of Batang. Data collection techniques used were observation, interview techniques, and technical documentation. Data analysis were using data reduction, data presentation, drawing conclusions, and verification. The validity test were using triangulation of data sources, techniques, and time. Dolanan anak dance learning in Mekarsari Kindergarten consists of several components, namely teaching and learning activities, goals, teachers, students, materials, methods, media, tools and learning resources, and evaluation. Dolanan dance learning was using demonstration method implemented through three stages: pre-development activities, core activities, and closing activities. The learning outcomes of dolanan anak dance learning in Mekarsari kindergarten were categorized into three aspects, namely cognitive, affective, and psychomotor. Cognitive aspects can be seen from the students’ ability to remember, memorize and understand the dance. Affective aspects include familiar levels, namely learning to know friends and dance movements, respond the movements amomg friends, and appreciate the teacher’s explanation given to each student. Psychomotor aspects can be seen from the students’ ability to imitate the dance movements, use the concept of doing the movements and precision of movements, weave movement and exercise appropriately.

  20. Facilitating Problem Framing in Project-Based Learning

    Science.gov (United States)

    Svihla, Vanessa; Reeve, Richard

    2016-01-01

    While problem solving is a relatively well understood process, problem framing is less well understood, particularly with regard to supporting students to learn as they frame problems. Project-based learning classrooms are an ideal setting to investigate how teachers facilitate this process. Using participant observation, this study investigated…

  1. Facilitating Problem Framing in Project-Based Learning

    Science.gov (United States)

    Svihla, Vanessa; Reeve, Richard

    2016-01-01

    While problem solving is a relatively well understood process, problem framing is less well understood, particularly with regard to supporting students to learn as they frame problems. Project-based learning classrooms are an ideal setting to investigate how teachers facilitate this process. Using participant observation, this study investigated…

  2. Discussion on supervision implementation regulation problems and countermeasures%谈监理实施细则存在的问题及对策

    Institute of Scientific and Technical Information of China (English)

    杨富金

    2015-01-01

    简述了国内相关法律、法规对监理实施细则的编制要求,通过分析当前监理实施细则存在的问题,提出了一些解决对策,以促进监理行业朝着科学管理、规范管理的方向健康发展。%The paper briefly illustrates supervision implementation regulations compiling demands according to domestic relevant laws and rules, analyzes current supervision implementation problems,and puts forward some solving countermeasures,with a view to promote its scientific and regular development.

  3. Health science students' experiences of group supervision of the bachelor's thesis.

    Science.gov (United States)

    Utriainen, Kati; Ahonen, Sanna-Mari; Kangasniemi, Mari; Liikanen, Eeva

    2011-04-01

    This study aimed to describe health science university students' experiences of group supervision of the bachelor's thesis. Sixty-one students responded to questions on an open data collection form, and the data were analyzed by using qualitative inductive content analysis. According to the students, group supervision is supportive in terms of joint learning as well as commitment-enhancing and participative learning, but it also can be useless from the viewpoint of an individual student's own thesis. Teachers' role as experts and active directors of the group, students' commitment to learn together, and workable practical organization of the group were promoting factors, whereas students' timetable problems, mismatch between received and needed supervision, and difficulties in supervising other students were restraining factors. The results can be used for developing group supervision in higher health education.

  4. Learning Problems in Children of Refugee Background: A Systematic Review

    National Research Council Canada - National Science Library

    Graham, Hamish R; Minhas, Ripudaman S; Paxton, Georgia

    2016-01-01

    Learning problems are common, affecting up to 1 in 10 children. Refugee children may have cumulative risk for educational disadvantage, but there is limited information on learning in this population...

  5. Learning Problems in Children of Refugee Background: A Systematic Review

    National Research Council Canada - National Science Library

    Hamish R Graham; Ripudaman S Minhas; Georgia Paxton

    2016-01-01

      Learning problems are common, affecting up to 1 in 10 children. Refugee children may have cumulative risk for educational disadvantage, but there is limited information on learning in this population...

  6. Creating Problem-Based Leadership Learning across the Curriculum

    Science.gov (United States)

    Thompson, Sara E.; Couto, Richard A.

    2016-01-01

    This chapter explores problem-based learning (PBL) as effective pedagogy to enhance leadership learning. Through institutional examples, research, and personal experiences, the authors provide a rationale for faculty and staff to utilize PBL across the curriculum.

  7. Problem-Based Learning in Social Work Education

    DEFF Research Database (Denmark)

    Monrad, Merete; Mølholt, Anne-Kirstine

    2017-01-01

    Problem-based learning (PBL) constitutes a promising way of integrating academia and social work practice because PBL fosters engagement with real-life problems and enhances important skills needed in social work practice. However, little attention has been given to social work students......’ experiences of PBL. In this article we address this gap by exploring experiences of learning and learning preferences among master’s-level students in a Danish social work education setting where extensive problem-based project work is used. We find a discrepancy between students’ preferred learning and when...... they experience learning the most. Possible reasons for this discrepancy, and implications for teaching, are discussed....

  8. Problem-Based Learning in Social Work Education: Students’ experiences

    DEFF Research Database (Denmark)

    Monrad, Merete; Mølholt, Anne-Kirstine

    2017-01-01

    Problem-based learning (PBL) constitutes a promising way of integrating academia and social work practice because PBL fosters engagement with real-life problems and enhances important skills needed in social work practice. However, little attention has been given to social work students......’ experiences of PBL. In this article we address this gap by exploring experiences of learning and learning preferences among master’s-level students in a Danish social work education setting where extensive problem-based project work is used. We find a discrepancy between students’ preferred learning and when...... they experience learning the most. Possible reasons for this discrepancy, and implications for teaching, are discussed....

  9. 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

  10. Assessment of work-integrated learning: comparison of the usage of a grading rubric by supervising radiographers and teachers

    Energy Technology Data Exchange (ETDEWEB)

    Kilgour, Andrew J, E-mail: akilgour@csu.edu.au [Charles Sturt University, Wagga Wagga, NSW (Australia); Kilgour, Peter W [Avondale College of Higher Education, Cooranbong, NSW (Australia); Gerzina, Tania [Dental Educational Research, Faculty of Dentistry, Jaw Function and Orofacial Pain Research Unit, Westmead Centre for Oral Health, C24- Westmead Hospital, The University of Sydney, Sydney, NSW, 2006 (Australia); Christian, Beverly [Avondale College of Higher Education, Cooranbong, NSW (Australia); Charles Sturt University, Wagga Wagga, NSW (Australia)

    2014-02-15

    Introduction: Professional work-integrated learning (WIL) that integrates the academic experience with off-campus professional experience placements is an integral part of many tertiary courses. Issues with the reliability and validity of assessment grades in these placements suggest that there is a need to strengthen the level of academic rigour of placements in these programmes. This study aims to compare the attitudes to the usage of assessment rubrics of radiographers supervising medical imaging students and teachers supervising pre-service teachers. Methods: WIL placement assessment practices in two programmes, pre-service teacher training (Avondale College of Higher Education, NSW) and medical diagnostic radiography (Faculty of Health Sciences, University of Sydney, NSW), were compared with a view to comparing assessment strategies across these two different educational domains. Educators (course coordinators) responsible for teaching professional development placements of teacher trainees and diagnostic radiography students developed a standards-based grading rubric designed to guide assessment of students’ work during WIL placement by assessors. After ∼12 months of implementation of the rubrics, assessors’ reaction to the effectiveness and usefulness of the grading rubric was determined using a specially created survey form. Data were collected over the period from March to June 2011. Quantitative and qualitative data found that assessors in both programmes considered the grading rubric to be a vital tool in the assessment process, though teacher supervisors were more positive about the benefits of its use than the radiographer supervisors. Results: Benefits of the grading rubric included accuracy and consistency of grading, ability to identify specific areas of desired development and facilitation of the provision of supervisor feedback. The use of assessment grading rubrics is of benefit to assessors in WIL placements from two very different

  11. Assessment of work-integrated learning: comparison of the usage of a grading rubric by supervising radiographers and teachers.

    Science.gov (United States)

    Kilgour, Andrew J; Kilgour, Peter W; Gerzina, Tania; Christian, Beverly

    2014-02-01

    IntroductionProfessional work-integrated learning (WIL) that integrates the academic experience with off-campus professional experience placements is an integral part of many tertiary courses. Issues with the reliability and validity of assessment grades in these placements suggest that there is a need to strengthen the level of academic rigour of placements in these programmes. This study aims to compare the attitudes to the usage of assessment rubrics of radiographers supervising medical imaging students and teachers supervising pre-service teachers. MethodsWIL placement assessment practices in two programmes, pre-service teacher training (Avondale College of Higher Education, NSW) and medical diagnostic radiography (Faculty of Health Sciences, University of Sydney, NSW), were compared with a view to comparing assessment strategies across these two different educational domains. Educators (course coordinators) responsible for teaching professional development placements of teacher trainees and diagnostic radiography students developed a standards-based grading rubric designed to guide assessment of students' work during WIL placement by assessors. After ∼12 months of implementation of the rubrics, assessors' reaction to the effectiveness and usefulness of the grading rubric was determined using a specially created survey form. Data were collected over the period from March to June 2011. Quantitative and qualitative data found that assessors in both programmes considered the grading rubric to be a vital tool in the assessment process, though teacher supervisors were more positive about the benefits of its use than the radiographer supervisors. ResultsBenefits of the grading rubric included accuracy and consistency of grading, ability to identify specific areas of desired development and facilitation of the provision of supervisor feedback. The use of assessment grading rubrics is of benefit to assessors in WIL placements from two very different teaching

  12. Classification models for clear cell renal carcinoma stage progression, based on tumor RNAseq expression trained supervised machine learning algorithms.

    Science.gov (United States)

    Jagga, Zeenia; Gupta, Dinesh

    2014-01-01

    Clear-cell Renal Cell Carcinoma (ccRCC) is the most- prevalent, chemotherapy resistant and lethal adult kidney cancer. There is a need for novel diagnostic and prognostic biomarkers for ccRCC, due to its heterogeneous molecular profiles and asymptomatic early stage. This study aims to develop classification models to distinguish early stage and late stage of ccRCC based on gene expression profiles. We employed supervised learning algorithms- J48, Random Forest, SMO and Naïve Bayes; with enriched model learning by fast correlation based feature selection to develop classification models trained on sequencing based gene expression data of RNAseq experiments, obtained from The Cancer Genome Atlas. Different models developed in the study were evaluated on the basis of 10 fold cross validations and independent dataset testing. Random Forest based prediction model performed best amongst the models developed in the study, with a sensitivity of 89%, accuracy of 77% and area under Receivers Operating Curve of 0.8. We anticipate that the prioritized subset of 62 genes and prediction models developed in this study will aid experimental oncologists to expedite understanding of the molecular mechanisms of stage progression and discovery of prognostic factors for ccRCC tumors.

