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Sample records for supervised support vector

  1. CompareSVM: supervised, Support Vector Machine (SVM) inference of gene regularity networks.

    Science.gov (United States)

    Gillani, Zeeshan; Akash, Muhammad Sajid Hamid; Rahaman, M D Matiur; Chen, Ming

    2014-11-30

    Predication of gene regularity network (GRN) from expression data is a challenging task. There are many methods that have been developed to address this challenge ranging from supervised to unsupervised methods. Most promising methods are based on support vector machine (SVM). There is a need for comprehensive analysis on prediction accuracy of supervised method SVM using different kernels on different biological experimental conditions and network size. We developed a tool (CompareSVM) based on SVM to compare different kernel methods for inference of GRN. Using CompareSVM, we investigated and evaluated different SVM kernel methods on simulated datasets of microarray of different sizes in detail. The results obtained from CompareSVM showed that accuracy of inference method depends upon the nature of experimental condition and size of the network. For network with nodes (SVM Gaussian kernel outperform on knockout, knockdown, and multifactorial datasets compared to all the other inference methods. For network with large number of nodes (~500), choice of inference method depend upon nature of experimental condition. CompareSVM is available at http://bis.zju.edu.cn/CompareSVM/ .

  2. Clustering Categories in Support Vector Machines

    DEFF Research Database (Denmark)

    Carrizosa, Emilio; Nogales-Gómez, Amaya; Morales, Dolores Romero

    2017-01-01

    The support vector machine (SVM) is a state-of-the-art method in supervised classification. In this paper the Cluster Support Vector Machine (CLSVM) methodology is proposed with the aim to increase the sparsity of the SVM classifier in the presence of categorical features, leading to a gain in in...

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

    Directory of Open Access Journals (Sweden)

    Fei Gao

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

  4. Variance inflation in high dimensional Support Vector Machines

    DEFF Research Database (Denmark)

    Abrahamsen, Trine Julie; Hansen, Lars Kai

    2013-01-01

    Many important machine learning models, supervised and unsupervised, are based on simple Euclidean distance or orthogonal projection in a high dimensional feature space. When estimating such models from small training sets we face the problem that the span of the training data set input vectors...... the case of Support Vector Machines (SVMS) and we propose a non-parametric scheme to restore proper generalizability. We illustrate the algorithm and its ability to restore performance on a wide range of benchmark data sets....... follow a different probability law with less variance. While the problem and basic means to reconstruct and deflate are well understood in unsupervised learning, the case of supervised learning is less well understood. We here investigate the effect of variance inflation in supervised learning including...

  5. Evaluating automatically parallelized versions of the support vector machine

    NARCIS (Netherlands)

    Codreanu, Valeriu; Droge, Bob; Williams, David; Yasar, Burhan; Yang, Fo; Liu, Baoquan; Dong, Feng; Surinta, Olarik; Schomaker, Lambertus; Roerdink, Jos; Wiering, Marco

    2014-01-01

    The support vector machine (SVM) is a supervised learning algorithm used for recognizing patterns in data. It is a very popular technique in machine learning and has been successfully used in applications such as image classification, protein classification, and handwriting recognition. However, the

  6. Evaluating automatically parallelized versions of the support vector machine

    NARCIS (Netherlands)

    Codreanu, V.; Dröge, B.; Williams, D.; Yasar, B.; Yang, P.; Liu, B.; Dong, F.; Surinta, O.; Schomaker, L.R.B.; Roerdink, J.B.T.M.; Wiering, M.A.

    2016-01-01

    The support vector machine (SVM) is a supervised learning algorithm used for recognizing patterns in data. It is a very popular technique in machine learning and has been successfully used in applications such as image classification, protein classification, and handwriting recognition. However, the

  7. Support vector machine: a tool for mapping mineral prospectivity

    NARCIS (Netherlands)

    Zuo, R.; Carranza, E.J.M

    2011-01-01

    In this contribution, we describe an application of support vector machine (SVM), a supervised learning algorithm, to mineral prospectivity mapping. The free R package e1071 is used to construct a SVM with sigmoid kernel function to map prospectivity for Au deposits in western Meguma Terrain of Nova

  8. Investigation of support vector machine for the detection of architectural distortion in mammographic images

    International Nuclear Information System (INIS)

    Guo, Q; Shao, J; Ruiz, V

    2005-01-01

    This paper investigates detection of architectural distortion in mammographic images using support vector machine. Hausdorff dimension is used to characterise the texture feature of mammographic images. Support vector machine, a learning machine based on statistical learning theory, is trained through supervised learning to detect architectural distortion. Compared to the Radial Basis Function neural networks, SVM produced more accurate classification results in distinguishing architectural distortion abnormality from normal breast parenchyma