  13. Supervised practice in occupational therapy in a psychosocial care center: Challenges for the assistance and the teaching and learning process

    Directory of Open Access Journals (Sweden)

    Milton Carlos Mariotti

    2014-09-01

    Full Text Available The psychiatric reform in Brazil has replaced the hospital-centered model by the reintegration of users to their respective communities. The Center of Psychosocial Care (CAPS has been the main equipment in that scope. Objectives: To report the development of Supervised Practice in Occupational Therapy in a CAPS II unit in Curitiba, Parana state, Brazil. Methods: This is an experience report. It features the training field and describes the stages of the teaching and learning process which involved institutional observation, reporting and intervention proposal, collecting data about the users’ profile and attendances. The work focused the non-intensive users because they are close to hospital discharge. Results: We found that users of the non-intensive system, rather than crave the discharge, would like to return to the semi-intensive or intensive systems, aiming to regain sickness and transportation benefits, which are lost as users make progress. This fact denotes great contradictions in the system. We also attended intensive and semi-intensive systems users. Conclusions: The students’ learning included aspects such as direct contact with the institutional reality; knowledge about the health system, its limitations and contradictions; approach to users, their families, realities, socioeconomic conditions, desires, aspirations, or lack thereof; difficulties in engaging in meaningful occupations in their territories, limitations, and social stigma; working with frustrations, reflecting about ways to change the reality; in addition to expanded clinical practice, participating in the discussions and formulation of public policies on mental healthcare and social control.

  14. Supervision and group dynamics

    DEFF Research Database (Denmark)

    Hansen, Søren; Jensen, Lars Peter

    2004-01-01

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

  15. Problem-Based Learning: Exploiting Knowledge of How People Learn to Promote Effective Learning

    Science.gov (United States)

    Wood, E. J.

    2004-01-01

    There is much information from educational psychology studies on how people learn. The thesis of this paper is that we should use this information to guide the ways in which we teach rather than blindly using our traditional methods. In this context, problem-based learning (PBL), as a method of teaching widely used in medical schools but…

  16. 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.

  17. Use of Problem-Based Learning in the Teaching and Learning of Horticultural Production

    Science.gov (United States)

    Abbey, Lord; Dowsett, Eric; Sullivan, Jan

    2017-01-01

    Purpose: Problem-based learning (PBL), a relatively novel teaching and learning process in horticulture, was investigated. Proper application of PBL can potentially create a learning context that enhances student learning. Design/Methodology/Approach: Students worked on two complex ill-structured problems: (1) to produce fresh baby greens for a…

  18. Statistical learning problem of artificial neural network to control roofing process

    Directory of Open Access Journals (Sweden)

    Lapidus Azariy

    2017-01-01

    Full Text Available Now software developed on the basis of artificial neural networks (ANN has been actively implemented in construction companies to support decision-making in organization and management of construction processes. ANN learning is the main stage of its development. A key question for supervised learning is how many number of training examples we need to approximate the true relationship between network inputs and output with the desired accuracy. Also designing of ANN architecture is related to learning problem known as “curse of dimensionality”. This problem is important for the study of construction process management because of the difficulty to get training data from construction sites. In previous studies the authors have designed a 4-layer feedforward ANN with a unit model of 12-5-4-1 to approximate estimation and prediction of roofing process. This paper presented the statistical learning side of created ANN with simple-error-minimization algorithm. The sample size to efficient training and the confidence interval of network outputs defined. In conclusion the authors predicted successful ANN learning in a large construction business company within a short space of time.

  19. Improving Supervision of Cross-Cultural Postgraduate University Students

    Science.gov (United States)

    Strang, Kenneth David

    2010-01-01

    The internet has led to an increasing number of international students enrolling for postgraduate degrees. The literature confirms that there have been problems such as attrition, motivation, supervision and others. Professors struggle to appease international student learning styles, while simultaneously international students strain to…

  20. Supervision of Teachers Based on Adjusted Arithmetic Learning in Special Education

    Science.gov (United States)

    Eriksson, Gota

    2008-01-01

    This article reports on 20 children's learning in arithmetic after teaching was adjusted to their conceptual development. The report covers periods from three months up to three terms in an ongoing intervention study of teachers and children in schools for the intellectually disabled and of remedial teaching in regular schools. The researcher…

  1. Clinical supervision.

    Science.gov (United States)

    Goorapah, D

    1997-05-01

    The introduction of clinical supervision to a wider sphere of nursing is being considered from a professional and organizational point of view. Positive views are being expressed about adopting this concept, although there are indications to suggest that there are also strong reservations. This paper examines the potential for its success amidst the scepticism that exists. One important question raised is whether clinical supervision will replace or run alongside other support systems.

  2. The implementation of problem-based learning in collaborative groups in a chiropractic program in Malaysia

    Directory of Open Access Journals (Sweden)

    Ni Ni Win

    2015-05-01

    Full Text Available Purpose: Problem-based learning (PBL is usually conducted in small-group learning sessions with approximately eight students per facilitator. In this study, we implemented a modified version of PBL involving collaborative groups in an undergraduate chiropractic program and assessed its pedagogical effectiveness. Methods: This study was conducted at the International Medical University, Kuala Lumpur, Malaysia, and involved the 2012 chiropractic student cohort. Six PBL cases were provided to chiropractic students, consisting of three PBL cases for which learning resources were provided and another three PBL cases for which learning resources were not provided. Group discussions were not continuously supervised, since only one facilitator was present. The students’ perceptions of PBL in collaborative groups were assessed with a questionnaire that was divided into three domains: motivation, cognitive skills, and perceived pressure to work. Results: Thirty of the 31 students (97% participated in the study. PBL in collaborative groups was significantly associated with positive responses regarding students’ motivation, cognitive skills, and perceived pressure to work (P<0.05. The students felt that PBL with learning resources increased motivation and cognitive skills (P<0.001. Conclusion: The new PBL implementation described in this study does not require additional instructors or any additional funding. When implemented in a classroom setting, it has pedagogical benefits equivalent to those of small-group sessions. Our findings also suggest that students rely significantly on available learning resources.

  3. Problems and suggestions in the supervise test of hospital preparation%医院制剂监督抽验中存在的问题及建议

    Institute of Scientific and Technical Information of China (English)

    李佐法; 王波; 冉晓静

    2014-01-01

    The Problems found in the suPervise test of hosPital PreParation were summed uP,ProPosed correlated sug-gestions,in order to Promote the quality of hosPital PreParation to imProve and Protect the PeoPle safe and affective medica-tion.%对近几年医院制剂监督抽验中发现的问题进行归纳,提出相关建议,促进医院制剂质量不断提升,保障人民用药安全、有效。

  4. Problem-Based Learning Assessment for Information Systems Courses.

    Science.gov (United States)

    Yip, Willie; Ghafarian, Ahmad

    A promising recent development in tertiary education involves the application of problem-based learning as a curricular vehicle to develop student talent. Problem-based learning (PBL) is common in professional education, such as in medical, law, and business schools, and is becoming increasingly common in pre-college education. However, it is less…

  5. Dyscalculia and Other Learning Problems in Arithmetic: A Historical Perspective.

    Science.gov (United States)

    Sharma, Mahesh C.

    1986-01-01

    Evidence on learning problems due to dyscalculia is surveyed. Definitions, factors responsible for dyscalculia, split-brain research and hemispheric roles, mathematics learning problems and personality, materials for instruction, and levels of knowing mathematics are among the topics discussed with an extensive list of references. (MNS)

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

    CERN Document Server

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

    2016-01-01

    In the era of large astronomical surveys, photometric classification of supernovae (SNe) has become an important research field due to limited spectroscopic resources for candidate follow-up and classification. In this work, we present a method to photometrically classify type Ia supernovae based on machine learning with redshifts that are derived from the SN light-curves. This method is implemented on real data from the SNLS deferred pipeline, a purely photometric pipeline that identifies SNe Ia at high-redshifts ($0.2learning classification. We study the performance of different algorithms such as Random Forest and Boosted Decision Trees. We evaluate the performance using SN simulations and real data from the first 3 years of the Supernova Legacy Survey (SNLS), which contains large spectroscopically and photometrically classified type Ia sa...

  7. Physical Realization of a Supervised Learning System Built with Organic Memristive Synapses

    Science.gov (United States)

    Lin, Yu-Pu; Bennett, Christopher H.; Cabaret, Théo; Vodenicarevic, Damir; Chabi, Djaafar; Querlioz, Damien; Jousselme, Bruno; Derycke, Vincent; Klein, Jacques-Olivier

    2016-09-01

    Multiple modern applications of electronics call for inexpensive chips that can perform complex operations on natural data with limited energy. A vision for accomplishing this is implementing hardware neural networks, which fuse computation and memory, with low cost organic electronics. A challenge, however, is the implementation of synapses (analog memories) composed of such materials. In this work, we introduce robust, fastly programmable, nonvolatile organic memristive nanodevices based on electrografted redox complexes that implement synapses thanks to a wide range of accessible intermediate conductivity states. We demonstrate experimentally an elementary neural network, capable of learning functions, which combines four pairs of organic memristors as synapses and conventional electronics as neurons. Our architecture is highly resilient to issues caused by imperfect devices. It tolerates inter-device variability and an adaptable learning rule offers immunity against asymmetries in device switching. Highly compliant with conventional fabrication processes, the system can be extended to larger computing systems capable of complex cognitive tasks, as demonstrated in complementary simulations.

  8. Educational Data Mining and Problem-Based Learning

    Science.gov (United States)

    Walldén, Sari; Mäkinen, Erkki

    2014-01-01

    This paper considers the use of log data provided by learning management systems when studying whether students obey the problem-based learning (PBL) method. Log analysis turns out to be a valuable tool in measuring the use of the learning material of interest. It gives reliable figures concerning not only the number of use sessions but also the…

  9. Teaching and Learning. A Problem-Solving Focus.

    Science.gov (United States)

    Curcio, Frances R., Ed.

    This book is dedicated to George Polya, who focused on problem solving as the means for teaching and learning mathematics. The first chapter is a reprint of his article "On Learning, Teaching, and Learning Teaching." Then, G. L. Alexanderson paints a portrait of "George Polya, Teacher," including some anecdotes that exemplify…

  10. An active learning based classification strategy for the minority class problem: application to histopathology annotation

    Directory of Open Access Journals (Sweden)

    Doyle Scott

    2011-10-01

    Full Text Available Abstract Background Supervised classifiers for digital pathology can improve the ability of physicians to detect and diagnose diseases such as cancer. Generating training data for classifiers is problematic, since only domain experts (e.g. pathologists can correctly label ground truth data. Additionally, digital pathology datasets suffer from the "minority class problem", an issue where the number of exemplars from the non-target class outnumber target class exemplars which can bias the classifier and reduce accuracy. In this paper, we develop a training strategy combining active learning (AL with class-balancing. AL identifies unlabeled samples that are "informative" (i.e. likely to increase classifier performance for annotation, avoiding non-informative samples. This yields high accuracy with a smaller training set size compared with random learning (RL. Previous AL methods have not explicitly accounted for the minority class problem in biomedical images. Pre-specifying a target class ratio mitigates the problem of training bias. Finally, we develop a mathematical model to predict the number of annotations (cost required to achieve balanced training classes. In addition to predicting training cost, the model reveals the theoretical properties of AL in the context of the minority class problem. Results Using this class-balanced AL training strategy (CBAL, we build a classifier to distinguish cancer from non-cancer regions on digitized prostate histopathology. Our dataset consists of 12,000 image regions sampled from 100 biopsies (58 prostate cancer patients. We compare CBAL against: (1 unbalanced AL (UBAL, which uses AL but ignores class ratio; (2 class-balanced RL (CBRL, which uses RL with a specific class ratio; and (3 unbalanced RL (UBRL. The CBAL-trained classifier yields 2% greater accuracy and 3% higher area under the receiver operating characteristic curve (AUC than alternatively-trained classifiers. Our cost model accurately predicts

  11. An active learning based classification strategy for the minority class problem: application to histopathology annotation.

    Science.gov (United States)

    Doyle, Scott; Monaco, James; Feldman, Michael; Tomaszewski, John; Madabhushi, Anant

    2011-10-28

    Supervised classifiers for digital pathology can improve the ability of physicians to detect and diagnose diseases such as cancer. Generating training data for classifiers is problematic, since only domain experts (e.g. pathologists) can correctly label ground truth data. Additionally, digital pathology datasets suffer from the "minority class problem", an issue where the number of exemplars from the non-target class outnumber target class exemplars which can bias the classifier and reduce accuracy. In this paper, we develop a training strategy combining active learning (AL) with class-balancing. AL identifies unlabeled samples that are "informative" (i.e. likely to increase classifier performance) for annotation, avoiding non-informative samples. This yields high accuracy with a smaller training set size compared with random learning (RL). Previous AL methods have not explicitly accounted for the minority class problem in biomedical images. Pre-specifying a target class ratio mitigates the problem of training bias. Finally, we develop a mathematical model to predict the number of annotations (cost) required to achieve balanced training classes. In addition to predicting training cost, the model reveals the theoretical properties of AL in the context of the minority class problem. Using this class-balanced AL training strategy (CBAL), we build a classifier to distinguish cancer from non-cancer regions on digitized prostate histopathology. Our dataset consists of 12,000 image regions sampled from 100 biopsies (58 prostate cancer patients). We compare CBAL against: (1) unbalanced AL (UBAL), which uses AL but ignores class ratio; (2) class-balanced RL (CBRL), which uses RL with a specific class ratio; and (3) unbalanced RL (UBRL). The CBAL-trained classifier yields 2% greater accuracy and 3% higher area under the receiver operating characteristic curve (AUC) than alternatively-trained classifiers. Our cost model accurately predicts the number of annotations necessary

  12. Managing the Gap between Curriculum Based and Problem Based Learning

    DEFF Research Database (Denmark)

    Bygholm, Ann; Buus, Lillian

    2009-01-01

    the challenges in applying problem based learning strategies in a context where several universities, with different cultures of teaching, collaboratively develop and deliver online courses. We present a pedagogical framework embracing both problem based and curriculum based strategies and show how we used....../or but rather both/and. In this paper we describe an approach to design and delivery of online courses in computer science which on the one hand is based on a specified curriculum and on the other hand gives room for different learning strategies, problem based learning being one of them. We discuss...... this as a basis for trying out various online learning strategies....

  13. 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.

  14. A survey of learning problems in black primary school children.

    Science.gov (United States)

    Cartwright, J D; Jukes, C; Wilson, A; Xaba, D

    1981-03-28

    A survey of the prevalence and types of learning disorders among Black primary school children was undertaken on the East Rand. Class teachers were given a questionnaire and asked to identify the number of children in their class with learning problems and the number of those with specific disabilities such as poor eyesight or hearing, epilepsy, physical handicaps or mental retardation. There were 7516 children in the classes surveyed; 1692 (22,4%) of them were identified by their teachers as having learning problems, while 666 (8,7%) had a physical or mental handicap. The prevalence and present status of children with learning disability need to be defined before plans to improve their education can be established. Our data show that at present classes are large and the prevalence of children with learning problems is high. Improving teachers' skills and reducing the number of children per class might improve the education of children with learning problems.

  15. Clinical supervision using video-conferencing technology: a reflective account.

    Science.gov (United States)

    Marrow, Carol E; Hollyoake, Kim; Hamer, Dorothy; Kenrick, Catherine

    2002-09-01

    This paper discusses three nursing practitioners' experiences of clinical supervision using video-conferencing (VC) technology. The study, based on supportive action research, involved 40 practising community and hospital nurses from a variety of specialties and took place over an 18-month period. The research was collaborative, educational and applied and focused on the effects of remote clinical supervision on the development of professional practice. Data were obtained through pre- and post-study questionnaires, repertory grids, focus group interviews and written narratives. The three research participants found that clinical supervision, as a mediator of learning, was vital in enabling them to develop their reflective and problem-solving skills. Through critical conversations with either a supervisor or within peer group supervision, the participants increased both their confidence and self-awareness and gained more insight into the practices and needs of other practitioners. They also examined critically patient care issues resulting in attention to existing or the development of new care protocols. They did, however, experience some impediments to the process of clinical supervision, mainly lack of peer and management support and ongoing education. The participants had mixed perceptions with regards to using VC technology for clinical supervision. They experienced technical and accessibility problems and communication problems, suggesting the need for clear protocols for both technical support and applied training.

  16. Use of auditory learning to manage listening problems in children

    OpenAIRE

    Moore, David R.; Halliday, Lorna F.; Amitay, Sygal

    2008-01-01

    This paper reviews recent studies that have used adaptive auditory training to address communication problems experienced by some children in their everyday life. It considers the auditory contribution to developmental listening and language problems and the underlying principles of auditory learning that may drive further refinement of auditory learning applications. Following strong claims that language and listening skills in children could be improved by auditory learning, researchers hav...

  17. Supervised hub-detection for brain connectivity

    Science.gov (United States)

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

    2016-03-01

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

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

    CERN Document Server

    Ostrovski, Fernanda; Connolly, Andrew J; Lemon, Cameron A; Auger, Matthew W; Banerji, Manda; Hung, Johnathan M; Koposov, Sergey E; Lidman, Christopher E; Reed, Sophie L; Allam, Sahar; Benoit-Lévy, Aurélien; Bertin, Emmanuel; Brooks, David; Buckley-Geer, Elizabeth; Rosell, Aurelio Carnero; Kind, Matias Carrasco; Carretero, Jorge; Cunha, Carlos E; da Costa, Luiz N; Desai, Shantanu; Diehl, H Thomas; Dietrich, Jörg P; Evrard, August E; Finley, David A; Flaugher, Brenna; Fosalba, Pablo; Frieman, Josh; Gerdes, David W; Goldstein, Daniel A; Gruen, Daniel; Gruendl, Robert A; Gutierrez, Gaston; Honscheid, Klaus; James, David J; Kuehn, Kyler; Kuropatkin, Nikolay; Lima, Marcos; Lin, Huan; Maia, Marcio A G; Marshall, Jennifer L; Martini, Paul; Melchior, Peter; Miquel, Ramon; Ogando, Ricardo; Malagón, Andrés Plazas; Reil, Kevin; Romer, Kathy; Sanchez, Eusebio; Santiago, Basilio; Scarpine, Vic; Sevilla-Noarbe, Ignacio; Soares-Santos, Marcelle; Sobreira, Flavia; Suchyta, Eric; Tarle, Gregory; Thomas, Daniel; Tucker, Douglas L; Walker, Alistair R

    2016-01-01

    We present the discovery and preliminary characterization of a gravitationally lensed quasar with a source redshift $z_{s}=2.74$ and image separation of $2.9"$ lensed by a foreground $z_{l}=0.40$ elliptical galaxy. Since the images of gravitationally lensed quasars are the superposition of multiple point sources and a foreground lensing galaxy, we have developed a morphology independent multi-wavelength approach to the photometric selection of lensed quasar candidates based on Gaussian Mixture Models (GMM) supervised machine learning. Using this technique and $gi$ multicolour photometric observations from the Dark Energy Survey (DES), near IR $JK$ photometry from the VISTA Hemisphere Survey (VHS) and WISE mid IR photometry, we have identified a candidate system with two catalogue components with $i_{AB}=18.61$ and $i_{AB}=20.44$ comprised of an elliptical galaxy and two blue point sources. Spectroscopic follow-up with NTT and the use of an archival AAT spectrum show that the point sources can be identified as...

  19. Automated cell analysis tool for a genome-wide RNAi screen with support vector machine based supervised learning

    Science.gov (United States)

    Remmele, Steffen; Ritzerfeld, Julia; Nickel, Walter; Hesser, Jürgen

    2011-03-01

    RNAi-based high-throughput microscopy screens have become an important tool in biological sciences in order to decrypt mostly unknown biological functions of human genes. However, manual analysis is impossible for such screens since the amount of image data sets can often be in the hundred thousands. Reliable automated tools are thus required to analyse the fluorescence microscopy image data sets usually containing two or more reaction channels. The herein presented image analysis tool is designed to analyse an RNAi screen investigating the intracellular trafficking and targeting of acylated Src kinases. In this specific screen, a data set consists of three reaction channels and the investigated cells can appear in different phenotypes. The main issue of the image processing task is an automatic cell segmentation which has to be robust and accurate for all different phenotypes and a successive phenotype classification. The cell segmentation is done in two steps by segmenting the cell nuclei first and then using a classifier-enhanced region growing on basis of the cell nuclei to segment the cells. The classification of the cells is realized by a support vector machine which has to be trained manually using supervised learning. Furthermore, the tool is brightness invariant allowing different staining quality and it provides a quality control that copes with typical defects during preparation and acquisition. A first version of the tool has already been successfully applied for an RNAi-screen containing three hundred thousand image data sets and the SVM extended version is designed for additional screens.

  20. 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.

  1. Predicting the Ecological Quality Status of Marine Environments from eDNA Metabarcoding Data Using Supervised Machine Learning.

    Science.gov (United States)

    Cordier, Tristan; Esling, Philippe; Lejzerowicz, Franck; Visco, Joana; Ouadahi, Amine; Martins, Catarina; Cedhagen, Tomas; Pawlowski, Jan

    2017-08-15

    Monitoring biodiversity is essential to assess the impacts of increasing anthropogenic activities in marine environments. Traditionally, marine biomonitoring involves the sorting and morphological identification of benthic macro-invertebrates, which is time-consuming and taxonomic-expertise demanding. High-throughput amplicon sequencing of environmental DNA (eDNA metabarcoding) represents a promising alternative for benthic monitoring. However, an important fraction of eDNA sequences remains unassigned or belong to taxa of unknown ecology, which prevent their use for assessing the ecological quality status. Here, we show that supervised machine learning (SML) can be used to build robust predictive models for benthic monitoring, regardless of the taxonomic assignment of eDNA sequences. We tested three SML approaches to assess the environmental impact of marine aquaculture using benthic foraminifera eDNA, a group of unicellular eukaryotes known to be good bioindicators, as features to infer macro-invertebrates based biotic indices. We found similar ecological status as obtained from macro-invertebrates inventories. We argue that SML approaches could overcome and even bypass the cost and time-demanding morpho-taxonomic approaches in future biomonitoring.

  2. Translation and validation of the clinical learning environment, supervision and nurse teacher scale (CLES + T) in Croatian language.