  9. Investigation of support vector machine for the detection of architectural distortion in mammographic images

    Energy Technology Data Exchange (ETDEWEB)

    Guo, Q [Department of Cybernetics, University of Reading, Reading RG6 6AY (United Kingdom); Shao, J [Department of Electronics, University of Kent at Canterbury, Kent CT2 7NT (United Kingdom); Ruiz, V [Department of Cybernetics, University of Reading, Reading RG6 6AY (United Kingdom)

    2005-01-01

    This paper investigates detection of architectural distortion in mammographic images using support vector machine. Hausdorff dimension is used to characterise the texture feature of mammographic images. Support vector machine, a learning machine based on statistical learning theory, is trained through supervised learning to detect architectural distortion. Compared to the Radial Basis Function neural networks, SVM produced more accurate classification results in distinguishing architectural distortion abnormality from normal breast parenchyma.

  10. A Support Vector Machine Approach to Dutch Part-of-Speech Tagging

    NARCIS (Netherlands)

    Poel, Mannes; Stegeman, L.; op den Akker, Hendrikus J.A.; Berthold, M.R.; Shawe-Taylor, J.; Lavrac, N.

    Part-of-Speech tagging, the assignment of Parts-of-Speech to the words in a given context of use, is a basic technique in many systems that handle natural languages. This paper describes a method for supervised training of a Part-of-Speech tagger using a committee of Support Vector Machines on a

  11. Defeating abusive supervision: Training supervisors to support subordinates.

    Science.gov (United States)

    Gonzalez-Morales, M Gloria; Kernan, Mary C; Becker, Thomas E; Eisenberger, Robert

    2018-04-01

    Although much is known about the antecedents and consequences of abusive supervision, scant attention has been paid to investigating procedures to reduce its frequency. We conducted a quasiexperiment to examine the effects of supervisor support training on subordinate perceptions of abusive supervision and supervisor support. Supervisors (n = 23) in 4 restaurants were trained in 4 supportive supervision strategies (benevolence, sincerity, fairness, and experiential processing) during 4 2-hr sessions over a period of 2 months. We compared perceived supervisor support and abusive supervision before and 9 months after training for 208 employees whose supervisors received support training and 241 employees in 4 similar control restaurants. Compared to employees in the control restaurants, employees whose supervisors received the support training reported higher levels of perceived supervisor support and less abusive supervision. These findings suggest that a relatively brief training program can help managers become more supportive and less abusive. Theoretical and practical implications for effectively managing abusive supervision are discussed. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  12. Support Vector Machine Based Tool for Plant Species Taxonomic Classification

    OpenAIRE

    Manimekalai .K; Vijaya.MS

    2014-01-01

    Plant species are living things and are generally categorized in terms of Domain, Kingdom, Phylum, Class, Order, Family, Genus and name of Species in a hierarchical fashion. This paper formulates the taxonomic leaf categorization problem as the hierarchical classification task and provides a suitable solution using a supervised learning technique namely support vector machine. Features are extracted from scanned images of plant leaves and trained using SVM. Only class, order, family of plants...

  13. Ultrasonic fluid quantity measurement in dynamic vehicular applications a support vector machine approach

    CERN Document Server

    Terzic, Jenny; Nagarajah, Romesh; Alamgir, Muhammad

    2013-01-01

    Accurate fluid level measurement in dynamic environments can be assessed using a Support Vector Machine (SVM) approach. SVM is a supervised learning model that analyzes and recognizes patterns. It is a signal classification technique which has far greater accuracy than conventional signal averaging methods. Ultrasonic Fluid Quantity Measurement in Dynamic Vehicular Applications: A Support Vector Machine Approach describes the research and development of a fluid level measurement system for dynamic environments. The measurement system is based on a single ultrasonic sensor. A Support Vector Machines (SVM) based signal characterization and processing system has been developed to compensate for the effects of slosh and temperature variation in fluid level measurement systems used in dynamic environments including automotive applications. It has been demonstrated that a simple ν-SVM model with Radial Basis Function (RBF) Kernel with the inclusion of a Moving Median filter could be used to achieve the high levels...

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2009-04-29

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

  15. 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.