    Science.gov (United States)

    Lovrić, Robert; Piškorjanac, Silvija; Pekić, Vlasta; Vujanić, Jasenka; Ratković, Karolina Kramarić; Luketić, Suzana; Plužarić, Jadranka; Matijašić-Bodalec, Dubravka; Barać, Ivana; Žvanut, Boštjan

    2016-07-01

    Clinical practice is essential to nursing education as it provides experience with patients and work environments that prepare students for future work as nurses. The aim of this study was to translate the "Clinical Learning Environment, Supervision and Nurse Teacher" questionnaire in Croatian language and test its validity and reliability in practice. The study was performed at the Faculty of medicine, Josip Juraj Strossmayer University of Osijek, Croatia in April 2014. The translated questionnaire was submitted to 136 nursing students: 20 males and 116 females. Our results reflected a slightly different factor structure, consisting of four factors. All translated items of the original constructs "Supervisory relationship", "Role of nurse teacher" and "Leadership style of the ward manager" loaded on factor 1. Items of "Pedagogical atmosphere on the ward" are distributed on two factors (3 and 4). The items of "Premises of nursing on the ward" loaded on factor 2. Three items were identified as problematic and iteratively removed from the analysis. The translated version of the aforementioned questionnaire has properties suitable for the evaluation of clinical practice for nursing students within a Croatian context and reflects the specifics of the nursing clinical education in this country.

  3. 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.

  4. Problems and Guidelines of Strategy Implementation in Basic Educational Institutions under the Supervision of KhonKaen Primary Educational Service Area Office 4

    Directory of Open Access Journals (Sweden)

    Sasiwan Tonkanya

    2016-09-01

    role models for the staff to be always alert and ready for the changes ; 8 local environments, cultures, and people’s ways of life are learned ; 9 the meetings are held to appoint the committee to supervise, suggest, give directions on the document operations, control and follow up the plans regularly ; and 10 tripartite is used in mutual supervision through the principle of good companions.

  5. A neuron model with trainable activation function (TAF) and its MFNN supervised learning

    Institute of Scientific and Technical Information of China (English)

    吴佑寿; 赵明生

    2001-01-01

    This paper addresses a new kind of neuron model, which has trainable activation function (TAF) in addition to only trainable weights in the conventional M-P model. The final neuron activation function can be derived from a primitive neuron activation function by training. The BP like learning algorithm has been presented for MFNN constructed by neurons of TAF model. Several simulation examples are given to show the network capacity and performance advantages of the new MFNN in comparison with that of conventional sigmoid MFNN.

  6. Anticipatory Driving for a Robot-Car Based on Supervised Learning

    DEFF Research Database (Denmark)

    Markelic, I.; Kulvicius, Tomas; Tamosiunaite, M.

    2009-01-01

    Using look ahead information and plan making improves hu- man driving. We therefore propose that also autonomously driving systems should dispose over such abilities. We adapt a machine learning approach, where the system, a car-like robot, is trained by an experienced driver by correlating visual...... adapt a two-level ap- proach, where the result of the database is combined with an additional reactive controller for robust behavior. Concerning velocity control this paper makes a novel contribution which is the ability of the system to react adequatly to upcoming curves...

  7. Anticipatory Driving for a Robot-Car Based on Supervised Learning

    DEFF Research Database (Denmark)

    Markelic, I.; Kulvicius, Tomas; Tamosiunaite, M.

    2009-01-01

    Using look ahead information and plan making improves hu- man driving. We therefore propose that also autonomously driving systems should dispose over such abilities. We adapt a machine learning approach, where the system, a car-like robot, is trained by an experienced driver by correlating visual...... adapt a two-level ap- proach, where the result of the database is combined with an additional reactive controller for robust behavior. Concerning velocity control this paper makes a novel contribution which is the ability of the system to react adequatly to upcoming curves...

  8. What students learn when studying physics practice exam problems

    Directory of Open Access Journals (Sweden)

    Witat Fakcharoenphol,

    2011-05-01

    Full Text Available We developed a web-based tool to provide students with access to old exam problems and solutions. By controlling the order in which students saw the problems, as well as their access to solutions, we obtained data about student learning by studying old exam problems. Our data suggest that in general students learn from doing old exam problems, and that having access to the problem solutions increases their learning. However, the data also suggest the depth of learning may be relatively shallow. In addition, the data show that doing old exam problems provides important formative assessment about the student’s overall preparedness for the exam and their particular areas of strength and weakness.

  9. An Educational Approach to Problem-based Learning

    Directory of Open Access Journals (Sweden)

    Nan-Chieh Chen

    2008-03-01

    Full Text Available This paper provides an analysis of the educational framework of problem-based learning (PBL. As known and used, PBL finds its root in the Structuralism and Pragmatism schools of philosophy. In this paper, the three main requirements of PBL, namely learning by doing, learning in context, and focusing on the student, are discussed within the context of these two schools of thought. Given these attributes, PBL also seems ideally suited for use in learning bioethics.

  10. Getting to the Root of the Problem in Experiential Learning: Using Problem Solving and Collective Reflection to Improve Learning Outcomes

    Science.gov (United States)

    Miller, Richard J.; Maellaro, Rosemary

    2016-01-01

    Experiential learning alone does not guarantee that students will accurately conceptualize content, or meet course outcomes in subsequent active experimentation stages. In an effort to more effectively meet learning objectives, the experiential learning cycle was modified with a unique combination of the 5 Whys root cause problem-solving tool and…

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

    Science.gov (United States)

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

    2016-12-01

    In the era of large astronomical surveys, photometric classification of supernovae (SNe) has become an important research field due to limited spectroscopic resources for candidate follow-up and classification. In this work, we present a method to photometrically classify type Ia supernovae based on machine learning with redshifts that are derived from the SN light-curves. This method is implemented on real data from the SNLS deferred pipeline, a purely photometric pipeline that identifies SNe Ia at high-redshifts (0.2 Random Forest and Boosted Decision Trees. We evaluate the performance using SN simulations and real data from the first 3 years of the Supernova Legacy Survey (SNLS), which contains large spectroscopically and photometrically classified type Ia samples. Using the Area Under the Curve (AUC) metric, where perfect classification is given by 1, we find that our best-performing classifier (Extreme Gradient Boosting Decision Tree) has an AUC of 0.98.We show that it is possible to obtain a large photometrically selected type Ia SN sample with an estimated contamination of less than 5%. When applied to data from the first three years of SNLS, we obtain 529 events. We investigate the differences between classifying simulated SNe, and real SN survey data. In particular, we find that applying a thorough set of selection cuts to the SN sample is essential for good classification. This work demonstrates for the first time the feasibility of machine learning classification in a high-z SN survey with application to real SN data.

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

    Science.gov (United States)

    Reynen, Andrew; Audet, Pascal

    2017-09-01

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

  13. Problems in Problem-Based Learning - Experiences, Analysis and Lessons Learned on an Introductory Programming Course

    Directory of Open Access Journals (Sweden)

    Päivi KINNUNEN

    2005-10-01

    Full Text Available We have applied Problem-Based Learning (PBL on an introductory programming course for several years with positive results. In this paper we present the outcomes and discuss our experiences of applying a modified version of PBL such that needs less tutoring resources and could better be used in large-scale courses, too. PBL has many positive effects on studying: Students report that they liked the social aspect of studying in a group. Generally students appreciated the possibility to be active participants in a course. On the other hand, group dynamic difficulties, tolerance of uncertainty and demanding studying skills caused problems that were too hard to overcome to some students. In this paper we introduce different versions of PBL, discuss efficiently and inefficiently working PBL groups and present their characters. We also discuss the possible reasons for differently working groups. Finally, we give some suggestions for interventions that might help the PBL groups to work better.

  14. Comparisons of Learning Achievement and Problem-Solving Thinking Abilities of Vocational Certificate Level 1 Students between Learning Management of Creative Problem Solving and Conventional Learning Approaches

    Directory of Open Access Journals (Sweden)

    Komkrit Chookhom

    2016-10-01

    Full Text Available The purposes of this study aimed to: 1 develop plan for creative problem solving learning activities with a required efficiency of 80/80, 2 find out effectiveness indices of the plan for creative problem solving learning activities, 3 compare learning achievements, problem-solving thinking abilities of the students who learned using creative problem solving learning activities between pretest and posttest, 4 compare learning achievements, problem-solving thinking abilities of the students who learned using conventional learning approaches between pretest and posttest, and 5 compare learning achievements, problem-solving thinking abilities of the students who learned using creative problem solving learning activities and those who conventional learning approaches, The samples for this study consisted of vocational certificate level 1 students in second semester of the academic year of 2014, Kantharalak Technical College. They were selected using the cluster random sampling technique and randomly divided into two groups: the experimental group one learning by creative problem solving learning activities, and the other one experimental group learning by conventional learning approaches. The tools used in the study were: 1 plans for creative problem solving learning activities and conventional Learning approaches, The instruments used in this study were: 6 units, 3 hours per week in each plan, for a total of 18 hours of teaching 2 a 30 items 4 choice achievement test with discriminating powers (B ranging 0.22-0.63 with a reliability (rcc of 0.86, and 3 a 28 item 4 choice test of problem-solving thinking with discriminating powers (B ranging 0.22-0.71 with a reliability (rcc of 0.76. Basic statistics used for analyzing the collected data were percentage, mean, and standard deviation, and the statistic used for testing the hypotheses was Hotelling’s T2 . The results of the study were as follows: 1. The efficiencies of the plan for creative problem

  15. Applying Learning Strategy Questionnaires: Problems and Possibilities

    Science.gov (United States)

    Schellings, Gonny

    2011-01-01

    This article discusses measuring learning strategies by means of questionnaires. In "multi-method" research, in which think-aloud measures are compared with questionnaires, low or moderate correlations are found. A conclusion often drawn is that learners are not able to verbally report on their learning activities. Alternative explanations concern…

  16. Applying Learning Strategy Questionnaires: Problems and Possibilities

    Science.gov (United States)

    Schellings, Gonny

    2011-01-01

    This article discusses measuring learning strategies by means of questionnaires. In "multi-method" research, in which think-aloud measures are compared with questionnaires, low or moderate correlations are found. A conclusion often drawn is that learners are not able to verbally report on their learning activities. Alternative explanations concern…

  17. Managing the Gap between Curriculum Based and Problem Based Learning

    DEFF Research Database (Denmark)

    Bygholm, Ann; Buus, Lillian

    2009-01-01

    Traditionally there has been a clear distinction between curriculum based and problem based approaches to accomplish learning. Preferred approaches depend of course on conviction, culture, traditions and also on the specific learning situation. We will argue that it is not a question of either....../or but rather both/and. In this paper we describe an approach to design and delivery of online courses in computer science which on the one hand is based on a specified curriculum and on the other hand gives room for different learning strategies, problem based learning being one of them. We discuss...... the challenges in applying problem based learning strategies in a context where several universities, with different cultures of teaching, collaboratively develop and deliver online courses. We present a pedagogical framework embracing both problem based and curriculum based strategies and show how we used...

  18. Random Group Problem-Based Learning in Engineering Dynamics

    CERN Document Server

    Fleischfresser, Luciano

    2014-01-01

    Dynamics problem solving is highly specific to the problem at hand and to develop the general mind framework to become an effective problem solver requires ingenuity and creativity on top of a solid grounding on theoretical and conceptual knowledge. A blended approach with prototype demo, problem-based learning, and an opinion questionnaire was used during first semester of 2013. Students working in randomly selected teams had to interact with classmates while solving a randomly selected problem. The approach helps improve awareness of what is important to learn in this class while reducing grading load. It also provides a more rewarding contact time for both pupils and instructor.

  19. Representation learning for cross-modality classification

    NARCIS (Netherlands)

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

    2017-01-01

    textabstractDifferences in scanning parameters or modalities can complicate image analysis based on supervised classification. This paper presents two representation learning approaches, based on autoencoders, that address this problem by learning representations that are similar across domains. Bot

  20. Situated Cognition and Problem-Based Learning: Implications for Learning and Instruction with Technology.

    Science.gov (United States)

    Hung, David

    2002-01-01

    Reviews the foundational principles of situated cognition and substantiates its theoretical underpinnings with a transactional worldview; draws connections between situated cognition and problem-based learning; and draws implications from situated cognition and problem-based learning to learning and instruction with technology. Suggests that…

  1. Deep and Surface Learning in Problem-Based Learning: A Review of the Literature

    Science.gov (United States)

    Dolmans, Diana H. J. M.; Loyens, Sofie M. M.; Marcq, Hélène; Gijbels, David

    2016-01-01

    In problem-based learning (PBL), implemented worldwide, students learn by discussing professionally relevant problems enhancing application and integration of knowledge, which is assumed to encourage students towards a deep learning approach in which students are intrinsically interested and try to understand what is being studied. This review…

  2. Deep and Surface Learning in Problem-Based Learning: A Review of the Literature

    Science.gov (United States)

    Dolmans, Diana H. J. M.; Loyens, Sofie M. M.; Marcq, Hélène; Gijbels, David

    2016-01-01

    In problem-based learning (PBL), implemented worldwide, students learn by discussing professionally relevant problems enhancing application and integration of knowledge, which is assumed to encourage students towards a deep learning approach in which students are intrinsically interested and try to understand what is being studied. This review…

  3. Enhancing Motivation for Overcoming Learning and Behavior Problems.

    Science.gov (United States)

    Adelman, Howard S.; Taylor, Linda

    1983-01-01

    The paper highlights motivation itself as a problem, suggests times when correction of the motivation problem should be the initial focus of intervention, and describes a framework and specific procedures for enhancing positive motivation and reducing avoidance as key aspects of correcting students' learning and behavior problems. (Author/MC)

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

    Institute of Scientific and Technical Information of China (English)

    李宜祥; 邢大伟; 沈广元

    2001-01-01

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

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

    Science.gov (United States)

    Singh, Swadha; Singh, Raghvendra

    2016-04-03

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

  6. 论看守所监管工作存在的问题与改革%The Existing Problems and Reform of Detention Supervision

    Institute of Scientific and Technical Information of China (English)

    周涛

    2012-01-01

    With the process of social rule of law and the increased social supervision,standard,legal and civilized detention supervision work is strongly urged.People pay more attention to the problems of long-standing detention supervision,such as suicide,self-mutilation,disguised extended detention,torture and other issues.The police are in difficult situation when they should not only improve positively,but also be faced with police officers’ pressure and the relationship with investigative organ and mass media.Therefore,it is the direction and emphasis of reform to establish a rational legal thought,coordinate the relationship between fairness and efficiency and lay stress on leader’s decision and police officer’s sense of responsibility.%随着社会法治进程的推进和社会监督透明度的提高,对看守所监管工作的规范化、法制化和文明化呼声日趋强烈。看守所监管工作中长期存在的自杀、自残、变相超期羁押、刑讯逼供等问题在媒体曝光下备受关注;看守所在积极改进的同时,又要应对高压态势下民警的工作压力、与刑侦机关及媒体的关系等新问题,倍感艰难。因此,树立理性的法律思维、协调好公平与效率的关系、重视领导者的科学决策和民警的责任意识,是看守所监管工作改革的方向与重点。

  7. New Realities and the Implications for Problem Based Learning

    DEFF Research Database (Denmark)

    Sørensen, Olav Jull

    2004-01-01

    The background for this article is the big changes we have experienced since the idea of the problem based learning model was conceived at Aalborg University in the beginning of the 1970s. Assuming that no teaching model is context free, the challenge is to confront the basics of the problem......-life learning and, finally, the integrative use of ICT (Information and Communication Technologies) and the Internet. If these modifications are made, the problem-based learning model will be able to cope with the new realities to a larger extent than more conventional university models....

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

    Institute of Scientific and Technical Information of China (English)

    高春玉

    2011-01-01

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

  9. What Students Learn in Problem-Based Learning: A Process Analysis

    Science.gov (United States)

    Yew, Elaine H. J.; Schmidt, Henk G.

    2012-01-01

    This study aimed to provide an account of how learning takes place in problem-based learning (PBL), and to identify the relationships between the learning-oriented activities of students with their learning outcomes. First, the verbal interactions and computer resources studied by nine students for an entire PBL cycle were recorded. The relevant…

  10. What students learn in problem-based learning: a process analysis

    NARCIS (Netherlands)

    E.H.J. Yew (Elaine); H.G. Schmidt (Henk)

    2011-01-01

    textabstractThis study aimed to provide an account of how learning takes place in problem-based learning (PBL), and to identify the relationships between the learning-oriented activities of students with their learning outcomes. First, the verbal interactions and computer resources studied by nine s

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

    Science.gov (United States)

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

    2017-03-01

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

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

    Science.gov (United States)

    Uezu, Tatsuya; Kiyokawa, Shuji

    2016-06-01

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

  13. EMD-Based Temporal and Spectral Features for the Classification of EEG Signals Using Supervised Learning.

    Science.gov (United States)

    Riaz, Farhan; Hassan, Ali; Rehman, Saad; Niazi, Imran Khan; Dremstrup, Kim

    2016-01-01

    This paper presents a novel method for feature extraction from electroencephalogram (EEG) signals using empirical mode decomposition (EMD). Its use is motivated by the fact that the EMD gives an effective time-frequency analysis of nonstationary signals. The intrinsic mode functions (IMF) obtained as a result of EMD give the decomposition of a signal according to its frequency components. We present the usage of upto third order temporal moments, and spectral features including spectral centroid, coefficient of variation and the spectral skew of the IMFs for feature extraction from EEG signals. These features are physiologically relevant given that the normal EEG signals have different temporal and spectral centroids, dispersions and symmetries when compared with the pathological EEG signals. The calculated features are fed into the standard support vector machine (SVM) for classification purposes. The performance of the proposed method is studied on a publicly available dataset which is designed to handle various classification problems including the identification of epilepsy patients and detection of seizures. Experiments show that good classification results are obtained using the proposed methodology for the classification of EEG signals. Our proposed method also compares favorably to other state-of-the-art feature extraction methods.

  14. 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.

  15. Group supervision for general practitioners

    DEFF Research Database (Denmark)

    Galina Nielsen, Helena; Sofie Davidsen, Annette; Dalsted, Rikke;

    2013-01-01

    AIM: Group supervision is a sparsely researched method for professional development in general practice. The aim of this study was to explore general practitioners' (GPs') experiences of the benefits of group supervision for improving the treatment of mental disorders. METHODS: One long...... considered important prerequisites for disclosing and discussing professional problems. CONCLUSION: The results of this study indicate that participation in a supervision group can be beneficial for maintaining and developing GPs' skills in dealing with patients with mental health problems. Group supervision......-established supervision group was studied closely for six months by observing the group sessions, and by interviewing GPs and their supervisors, individually and collectively. The interviews were recorded digitally and transcribed verbatim. The data were analysed using systematic text condensation. RESULTS: The GPs found...

  16. Problems Implementing Problem-Based Learning by a Private Malaysian University

    Science.gov (United States)

    Tik, Chan Chang

    2014-01-01

    In this case study the focus is on lecturers' readiness in the design of PBL problems and to facilitate students' learning. This paper also looks into students' readiness in terms of acquiring metacognitive skills and collaborating in group to solve PBL problems. Problems encountered by both lecturers and students are discussed in the context of…

  17. Problems Implementing Problem-Based Learning by a Private Malaysian University

    Directory of Open Access Journals (Sweden)

    Chan Chang Tik

    2014-06-01

    Full Text Available In this case study the focus is on lecturers’ readiness in the design of PBL problems and to facilitate students’ learning. This paper also looks into students’ readiness in terms of acquiring metacognitive skills and collaborating in group to solve PBL problems. Problems encountered by both lecturers and students are discussed in the context of the chosen private Malaysian university.

  18. Teaching and Learning through Problem Solving

    Science.gov (United States)

    Ollerton, Mike

    2007-01-01

    In this article, the author relates some problem solving work with primary schools to Department for Children, Schools, and Families (DfES) support. In four primary schools in the West Midlands, the focus was teaching mathematics through problem solving, based on materials published on the DfES "standards" website. The author noticed…

  19. Problem-based Learning in a Competency-based World.

    Science.gov (United States)

    Bechtel, Gregory A.; Davidhizar, Ruth; Bradshaw, Martha J.

    1999-01-01

    Problem-based learning emphasizes critical thinking and clinical judgment. Competency-based education focuses on clinical competence. A merger of the two in nursing education could generate higher levels of inquiry and more expert clinicians. (SK)

  20. Introducing problem-based learning (PBL) into a foundation ...

    African Journals Online (AJOL)

    Institute for Mathematics and Science Teaching, Stellenbosch University, South Africa ... Problem-based Learning (PBL) approach in a one-year foundation ..... An open-ended questionnaire which consisted of 7 questions targeted the 2006.

  1. Problem Based Learning and Education for Sustainable Development

    DEFF Research Database (Denmark)

    Guerra, Aida

    2012-01-01

    scientific and technological knowledge but instead align it with the social, economic and environmental dimensions of the professional practice. It is agreed among ESD experts that PBL is a suitable learning approach to integrate ESD in the engineering curricula. This paper provides an overview......An engineering education that integrates education for sustainable development (ESD) perspectives claims for a learning approach centred on students, interdisciplinary and problem oriented, and this cannot be achieve through an “add on” strategy. Example of such active learning approaches...... are problem based learning (PBL), project based learning (PjBL), case study, role play, group discussion, field work, etc. These learning approaches are capable of integrating education for sustainable development (ESD) in engineering education at different levels, and do not aim to substitute the core...

  2. An e-learning approach to informed problem solving

    Directory of Open Access Journals (Sweden)

    Georg Weichhart

    2012-06-01

    Full Text Available When taking into account individualized learning processes not only content and interaction facilities need to be re-considered, but also the design of learning processes per se. Besides explicitness of learning objectives, interactive means of education need to enable intertwining content and communication elements as basic elements of active learning in a flexible way while preserving a certain structure of the learning process. Intelligibility Catchers are a theoretically grounded framework to enable such individualized processes. It allows learners and teachers agreeing and determining a desired learning outcome in written form. This type of e-learning contract enables students to individually explore content and participate in social interactions, while being guided by a transparent learning process structure. The developed implementation empowers learners in terms of creative problem-solving capabilities, and requires adaptation of classroom situations. The framework and its supporting semantic e-learning environment not only enables diverse learning and problem solving processes, but also supports the collaborative construction of e-learning contracts.

  3. Performance Monitoring Applied to System Supervision

    Directory of Open Access Journals (Sweden)

    Bertille Somon

    2017-07-01

    Full Text Available Nowadays, automation is present in every aspect of our daily life and has some benefits. Nonetheless, empirical data suggest that traditional automation has many negative performance and safety consequences as it changed task performers into task supervisors. In this context, we propose to use recent insights into the anatomical and neurophysiological substrates of action monitoring in humans, to help further characterize performance monitoring during system supervision. Error monitoring is critical for humans to learn from the consequences of their actions. A wide variety of studies have shown that the error monitoring system is involved not only in our own errors, but also in the errors of others. We hypothesize that the neurobiological correlates of the self-performance monitoring activity can be applied to system supervision. At a larger scale, a better understanding of system supervision may allow its negative effects to be anticipated or even countered. This review is divided into three main parts. First, we assess the neurophysiological correlates of self-performance monitoring and their characteristics during error execution. Then, we extend these results to include performance monitoring and error observation of others or of systems. Finally, we provide further directions in the study of system supervision and assess the limits preventing us from studying a well-known phenomenon: the Out-Of-the-Loop (OOL performance problem.