  16. Supervising undergraduate research: a collective approach utilising groupwork and peer support.

    Science.gov (United States)

    Baker, Mary-Jane; Cluett, Elizabeth; Ireland, Lorraine; Reading, Sheila; Rourke, Susan

    2014-04-01

    Nursing education now requires graduate entry for professional registration. The challenge is to ensure that students develop independence and team working in a resource effective manner. The dissertation is one opportunity for this. To evaluate changing from individual dissertation supervision to group peer supervision. Group supervision was implemented for one cohort. Dissertation outcomes were compared with two previous cohorts. Student evaluative data was assessed. Group supervision did not adversely affect dissertation outcomes (p=0.85). 88% of students reported peer supervision to be helpful, with themes being 'support and sharing', and 'progress and moving forward'. Peer group support provided consistent supervision harnessing the energy and resources of the students and Faculty, without adversely affecting outcomes. Copyright © 2013 Elsevier Ltd. All rights reserved.

  17. When Family-Supportive Supervision Matters: Relations between Multiple Sources of Support and Work-Family Balance

    Science.gov (United States)

    Greenhaus, Jeffrey H.; Ziegert, Jonathan C.; Allen, Tammy D.

    2012-01-01

    This study examines the mechanisms by which family-supportive supervision is related to employee work-family balance. Based on a sample of 170 business professionals, we found that the positive relation between family-supportive supervision and balance was fully mediated by work interference with family (WIF) and partially mediated by family…

  18. Support Vector Machines for Hyperspectral Remote Sensing Classification

    Science.gov (United States)

    Gualtieri, J. Anthony; Cromp, R. F.

    1998-01-01

    The Support Vector Machine provides a new way to design classification algorithms which learn from examples (supervised learning) and generalize when applied to new data. We demonstrate its success on a difficult classification problem from hyperspectral remote sensing, where we obtain performances of 96%, and 87% correct for a 4 class problem, and a 16 class problem respectively. These results are somewhat better than other recent results on the same data. A key feature of this classifier is its ability to use high-dimensional data without the usual recourse to a feature selection step to reduce the dimensionality of the data. For this application, this is important, as hyperspectral data consists of several hundred contiguous spectral channels for each exemplar. We provide an introduction to this new approach, and demonstrate its application to classification of an agriculture scene.

  19. Supporting Early Childhood Practitioners through Relationship-Based, Reflective Supervision

    Science.gov (United States)

    Bernstein, Victor J.; Edwards, Renee C.

    2012-01-01

    Reflective supervision is a relationship-based practice that supports the professional development of early childhood practitioners. Reflective supervision helps practitioners cope with the intense feelings and stress that are generated when working with at-risk children and families. It allows them to focus on the purpose and goals of the program…

  20. Support vector machines applications

    CERN Document Server

    Guo, Guodong

    2014-01-01

    Support vector machines (SVM) have both a solid mathematical background and good performance in practical applications. This book focuses on the recent advances and applications of the SVM in different areas, such as image processing, medical practice, computer vision, pattern recognition, machine learning, applied statistics, business intelligence, and artificial intelligence. The aim of this book is to create a comprehensive source on support vector machine applications, especially some recent advances.

  1. 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…

  2. Robust Pseudo-Hierarchical Support Vector Clustering

    DEFF Research Database (Denmark)

    Hansen, Michael Sass; Sjöstrand, Karl; Olafsdóttir, Hildur

    2007-01-01

    Support vector clustering (SVC) has proven an efficient algorithm for clustering of noisy and high-dimensional data sets, with applications within many fields of research. An inherent problem, however, has been setting the parameters of the SVC algorithm. Using the recent emergence of a method...... for calculating the entire regularization path of the support vector domain description, we propose a fast method for robust pseudo-hierarchical support vector clustering (HSVC). The method is demonstrated to work well on generated data, as well as for detecting ischemic segments from multidimensional myocardial...

  3. Vision-Based Perception and Classification of Mosquitoes Using Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Masataka Fuchida

    2017-01-01

    Full Text Available The need for a novel automated mosquito perception and classification method is becoming increasingly essential in recent years, with steeply increasing number of mosquito-borne diseases and associated casualties. There exist remote sensing and GIS-based methods for mapping potential mosquito inhabitants and locations that are prone to mosquito-borne diseases, but these methods generally do not account for species-wise identification of mosquitoes in closed-perimeter regions. Traditional methods for mosquito classification involve highly manual processes requiring tedious sample collection and supervised laboratory analysis. In this research work, we present the design and experimental validation of an automated vision-based mosquito classification module that can deploy in closed-perimeter mosquito inhabitants. The module is capable of identifying mosquitoes from other bugs such as bees and flies by extracting the morphological features, followed by support vector machine-based classification. In addition, this paper presents the results of three variants of support vector machine classifier in the context of mosquito classification problem. This vision-based approach to the mosquito classification problem presents an efficient alternative to the conventional methods for mosquito surveillance, mapping and sample image collection. Experimental results involving classification between mosquitoes and a predefined set of other bugs using multiple classification strategies demonstrate the efficacy and validity of the proposed approach with a maximum recall of 98%.