  4. On stochastic approximation algorithms for classes of PAC learning problems

    Energy Technology Data Exchange (ETDEWEB)

    Rao, N.S.V.; Uppuluri, V.R.R.; Oblow, E.M.

    1994-03-01

    The classical stochastic approximation methods are shown to yield algorithms to solve several formulations of the PAC learning problem defined on the domain [o,1]{sup d}. Under some assumptions on different ability of the probability measure functions, simple algorithms to solve some PAC learning problems are proposed based on networks of non-polynomial units (e.g. artificial neural networks). Conditions on the sizes of these samples required to ensure the error bounds are derived using martingale inequalities.

  5. The implementation of problem-based learning in collaborative groups in a chiropractic program in Malaysia.

    Science.gov (United States)

    Win, Ni Ni; Nadarajah, Vishna Devi V; Win, Daw Khin

    2015-01-01

    Problem-based learning (PBL) is usually conducted in small-group learning sessions with approximately eight students per facilitator. In this study, we implemented a modified version of PBL involving collaborative groups in an undergraduate chiropractic program and assessed its pedagogical effectiveness. This study was conducted at the International Medical University, Kuala Lumpur, Malaysia, and involved the 2012 chiropractic student cohort. Six PBL cases were provided to chiropractic students, consisting of three PBL cases for which learning resources were provided and another three PBL cases for which learning resources were not provided. Group discussions were not continuously supervised, since only one facilitator was present. The students' perceptions of PBL in collaborative groups were assessed with a questionnaire that was divided into three domains: motivation, cognitive skills, and perceived pressure to work. Thirty of the 31 students (97%) participated in the study. PBL in collaborative groups was significantly associated with positive responses regarding students' motivation, cognitive skills, and perceived pressure to work (Plearning resources increased motivation and cognitive skills (Plearning resources.

  6. EFFECT OF PROBLEM BASED LEARNING AND MODEL CRITICAL THINKING ABILITY TO PROBLEM SOLVING SKILLS

    Directory of Open Access Journals (Sweden)

    Unita S. Zuliani Nasution

    2016-12-01

    Full Text Available The purposes of this research were to analyze the different between physic resolving problem ability by using problem based learning model and direct instruction model, the different of physic resolving problem ability between the students that have critical thinking ability upper the average and the students that have critical thinking ability under the average, and the interaction of problem based learning model toward critical thinking ability and students’ physic resolving problem ability. This research was quasy experimental research that use critical thinking ability tests and physic resolving problem ability tests as the instruments. Result of the research showed that the students’ physic resolving problem ability by using problem based learning model was better than by using direct instruction model, students’ physic resolving problem ability and critical thinking ability upper the average showed better different and result than students’ critical thinking ability under the average, besides there was an interaction between problem based learning model and critical thinking ability in improving students’ physic resolving problem ability.

  7. Designing Teaching Materials for Learning Problem Solving in Technology Education

    NARCIS (Netherlands)

    Doornekamp, B.G.

    2001-01-01

    In the process of designing teaching materials for learning problem solving in technology education, domain-specific design specifications are considered important elements to raise learning outcomes with these materials. Two domain-specific design specifications were drawn up using a four-step proc

  8. Implementation of Problem-Based Learning in Environmental Chemistry

    Science.gov (United States)

    Jansson, Stina; So¨derstro¨m, Hanna; Andersson, Patrik L.; Nording, Malin L.

    2015-01-01

    Environmental Chemistry covers a range of topics within the discipline of chemistry, from toxicology to legislation, which warrants interdisciplinary study. Consequently, problem-based learning (PBL), a style of student-centered learning which facilitates the integration of multiple subjects, was investigated to determine if it would be a more…

  9. Designing Teaching Materials for Learning Problem Solving in Technology Education

    NARCIS (Netherlands)

    Doornekamp, B.G.

    2001-01-01

    In the process of designing teaching materials for learning problem solving in technology education, domain-specific design specifications are considered important elements to raise learning outcomes with these materials. Two domain-specific design specifications were drawn up using a four-step proc

  10. Designing Teaching Materials for Learning Problem Solving in Technology Education

    NARCIS (Netherlands)

    Doornekamp, B.G.

    In the process of designing teaching materials for learning problem solving in technology education, domain-specific design specifications are considered important elements to raise learning outcomes with these materials. Two domain-specific design specifications were drawn up using a four-step

  11. ICT in Problem- and Project-based Learning

    DEFF Research Database (Denmark)

    Nielsen, Jørgen Lerche; Birch Andreasen, Lars

    2012-01-01

    The paper discusses how teaching and learning practices at universities can implement new information technologies, inspired by the traditions of problem- and project-based learning. The changing roles in the teacher-student relationship, and students’ development of information literacy...

  12. Foundations of problem-based learning: Some explanatory notes

    NARCIS (Netherlands)

    H.G. Schmidt (Henk)

    1993-01-01

    textabstractThe present article elaborates on cognitive effects of problem-based learning put forward by Schmidt, De Volder, De Grave, Moust & Patel (1989) and Norman & Schmidt (1992). Its purpose is to discuss, in some detail, the theoretical premises of this approach to learning and instruction.

  13. Implementation of Problem-Based Learning in Environmental Chemistry

    Science.gov (United States)

    Jansson, Stina; So¨derstro¨m, Hanna; Andersson, Patrik L.; Nording, Malin L.

    2015-01-01

    Environmental Chemistry covers a range of topics within the discipline of chemistry, from toxicology to legislation, which warrants interdisciplinary study. Consequently, problem-based learning (PBL), a style of student-centered learning which facilitates the integration of multiple subjects, was investigated to determine if it would be a more…

  14. ICT in Problem- and Project-based Learning

    DEFF Research Database (Denmark)

    Andreasen, Lars Birch; Lerche Nielsen, Jørgen

    2012-01-01

    The paper discusses how teaching and learning practices at universities can implement new information technologies, inspired by the traditions of problem- and project-based learning. The changing roles in the teacher-student relationship, and students’ development of information literacy...

  15. Problems in the Deployment of Learning Networks In Small Organizations

    NARCIS (Netherlands)

    Shankle, Dean E.; Shankle, Jeremy P.

    2006-01-01

    Please, cite this publication as: Shankle, D.E., & Shankle, J.P. (2006). Problems in the Deployment of Learning Networks In Small Organizations. Proceedings of International Workshop in Learning Networks for Lifelong Competence Development, TENCompetence Conference. March 30th-31st, Sofia, Bulgaria:

  16. Problems in the Deployment of Learning Networks In Small Organizations

    OpenAIRE

    Shankle, Dean E.; Shankle, Jeremy P.

    2006-01-01

    Please, cite this publication as: Shankle, D.E., & Shankle, J.P. (2006). Problems in the Deployment of Learning Networks In Small Organizations. Proceedings of International Workshop in Learning Networks for Lifelong Competence Development, TENCompetence Conference. March 30th-31st, Sofia, Bulgaria: TENCompetence. Retrieved June 30th, 2006, from http://dspace.learningnetworks.org

  17. Impact of Problem-Based Learning to Students and Teachers

    Science.gov (United States)

    Hirca, Necati

    2011-01-01

    The Ministry of National Education of Turkey has decided to give up traditional methods to be used in the classes and to develop a new secondary school curriculum based on Context-Based Learning (CBL) in 2007. This paper discusses integrating Problem-Based Learning (PBL) tasks into the new physics curriculum in Turkey. A brief overview of a…

  18. The Guinea Pigs of a Problem-Based Learning Curriculum

    Science.gov (United States)

    Reddy, Sarasvathie; McKenna, Sioux

    2016-01-01

    Participants in a study on learning the clinical aspects of medicine in a problem-based learning (PBL) curriculum repeatedly referred to themselves as "Guinea pigs" at the mercy of a curriculum experiment. This article interrogates and problematises the "Guinea pig" identity ascribed to and assumed by the first cohort of…

  19. Evaluation of a Problem Based Learning Curriculum Using Content Analysis

    Science.gov (United States)

    Prihatiningsih, Titi Savitri; Qomariyah, Nurul

    2016-01-01

    Faculty of Medicine UGM has implemented Problem Based Learning (PBL) since 1985. Seven jump tutorial discussions are applied. A scenario is used as a trigger to stimulate students to identify learning objectives (LOs) in step five which are used as the basis for self study in step six. For each scenario, the Block Team formulates the LOs which are…

  20. Nursing students' perceptions of effective problem-based learning tutors.

    Science.gov (United States)

    Matthew-Maich, Nancy; Martin, Lynn; Hammond, Cynthia; Palma, Amy; Pavkovic, Maria; Sheremet, Darlene; Roche, Carmen

    2016-11-16

    Aim To explore baccalaureate nursing students' perceptions of what makes an effective tutor in problem-based learning courses, and the influence of effective teaching on students' learning and experience. Method Students enrolled in all four years of a baccalaureate nursing programme completed online surveys (n=511) and participated in focus groups (n=19). Data were analysed and combined using content analysis. Findings The data were summarised using five themes, the '5 Ps' of effective teaching in problem-based learning. Nursing students perceived effective problem-based learning tutors to be prepared with knowledge and facilitation skills, person-centred, passionate, professional and able to prepare students for success in the nursing programme. Effective tutors adjusted their approaches to students throughout the four years of the nursing programme. Conclusion Effective teaching in problem-based learning is essential and has significant effects on nursing students' learning, motivation and experience. Important attributes, skills and strategies of effective problem-based learning tutors were identified and may be used to enhance teaching and plan professional development initiatives.

  1. Extracting PICO Sentences from Clinical Trial Reports using Supervised Distant Supervision.

    Science.gov (United States)

    Wallace, Byron C; Kuiper, Joël; Sharma, Aakash; Zhu, Mingxi Brian; Marshall, Iain J

    2016-01-01

    Systematic reviews underpin Evidence Based Medicine (EBM) by addressing precise clinical questions via comprehensive synthesis of all relevant published evidence. Authors of systematic reviews typically define a Population/Problem, Intervention, Comparator, and Outcome (a PICO criteria) of interest, and then retrieve, appraise and synthesize results from all reports of clinical trials that meet these criteria. Identifying PICO elements in the full-texts of trial reports is thus a critical yet time-consuming step in the systematic review process. We seek to expedite evidence synthesis by developing machine learning models to automatically extract sentences from articles relevant to PICO elements. Collecting a large corpus of training data for this task would be prohibitively expensive. Therefore, we derive distant supervision (DS) with which to train models using previously conducted reviews. DS entails heuristically deriving 'soft' labels from an available structured resource. However, we have access only to unstructured, free-text summaries of PICO elements for corresponding articles; we must derive from these the desired sentence-level annotations. To this end, we propose a novel method - supervised distant supervision (SDS) - that uses a small amount of direct supervision to better exploit a large corpus of distantly labeled instances by learning to pseudo-annotate articles using the available DS. We show that this approach tends to outperform existing methods with respect to automated PICO extraction.