  4. Learning with Support Vector Machines

    CERN Document Server

    Campbell, Colin

    2010-01-01

    Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such a

  5. Supervised Learning for Visual Pattern Classification

    Science.gov (United States)

    Zheng, Nanning; Xue, Jianru

    This chapter presents an overview of the topics and major ideas of supervised learning for visual pattern classification. Two prevalent algorithms, i.e., the support vector machine (SVM) and the boosting algorithm, are briefly introduced. SVMs and boosting algorithms are two hot topics of recent research in supervised learning. SVMs improve the generalization of the learning machine by implementing the rule of structural risk minimization (SRM). It exhibits good generalization even when little training data are available for machine training. The boosting algorithm can boost a weak classifier to a strong classifier by means of the so-called classifier combination. This algorithm provides a general way for producing a classifier with high generalization capability from a great number of weak classifiers.

  6. Assessment of various supervised learning algorithms using different performance metrics

    Science.gov (United States)

    Susheel Kumar, S. M.; Laxkar, Deepak; Adhikari, Sourav; Vijayarajan, V.

    2017-11-01

    Our work brings out comparison based on the performance of supervised machine learning algorithms on a binary classification task. The supervised machine learning algorithms which are taken into consideration in the following work are namely Support Vector Machine(SVM), Decision Tree(DT), K Nearest Neighbour (KNN), Naïve Bayes(NB) and Random Forest(RF). This paper mostly focuses on comparing the performance of above mentioned algorithms on one binary classification task by analysing the Metrics such as Accuracy, F-Measure, G-Measure, Precision, Misclassification Rate, False Positive Rate, True Positive Rate, Specificity, Prevalence.

  7. PhD Student Emotional Exhaustion: The Role of Supportive Supervision and Self-Presentation Behaviours

    Science.gov (United States)

    Devine, Kay; Hunter, Karen H.

    2017-01-01

    This research examines doctoral student perceptions of emotional exhaustion relative to supportive supervision and the use of impression management (IM) and facades of conformity (FOC). Results indicated that supportive supervision significantly reduced emotional exhaustion and the use of self-presentation behaviours, while the use of FOC…

  8. Applying Multi-Class Support Vector Machines for performance assessment of shipping operations: The case of tanker vessels

    DEFF Research Database (Denmark)

    Pagoropoulos, Aris; Møller, Anders H.; McAloone, Tim C.

    2017-01-01

    of feature selection algorithms. Afterwards, a model based on Multi- Class Support Vector Machines (SVM) was constructed and the efficacy of the approach is shown through the application of a test set. The results demonstrate the importance and benefits of machine learning algorithms in driving energy....... Identifying the potential of behavioural savings can be challenging, due to the inherent difficulty in analysing the data and operationalizing energy efficiency within the dynamic operating environment of the vessels. This article proposes a supervised learning model for identifying the presence of energy...

  9. Deep Support Vector Machines for Regression Problems

    NARCIS (Netherlands)

    Wiering, Marco; Schutten, Marten; Millea, Adrian; Meijster, Arnold; Schomaker, Lambertus

    2013-01-01

    In this paper we describe a novel extension of the support vector machine, called the deep support vector machine (DSVM). The original SVM has a single layer with kernel functions and is therefore a shallow model. The DSVM can use an arbitrary number of layers, in which lower-level layers contain

  10. A least square support vector machine-based approach for contingency classification and ranking in a large power system

    Directory of Open Access Journals (Sweden)

    Bhanu Pratap Soni

    2016-12-01

    Full Text Available This paper proposes an effective supervised learning approach for static security assessment of a large power system. Supervised learning approach employs least square support vector machine (LS-SVM to rank the contingencies and predict the system severity level. The severity of the contingency is measured by two scalar performance indices (PIs: line MVA performance index (PIMVA and Voltage-reactive power performance index (PIVQ. SVM works in two steps. Step I is the estimation of both standard indices (PIMVA and PIVQ that is carried out under different operating scenarios and Step II contingency ranking is carried out based on the values of PIs. The effectiveness of the proposed methodology is demonstrated on IEEE 39-bus (New England system. The approach can be beneficial tool which is less time consuming and accurate security assessment and contingency analysis at energy management center.