  2. Applying learning strategy questionnaires: problems and possibilities

    NARCIS (Netherlands)

    Schellings, G.

    2011-01-01

    This article discusses measuring learning strategies by means of questionnaires. In ‘multi-method’ research, in which think-aloud measures are compared with questionnaires, low or moderate correlations are found. A conclusion often drawn is that learners are not able to verbally report on their lear

  3. Adaptive functioning in children with epilepsy and learning problems.

    Science.gov (United States)

    Buelow, Janice M; Perkins, Susan M; Johnson, Cynthia S; Byars, Anna W; Fastenau, Philip S; Dunn, David W; Austin, Joan K

    2012-10-01

    In the study we describe adaptive functioning in children with epilepsy whose primary caregivers identified them as having learning problems. This was a cross-sectional study of 50 children with epilepsy and learning problems. Caregivers supplied information regarding the child's adaptive functioning and behavior problems. Children rated their self-concept and completed a battery of neuropsychological tests. Mean estimated IQ (PPVT-III) in the participant children was 72.8 (SD = 18.3). On average, children scored 2 standard deviations below the norm on the Vineland Adaptive Behavior Scale-II and this was true even for children with epilepsy who had estimated IQ in the normal range. In conclusion, children with epilepsy and learning problems had relatively low adaptive functioning scores and substantial neuropsychological and mental health problems. In epilepsy, adaptive behavior screening can be very informative and guide further evaluation and intervention, even in those children whose IQ is in the normal range.

  4. PERBANDINGAN HASIL BELAJAR DISCOVERY LEARNING BERBASIS PROBLEM SOLVING DAN GROUP INVESTIGATION BERBASIS PROBLEM SOLVING PADA PEMBELAJARAN METODE NUMERIK

    Directory of Open Access Journals (Sweden)

    Ira Vahlia

    2016-06-01

    Full Text Available Discovery learning is a learning that involves students in the process of mental activity through a brainstorm, to discuss, and try it yourself, so that children can learn to be independent. Resulting in the discovery-based learning problem solving is to let children discover things on their own and group investigation that emphasizes problem solving based on a debriefing activities and student participation. The classes drawn into the sample, the class as a class given control based discovery learning and problem solving one class another group investigation given learning-based problem solving. Data analysis techniques in this study using Ttes. From the results of this study concluded that: (a In the first experiment class that implements learning model-based problem solving group investigation obtained average value of learning outcomes at 73.10 while the experimental class II implementing discovery-based learning problem solving obtained average value postest average of 66.55. (b There are significant differences between the results of student learning in the class that implements the discovery-based learning and problem solving class that implements the learning model based problem solving group investigation. Student results in learning problem solving based group investigation better on the class that implements the model-based discovery learning problem solving.  Keywords: discovery learning, group investigation, learning numerical methods,  problem solving

  5. Use of auditory learning to manage listening problems in children.

    Science.gov (United States)

    Moore, David R; Halliday, Lorna F; Amitay, Sygal

    2009-02-12

    This paper reviews recent studies that have used adaptive auditory training to address communication problems experienced by some children in their everyday life. It considers the auditory contribution to developmental listening and language problems and the underlying principles of auditory learning that may drive further refinement of auditory learning applications. Following strong claims that language and listening skills in children could be improved by auditory learning, researchers have debated what aspect of training contributed to the improvement and even whether the claimed improvements reflect primarily a retest effect on the skill measures. Key to understanding this research have been more circumscribed studies of the transfer of learning and the use of multiple control groups to examine auditory and non-auditory contributions to the learning. Significant auditory learning can occur during relatively brief periods of training. As children mature, their ability to train improves, but the relation between the duration of training, amount of learning and benefit remains unclear. Individual differences in initial performance and amount of subsequent learning advocate tailoring training to individual learners. The mechanisms of learning remain obscure, especially in children, but it appears that the development of cognitive skills is of at least equal importance to the refinement of sensory processing. Promotion of retention and transfer of learning are major goals for further research.

  6. Project-Organized Problem-Based Learning in Distance Education

    DEFF Research Database (Denmark)

    Jensen, Lars Peter; Helbo, Jan; Knudsen, Morten

    2003-01-01

    in a new Master of Information Technology education. The main conclusions are, that the project work is a strong learning motivator, enhancing peer collaboration, for off-campus students as well. However, the concept cannot be directly transferred to off-campus learning. In this paper, the main problems......Project organized problem based learning is a successful concept for on-campus engineering education at Aalborg University. Recently this "Aalborg concept" has been used in networked distance education as well. This paper describes the experiences from two years of Internet-mediated project work...

  7. 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.

  8. Learning with MOOCs: promises and problems

    OpenAIRE

    Steffen, Karl

    2016-01-01

    Massive Open Online Courses (MOOCs) may be considered to be a new form of virtual technology enhanced learning environments. Since their first appearance in 2008, the increase in the number of MOOCs has been dramatic. The hype about MOOCs was accompanied by great expectations: 2012 was named the Year of the MOOCs and it was expected that MOOCs would revolutionise higher education. Two types of MOOCs may be distinguished: cMOOCs as proposed by Siemens, based on his ideas of connectivism, a...

  9. Learning via problem solving in mathematics education

    OpenAIRE

    Piet Human

    2009-01-01

    Three forms of mathematics education at school level are distinguished: direct expository teaching with an emphasis on procedures, with the expectation that learners will at some later stage make logical and functional sense of what they have learnt and practised (the prevalent form), mathematically rigorous teaching in terms of fundamental mathematical concepts, as in the so-called “modern mathematics” programmes of the sixties, teaching and learning in the context of engaging with meaningfu...

  10. Bias Modeling for Distantly Supervised Relation Extraction

    Directory of Open Access Journals (Sweden)

    Yang Xiang

    2015-01-01

    Full Text Available Distant supervision (DS automatically annotates free text with relation mentions from existing knowledge bases (KBs, providing a way to alleviate the problem of insufficient training data for relation extraction in natural language processing (NLP. However, the heuristic annotation process does not guarantee the correctness of the generated labels, promoting a hot research issue on how to efficiently make use of the noisy training data. In this paper, we model two types of biases to reduce noise: (1 bias-dist to model the relative distance between points (instances and classes (relation centers; (2 bias-reward to model the possibility of each heuristically generated label being incorrect. Based on the biases, we propose three noise tolerant models: MIML-dist, MIML-dist-classify, and MIML-reward, building on top of a state-of-the-art distantly supervised learning algorithm. Experimental evaluations compared with three landmark methods on the KBP dataset validate the effectiveness of the proposed methods.

  11. The Effect of Learning Environments Based on Problem Solving on Students’ Achievements of Problem Solving

    Directory of Open Access Journals (Sweden)

    Ilhan KARATAS

    2013-07-01

    Full Text Available Problem solving is recognized as an important life skill involving a range of processes including analyzing, interpreting, reasoning, predicting, evaluating and reflecting. For that reason educatingstudents as efficient problem solvers is an important role of mathematics education. Problem solving skill is the centre of mathematics curriculum. Students’ gaining of that skill in school mathematics is closely related with the learning environment to beformed and the roles given to the students. The aim of this study is to create a problem solving based learning environment to enhance the students’ problem solving skill. Within this scope, students’practiced activities and problems that provide them to proceed in Polya (1945’s problem solving phases and throughout the study, students’ success in problem solving have been evaluated. While experimental group students received problem solving based learning environment performed, control group students have continued their present program in this quise1experimental study. Eleven problem solving activities were given to the students at the beginning, middle and end of the study and the students’ performances wereanalyzed based on problem solving phases. The findings illustrated that the experimental group students’ success in problem solving activities has increased while the control group students’ success has not changed significantly.

  12. Speak Out: Dancing into Problem-Based Learning

    Science.gov (United States)

    Parrish, Mila

    2007-01-01

    In recent years, Problem Based Learning (PBL) has been applied in medical and psychological areas of professional education. The PBL approach requires students to move past traditional choreographic methods toward making dances informed by real-world issues. In PBL, students work cooperatively to solve complex problems. Rather than being presented…

  13. The Effects of Service Learning on Student Problem Solving

    Science.gov (United States)

    Guo, Fangfang; Yao, Meilin; Wang, Cong; Yan, Wenfan; Zong, Xiaoli

    2016-01-01

    Previous research indicated that service learning (SL) is an effective pedagogy to improve students' problem-solving ability and increase their classroom engagement. However, studies on SL are rare in China. This study examined the effects of SL on the problem solving of Chinese undergraduate students as well as the mechanism through which it…

  14. Problem-based learning packages: considerations for neophyte package writers.

    Science.gov (United States)

    Hengstberger-Sims, C; McMillan, M A

    1993-02-01

    Adopting problem-based learning (PBL) as the major educational approach in the implementation of a nursing curriculum requires the development of numerous learning stimulus packages. When reviewing the experience of the Division of Nursing within the Faculty of Health at the University of Western Sydney, Macarthur in Australia, several considerations for package writers were identified and are discussed. These include needs assessment, integration of the curricula content strands, multi-media learning stimuli, context, role and the incorporation of ongoing client management. The need for nurturance of writers and peer review of the developed packages is also addressed, as is a review of the impact of different learning stimuli.

  15. The Problems and Solutions of Learning English Effectively in class

    Institute of Scientific and Technical Information of China (English)

    钟丽君

    2016-01-01

    English, as an international language, is becoming more and more important in our society. Its proficiency has become a major requirement for attaining the highest level of professional and economic success around the world. We, English learners mainly learn English in Classroom, so classroom is regarded as the most important place where we learn English language. This paper gives out an analysis of the problems which lead to the ineffective English learning and discusses the solutions of an effective English learning in class according to the author' s own experience in English teaching in secondary schools and colleges.

  16. Enabling Problem Based Learning through Web 2.0 Technologies

    DEFF Research Database (Denmark)

    Tambouris, Efthimios; Panopoulou, Eleni; Tarabanis, Konstantinos

    2012-01-01

    Advances in Information and Communications Technology (ICT), particularly the so-called Web 2.0, are affecting all aspects of our life: how we communicate, how we shop, how we socialise, and how we learn. Facilitating learning through the use of ICT, also known as eLearning, is a vital part...... of modern educational systems. Established pedagogical strategies, such as Problem Based Learning (PBL), are being adapted for online use in conjunction with modern Web 2.0 technologies and tools. However, even though Web 2.0 and progressive social-networking technologies are automatically associated...

  17. Quality Training and Learning in Aviation: Problems of Alignment

    Science.gov (United States)

    Moore, Phillip J.; Lehrer, Henry R.; Telfer, Ross A.

    2001-01-01

    The challenge of producing training programs that lead to quality learning outcomes is ever present in aviation, especially when economic and regulatory pressures are brought into the equation. Previous research by Telfer & Moore (1997) indicates the importance of appropriate alignment of beliefs about learning across all levels of an organization from the managerial level, through the instructor/check and training level, to the pilots and other crew. This paper argues for a central focus on approaches to learning and training that encourage understanding, problem solving and application. Recent research in the area is emphasized as are methods and techniques for enhancing deeper learning.