  11. The Juggling Act of Supervision in Community Mental Health: Implications for Supporting Evidence-Based Treatment.

    Science.gov (United States)

    Dorsey, Shannon; Pullmann, Michael D; Kerns, Suzanne E U; Jungbluth, Nathaniel; Meza, Rosemary; Thompson, Kelly; Berliner, Lucy

    2017-11-01

    Supervisors are an underutilized resource for supporting evidence-based treatments (EBTs) in community mental health. Little is known about how EBT-trained supervisors use supervision time. Primary aims were to describe supervision (e.g., modality, frequency), examine functions of individual supervision, and examine factors associated with time allocation to supervision functions. Results from 56 supervisors and 207 clinicians from 25 organizations indicate high prevalence of individual supervision, often alongside group and informal supervision. Individual supervision serves a wide range of functions, with substantial variation at the supervisor-level. Implementation climate was the strongest predictor of time allocation to clinical and EBT-relevant functions.

  12. Doctoral learning: a case for a cohort model of supervision and support

    Directory of Open Access Journals (Sweden)

    Naydene de Lange

    2011-01-01

    Full Text Available We document the efforts of the faculty of education of a large research-oriented university in supporting doctoral learning. The development of a space for doctoral learning is in line with the need to develop a community of researchers in South Africa. We describe the historical origins of this cohort model of doctoral supervision and support, draw on literature around doctoral learning, and analyse a cohort of doctoral students' evaluation of the seminarsoverthree years. The findings indicate that the model has great value in developing scholarship and reflective practice in candidates, in providing support and supervision, and in sustaining students towards the completion of their doctorates.

  13. Supportive supervision for volunteers to deliver reproductive health education: a cluster randomized trial.

    Science.gov (United States)

    Singh, Debra; Negin, Joel; Orach, Christopher Garimoi; Cumming, Robert

    2016-10-03

    Community Health Volunteers (CHVs) can be effective in improving pregnancy and newborn outcomes through community education. Inadequate supervision of CHVs, whether due to poor planning, irregular visits, or ineffective supervisory methods, is, however, recognized as a weakness in many programs. There has been little research on best practice supervisory or accompaniment models. From March 2014 to February 2015 a proof of concept study was conducted to compare training alone versus training and supportive supervision by paid CHWs (n = 4) on the effectiveness of CHVs (n = 82) to deliver education about pregnancy, newborn care, family planning and hygiene. The pair-matched cluster randomized trial was conducted in eight villages (four intervention and four control) in Budondo sub-county in Jinja, Uganda. Increases in desired behaviors were seen in both the intervention and control arms over the study period. Both arms showed high retention rates of CHVs (95 %). At 1 year follow-up there was a significantly higher prevalence of installed and functioning tippy taps for hand washing (p services. Supportive supervision involves creating a non-threatening, empowering environment in which both the CHV and the supervising CHW learn together and overcome obstacles that might otherwise demotivate the CHV. While the results seem promising for added value with supportive supervision for CHVs undertaking reproductive health activities, further research on a larger scale will be needed to substantiate the effect.

  14. How to create more supportive supervision for primary healthcare: lessons from Ngamiland district of Botswana: co-operative inquiry group

    Science.gov (United States)

    Nkomazana, Oathokwa; Mash, Robert; Wojczewski, Silvia; Kutalek, Ruth; Phaladze, Nthabiseng

    2016-01-01

    Background Supportive supervision is a way to foster performance, productivity, motivation, and retention of health workforce. Nevertheless there is a dearth of evidence of the impact and acceptability of supportive supervision in low- and middle-income countries. This article describes a participatory process of transforming the supervisory practice of district health managers to create a supportive environment for primary healthcare workers. Objective The objective of the study was to explore how district health managers can change their practice to create a more supportive environment for primary healthcare providers. Design A facilitated co-operative inquiry group (CIG) was formed with Ngamiland health district managers. CIG belongs to the participatory action research paradigm and is characterised by a cyclic process of observation, reflection, planning, and action. The CIG went through three cycles between March 2013 and March 2014. Results Twelve district health managers participated in the inquiry group. The major insights and learning that emerged from the inquiry process included inadequate supervisory practice, perceptions of healthcare workers’ experiences, change in the managers’ supervision paradigm, recognition of the supervisors’ inadequate supervisory skills, and barriers to supportive supervision. Finally, the group developed a 10-point consensus on what they had learnt regarding supportive supervision. Conclusion Ngamiland health district managers have come to appreciate the value of supportive supervision and changed their management style to be more supportive of their subordinates. They also developed a consensus on supportive supervision that could be adapted for use nationally. Supportive supervision should be prioritised at all levels of the health system, and it should be adequately resourced. PMID:27345024

  15. Pharmaceutical Raw Material Identification Using Miniature Near-Infrared (MicroNIR) Spectroscopy and Supervised Pattern Recognition Using Support Vector Machine

    OpenAIRE

    Sun, Lan; Hsiung, Chang; Pederson, Christopher G.; Zou, Peng; Smith, Valton; von Gunten, Marc; O?Brien, Nada A.

    2016-01-01

    Near-infrared spectroscopy as a rapid and non-destructive analytical technique offers great advantages for pharmaceutical raw material identification (RMID) to fulfill the quality and safety requirements in pharmaceutical industry. In this study, we demonstrated the use of portable miniature near-infrared (MicroNIR) spectrometers for NIR-based pharmaceutical RMID and solved two challenges in this area, model transferability and large-scale classification, with the aid of support vector machin...

  16. Comparison of four support-vector based function approximators

    NARCIS (Netherlands)

    de Kruif, B.J.; de Vries, Theodorus J.A.

    2004-01-01

    One of the uses of the support vector machine (SVM), as introduced in V.N. Vapnik (2000), is as a function approximator. The SVM and approximators based on it, approximate a relation in data by applying interpolation between so-called support vectors, being a limited number of samples that have been

  17. Multi-task Vector Field Learning.

    Science.gov (United States)

    Lin, Binbin; Yang, Sen; Zhang, Chiyuan; Ye, Jieping; He, Xiaofei

    2012-01-01

    Multi-task learning (MTL) aims to improve generalization performance by learning multiple related tasks simultaneously and identifying the shared information among tasks. Most of existing MTL methods focus on learning linear models under the supervised setting. We propose a novel semi-supervised and nonlinear approach for MTL using vector fields. A vector field is a smooth mapping from the manifold to the tangent spaces which can be viewed as a directional derivative of functions on the manifold. We argue that vector fields provide a natural way to exploit the geometric structure of data as well as the shared differential structure of tasks, both of which are crucial for semi-supervised multi-task learning. In this paper, we develop multi-task vector field learning (MTVFL) which learns the predictor functions and the vector fields simultaneously. MTVFL has the following key properties. (1) The vector fields MTVFL learns are close to the gradient fields of the predictor functions. (2) Within each task, the vector field is required to be as parallel as possible which is expected to span a low dimensional subspace. (3) The vector fields from all tasks share a low dimensional subspace. We formalize our idea in a regularization framework and also provide a convex relaxation method to solve the original non-convex problem. The experimental results on synthetic and real data demonstrate the effectiveness of our proposed approach.

  18. Support vector machines optimization based theory, algorithms, and extensions

    CERN Document Server

    Deng, Naiyang; Zhang, Chunhua

    2013-01-01

    Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions presents an accessible treatment of the two main components of support vector machines (SVMs)-classification problems and regression problems. The book emphasizes the close connection between optimization theory and SVMs since optimization is one of the pillars on which SVMs are built.The authors share insight on many of their research achievements. They give a precise interpretation of statistical leaning theory for C-support vector classification. They also discuss regularized twi

  19. Pharmaceutical Raw Material Identification Using Miniature Near-Infrared (MicroNIR) Spectroscopy and Supervised Pattern Recognition Using Support Vector Machine.

    Science.gov (United States)

    Sun, Lan; Hsiung, Chang; Pederson, Christopher G; Zou, Peng; Smith, Valton; von Gunten, Marc; O'Brien, Nada A

    2016-05-01

    Near-infrared spectroscopy as a rapid and non-destructive analytical technique offers great advantages for pharmaceutical raw material identification (RMID) to fulfill the quality and safety requirements in pharmaceutical industry. In this study, we demonstrated the use of portable miniature near-infrared (MicroNIR) spectrometers for NIR-based pharmaceutical RMID and solved two challenges in this area, model transferability and large-scale classification, with the aid of support vector machine (SVM) modeling. We used a set of 19 pharmaceutical compounds including various active pharmaceutical ingredients (APIs) and excipients and six MicroNIR spectrometers to test model transferability. For the test of large-scale classification, we used another set of 253 pharmaceutical compounds comprised of both chemically and physically different APIs and excipients. We compared SVM with conventional chemometric modeling techniques, including soft independent modeling of class analogy, partial least squares discriminant analysis, linear discriminant analysis, and quadratic discriminant analysis. Support vector machine modeling using a linear kernel, especially when combined with a hierarchical scheme, exhibited excellent performance in both model transferability and large-scale classification. Hence, ultra-compact, portable and robust MicroNIR spectrometers coupled with SVM modeling can make on-site and in situ pharmaceutical RMID for large-volume applications highly achievable. © The Author(s) 2016.