  18. Optimizing dictionary learning parameters for solving Audio Inpainting problem

    Directory of Open Access Journals (Sweden)

    Václav Mach

    2013-01-01

    Full Text Available Recovering missing or distorted audio signal sam-ples has been recently improved by solving an Audio Inpaintingproblem. This paper aims to connect this problem with K-SVD dictionary learning to improve reconstruction error formissing signal insertion problem. Our aim is to adapt an initialdictionary to the reliable signal to be more accurate in missingsamples estimation. This approach is based on sparse signalsreconstruction and optimization problem. In the paper two staplealgorithms, connection between them and emerging problemsare described. We tried to find optimal parameters for efficientdictionary learning.

  19. Teaching, Learning and Assessing Statistical Problem Solving

    Science.gov (United States)

    Marriott, John; Davies, Neville; Gibson, Liz

    2009-01-01

    In this paper we report the results from a major UK government-funded project, started in 2005, to review statistics and handling data within the school mathematics curriculum for students up to age 16. As a result of a survey of teachers we developed new teaching materials that explicitly use a problem-solving approach for the teaching and…

  20. Should Children Learn to Solve Problems?

    Science.gov (United States)

    Watras, Joseph

    2011-01-01

    In this comparative essay, the author discusses the opposing educational theories of John Dewey and Gregory Bateson. While Dewey believed that the scientific method was the dominant method of solving problems and thereby acquiring knowledge that mattered, Bateson warned that this one-sided approach would lead to actions that could destroy the…

  1. Applying a Problem Based Learning Approach to Land Management Education

    DEFF Research Database (Denmark)

    Enemark, Stig

    -world context. The combination of different disciplines can be taught through a “learning-by-doing approach”. Problem solving skills can be taught through a project-oriented approach to surveying education with a focus on developing skills for “learning to learn”. The basic principles of this educational model...... engineering focus toward adopting an interdisciplinary and problem-based approach to ensure that academic programmes can cope with the wide range of land administration functions and challenges. An interdisciplinary approach to surveying education calls for the need to address issues and problems in a real...... are presented using the surveying programme at Aalborg University as an example. This paper is work in progress and draws from previous research. The paper supports the lecture on Problem Based Learning given at NUST 3 March 2016....

  2. Speak Out: Dancing into Problem-Based Learning

    OpenAIRE

    2007-01-01

    In recent years, Problem Based Learning (PBL) has been applied in medical and psychological areas of professional education. The PBL approach requires students to move past traditional choreographic methods toward making dances informed by real-world issues. In PBL, students work cooperatively to solve complex problems. Rather than being presented technical dance steps, they develop critical thinking abilities, acquire problem-solving skills, and communication dexterity. PBL can be effectivel...

  3. Role-playing in the problem-based learning class.

    Science.gov (United States)

    Chan, Zenobia C Y

    2012-01-01

    Learning and teaching have been conceptualized and executed in many styles, such as self-learning, peer learning, and interaction between the learner and mentor. Today, openness to alternative ideas and embracing innovative approaches in nursing education are encouraged in order to meet students' learning interests and needs, and to address ever-changing healthcare requests. Problem-based learning has been widely adopted in nursing education, with various positive effects on students' learning, such as motivated learning, team work, problem-solving skills and critical thinking. Role-plays have been demonstrated as an effective learning strategy that includes an active and experiential feature that facilitates students' autonomy in their health-related learning. However, there is a lack of discussion of whether and how role-play can be used in problem-based learning (PBL). This paper shows the development of a classroom-based innovation using role-play in the PBL class for higher diploma year-one nurse students (a total of 20 students, five per group). This paper consists of five sections: a) the literature on PBL and nurse education, and role-plays as the innovation; b) the PBL case scenario with the illustration of the two role-play scripts, c) student evaluation on role-play in the PBL class; d) discussions on both achievements and limitations of this innovation, and e) the conclusion. It is hoped that this paper will be an example to other nurse educators who are keen on exploring interactive and student-driven learning and teaching strategies in the PBL class.

  4. Learning disabilities and social problem solving skills

    Directory of Open Access Journals (Sweden)

    Pina Filippello

    2013-09-01

    Full Text Available Normal 0 14 false false false MicrosoftInternetExplorer4 Recent studies showed that children with learning disabilities present significant difficulties in learning as well as in social skills (Siperstein, 2009.Therefore, it was observed how it is difficult for these children to establish adequate relationships, especially to advise coping strategies to face interpersonal conflicts (Oliva & LaGreca, 1988. Accordingly to this argument and with reference to Agaliotis e Kalyva (2004, 2009, this study examines the preferences for strategies to solve an hypothetical conflict on a sample of children with LD in comparison to typical developing peers. They used the method of social story to conduct this research. In fact, researchers asked to the children, after they have listened a short story describing an interpersonal conflict interaction between adult and peers,  which strategies they would have chosen if they were in the same situation and the strategies that would be most appropriate to resolve a conflict. Results obtained from the experiment corroborated literature data and demonstrated that children with LD, in comparison to typical developing peers, use and prefer dysfunctional coping strategies, aggressive or passive, also in relation to the partner interaction (adult or peers to face interpersonal conflict.

  5. Redesigning Problem-Based Learning in the Knowledge Creation Paradigm for School Science Learning

    Science.gov (United States)

    Yeo, Jennifer; Tan, Seng Chee

    2014-01-01

    The introduction of problem-based learning into K-12 science classrooms faces the challenge of achieving the dual goal of learning science content and developing problem-solving skills. To overcome this content-process tension in science classrooms, we employed the knowledge-creation approach as a boundary object between the two seemingly…

  6. Problem-Based Learning to Foster Deep Learning in Preservice Geography Teacher Education

    Science.gov (United States)

    Golightly, Aubrey; Raath, Schalk

    2015-01-01

    In South Africa, geography education students' approach to deep learning has received little attention. Therefore the purpose of this one-shot experimental case study was to evaluate the extent to which first-year geography education students used deep or surface learning in an embedded problem-based learning (PBL) format. The researchers measured…

  7. Problem-Based Learning to Foster Deep Learning in Preservice Geography Teacher Education

    Science.gov (United States)

    Golightly, Aubrey; Raath, Schalk

    2015-01-01

    In South Africa, geography education students' approach to deep learning has received little attention. Therefore the purpose of this one-shot experimental case study was to evaluate the extent to which first-year geography education students used deep or surface learning in an embedded problem-based learning (PBL) format. The researchers measured…

  8. 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.

  9. 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.

  10. 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

  11. Problem based Learning in surveying Education

    DEFF Research Database (Denmark)

    Enemark, Stig

    The challenge of the future will be that the only constant is change. Therefore, the educational base must be flexible. The graduates must possess skills to adapt to a rapidly changing labour market and they must possess skills to deal with even the unknown problems of the future. The point is...... that opportunity. The basis principles of this educational model are presented using the surveying programme at Aalborg University as an example....

  12. 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.

  13. Understanding Individual Problem-Solving Style: A Key to Learning and Applying Creative Problem Solving

    Science.gov (United States)

    Treffinger, Donald J.; Selby, Edwin C.; Isaksen, Scott G.

    2008-01-01

    More than five decades of research and development have focused on making the Creative Problem Solving process and tools accessible across a wide range of ages and contexts. Recent evidence indicates that when individuals, in both school and corporate settings, understand their own style of problem solving, they are able to learn and apply process…

  14. Personalized learning Ecologies in Problem and Project Based Learning Environments

    DEFF Research Database (Denmark)

    Rongbutsri, Nikorn; Ryberg, Thomas; Zander, Pär-Ola

    2012-01-01

    the critique is that VLEs have become content silos enforcing a traditional, teacher-centred transmission pedagogy, and that there is a need to re-instate a more learner centred agenda and pedagogy. One of the proposed solutions is a move towards student-owned and controlled Personal Learning Environments...

  15. Learning Problems in Children of Refugee Background: A Systematic Review.

    Science.gov (United States)

    Graham, Hamish R; Minhas, Ripudaman S; Paxton, Georgia

    2016-06-01

    Learning problems are common, affecting up to 1 in 10 children. Refugee children may have cumulative risk for educational disadvantage, but there is limited information on learning in this population. To review the evidence on educational outcomes and learning problems in refugee children and to describe their major risk and resource factors. Medline, Embase, PubMed, Cumulative Index to Nursing and Allied Health Literature, PsycINFO, and Education Resources Information Center. English-language articles addressing the prevalence and determinants of learning problems in refugee children. Data were extracted and analyzed according to Arksey and O'Malley's descriptive analytical method for scoping studies. Thirty-four studies were included. Refugee youth had similar secondary school outcomes to their native-born peers; there were no data on preschool or primary school outcomes. There were limited prevalence data on learning problems, with single studies informing most estimates and no studies examining specific language disorders or autism spectrum disorders. Major risk factors for learning problems included parental misunderstandings about educational styles and expectations, teacher stereotyping and low expectations, bullying and racial discrimination, premigration and postmigration trauma, and forced detention. Major resource factors for success included high academic and life ambition, "gift-and-sacrifice" motivational narratives, parental involvement in education, family cohesion and supportive home environment, accurate educational assessment and grade placement, teacher understanding of linguistic and cultural heritage, culturally appropriate school transition, supportive peer relationships, and successful acculturation. Studies are not generalizable to other cohorts. This review provides a summary of published prevalence estimates for learning problems in resettled refugee children, highlights key risk and resource factors, and identifies gaps in research

  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. Ubiquitous Learning Environment as a Solution to Education Problem

    Institute of Scientific and Technical Information of China (English)

    ZHAO Hailan; Jung-Hwan Park

    2006-01-01

    Ubiquitous learning means the intelligent learning environment that enables people to learn using any terminal at any place and at any time no matter what the contents may be. This is different from the existing method of learning the information through the internet or studying at specific place, such as school or library, and so on. People do not need to go to specific place at specific time to access the information that they want, but the learning information finds its way to people, which is the biggest difference from the existing method.Ubiquitous learning aims to solve current problems of education in more creative and learner-oriented way by creating an education environment where learners can learn with any terminal anytime and anywhere without regard to contents. The presentation this time would help promote the understanding of the Ubiquitous and explore the ubiquitous education environment that is capable of solving overall problems of education. In addition, it will propose a direction of ubiquitous learning scenario and the change in the educational environment.

  18. The Effect of Learning Environments Based on Problem Solving on Students’ Achievements of Problem Solving

    OpenAIRE

    Ilhan KARATAS; Baki, Adnan

    2013-01-01

    Problem solving is recognized as an important life skill involving a range of processes including analyzing, interpreting, reasoning, predicting, evaluating and reflecting. For that reason educatingstudents as efficient problem solvers is an important role of mathematics education. Problem solving skill is the centre of mathematics curriculum. Students’ gaining of that skill in school mathematics is closely related with the learning environment to beformed and the roles given to the students....

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

    Science.gov (United States)

    Pender, Rebecca Lynn

    2012-01-01

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

  20. The Effect of Problem Based Learning (PBL) Instruction on Students' Motivation and Problem Solving Skills of Physics

    Science.gov (United States)

    Argaw, Aweke Shishigu; Haile, Beyene Bashu; Ayalew, Beyene Tesfaw; Kuma, Shiferaw Gadisa

    2017-01-01

    Through the learning of physics, students will acquire problem solving skills which are relevant to their daily life. Determining the best way in which students learn physics takes a priority in physics education. The goal of the present study was to determine the effect of problem based learning strategy on students' problem solving skills and…