  20. Support Vector Hazards Machine: A Counting Process Framework for Learning Risk Scores for Censored Outcomes.

    Science.gov (United States)

    Wang, Yuanjia; Chen, Tianle; Zeng, Donglin

    2016-01-01

    Learning risk scores to predict dichotomous or continuous outcomes using machine learning approaches has been studied extensively. However, how to learn risk scores for time-to-event outcomes subject to right censoring has received little attention until recently. Existing approaches rely on inverse probability weighting or rank-based regression, which may be inefficient. In this paper, we develop a new support vector hazards machine (SVHM) approach to predict censored outcomes. Our method is based on predicting the counting process associated with the time-to-event outcomes among subjects at risk via a series of support vector machines. Introducing counting processes to represent time-to-event data leads to a connection between support vector machines in supervised learning and hazards regression in standard survival analysis. To account for different at risk populations at observed event times, a time-varying offset is used in estimating risk scores. The resulting optimization is a convex quadratic programming problem that can easily incorporate non-linearity using kernel trick. We demonstrate an interesting link from the profiled empirical risk function of SVHM to the Cox partial likelihood. We then formally show that SVHM is optimal in discriminating covariate-specific hazard function from population average hazard function, and establish the consistency and learning rate of the predicted risk using the estimated risk scores. Simulation studies show improved prediction accuracy of the event times using SVHM compared to existing machine learning methods and standard conventional approaches. Finally, we analyze two real world biomedical study data where we use clinical markers and neuroimaging biomarkers to predict age-at-onset of a disease, and demonstrate superiority of SVHM in distinguishing high risk versus low risk subjects.

  1. The Use of E-supervision to Support Speech-Language Pathology Graduate Students during Student Teaching Practica

    Directory of Open Access Journals (Sweden)

    Charles H. Carlin

    2013-12-01

    Full Text Available In the present feasibility study, e-supervision was used to provide university liaison supervision to SLP graduate students enrolled in student teaching practica. Utilizing a mixed methodology approach, interview and survey data were compared in order to identify similarities and differences between face-to-face and e-supervision and guide future practice. Results showed e-supervised graduate students received adequate supervision, feedback, support, and communication. Further, e-supervision provided additional benefits to supervisors, children on the caseload, and universities. Despite the benefits, disadvantages emerged. Implications for future practice and limitations of the study were identified.

  2. Progressive Classification Using Support Vector Machines

    Science.gov (United States)

    Wagstaff, Kiri; Kocurek, Michael

    2009-01-01

    An algorithm for progressive classification of data, analogous to progressive rendering of images, makes it possible to compromise between speed and accuracy. This algorithm uses support vector machines (SVMs) to classify data. An SVM is a machine learning algorithm that builds a mathematical model of the desired classification concept by identifying the critical data points, called support vectors. Coarse approximations to the concept require only a few support vectors, while precise, highly accurate models require far more support vectors. Once the model has been constructed, the SVM can be applied to new observations. The cost of classifying a new observation is proportional to the number of support vectors in the model. When computational resources are limited, an SVM of the appropriate complexity can be produced. However, if the constraints are not known when the model is constructed, or if they can change over time, a method for adaptively responding to the current resource constraints is required. This capability is particularly relevant for spacecraft (or any other real-time systems) that perform onboard data analysis. The new algorithm enables the fast, interactive application of an SVM classifier to a new set of data. The classification process achieved by this algorithm is characterized as progressive because a coarse approximation to the true classification is generated rapidly and thereafter iteratively refined. The algorithm uses two SVMs: (1) a fast, approximate one and (2) slow, highly accurate one. New data are initially classified by the fast SVM, producing a baseline approximate classification. For each classified data point, the algorithm calculates a confidence index that indicates the likelihood that it was classified correctly in the first pass. Next, the data points are sorted by their confidence indices and progressively reclassified by the slower, more accurate SVM, starting with the items most likely to be incorrectly classified. The user

  3. SUPPORTIVE SUPERVISION AS A TECHNOLOGY OF IMPROVING THE QUALITY OF HOSPITAL CARE DELIVERY

    Directory of Open Access Journals (Sweden)

    Svetlana A. Mukhortova

    2017-01-01

    Full Text Available Improving the quality of medical care is a priority in countries with developed and developing health care system. There are various approaches to improve the quality and safety of patient’s care, as well as various strategies to encourage hospitals to achieve this goal. The purpose of the presented literature review was to analyze existing experience of the implementation of technology of supportive supervision in health care facilities to improve the quality of hospital care delivery. The data sources for publication were obtained from the following medical databases: PubMed, Cochrane Library, Medscape, e-library, and books on the topic of the review written by experts. The article discusses the results of the research studies demonstrating the successes and failures of supportive supervision technology application. Implementation of supportive supervision in medical facilities based on generalized experience of different countries is a promising direction in improving the quality of medical care delivery. This technology opens up opportunities to improve skills and work quality of the staff at pediatric hospitals in the Russian Federation.

  4. Construction and decomposition of biorthogonal vector-valued wavelets with compact support

    International Nuclear Information System (INIS)

    Chen Qingjiang; Cao Huaixin; Shi Zhi

    2009-01-01

    In this article, we introduce vector-valued multiresolution analysis and the biorthogonal vector-valued wavelets with four-scale. The existence of a class of biorthogonal vector-valued wavelets with compact support associated with a pair of biorthogonal vector-valued scaling functions with compact support is discussed. A method for designing a class of biorthogonal compactly supported vector-valued wavelets with four-scale is proposed by virtue of multiresolution analysis and matrix theory. The biorthogonality properties concerning vector-valued wavelet packets are characterized with the aid of time-frequency analysis method and operator theory. Three biorthogonality formulas regarding them are presented.

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

    Science.gov (United States)

    Patel, Nihir; Wang, Jason T L

    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.

  6. Clinical supervision and nursing students' professional competence: support-seeking behaviour and the attachment styles of students and mentors.

    Science.gov (United States)

    Moked, Zahava; Drach-Zahavy, Anat

    2016-02-01

    To examine whether the interdependent attachment style of students is positively related to their support-seeking behaviour during supervision and whether their over-dependent and counter-dependent attachment styles are negatively related to it. Second, to determine whether the mentors' attachment styles moderate the relationship between the students' support-seeking behaviours and their professional competence, such that this relationship is stronger when supervisors are characterized by higher independent attachment style. The mentor-student encounter during nursing clinical supervision is expected to create a supportive environment aimed at promoting support-seeking behaviours and subsequent positive supervision outcomes. Bowlby's attachment theory suggests that the three attachment styles - independent, counter-dependent and over-dependent - may have implications for clinical supervision. A correlative-prospective study. One hundred and seventy-eight students and 66 clinical mentors completed questionnaires at the beginning and end of a clinical supervision session during 2012-2013. Results demonstrated that high compared with low independent nursing students tended to seek less support. Second, students who seek less support evaluated their professional competence as higher than students who seek more support. Third, mentor's counter-dependent attachment style moderated the relationship between students' support-seeking behaviour and their professional competencies. The results allude to the detrimental meaning of support-seeking in the eyes of nursing students. Results can guide administrators in promoting supervision processes that are compatible with the students' independent learning style, while also preventing the negative implications of autonomic learning. Furthermore, as mentors' counter-dependent attachment style can hinder students' support-seeking, attachment styles should be considered in the selection of mentors. © 2015 John Wiley & Sons Ltd.

  7. A systematic review of supportive supervision as a strategy to improve primary healthcare services in Sub-Saharan Africa.

    Science.gov (United States)

    Bailey, Claire; Blake, Carolyn; Schriver, Michael; Cubaka, Vincent Kalumire; Thomas, Tisa; Martin Hilber, Adriane

    2016-01-01

    It may be assumed that supportive supervision effectively builds capacity, improves the quality of care provided by frontline health workers, and positively impacts clinical outcomes. Evidence on the role of supervision in Sub-Saharan Africa has been inconclusive, despite the critical need to maximize the workforce in low-resource settings. To review the published literature from Sub-Saharan Africa on the effects of supportive supervision on quality of care, and health worker motivation and performance. A systematic review of seven databases of both qualitative and quantitative studies published in peer-reviewed journals. Selected studies were based in primary healthcare settings in Sub-Saharan Africa and present primary data concerning supportive supervision. Thematic synthesis where data from the identified studies were grouped and interpreted according to prominent themes. Supportive supervision can increase job satisfaction and health worker motivation. Evidence is mixed on whether this translates to increased clinical competence and there is little evidence of the effect on clinical outcomes. Results highlight the lack of sound evidence on the effects of supportive supervision owing to limitations in research design and the complexity of evaluating such interventions. The approaches required a high level of external inputs, which challenge the sustainability of such models. Copyright © 2015 International Federation of Gynecology and Obstetrics. Published by Elsevier Ireland Ltd. All rights reserved.

  8. Image superresolution using support vector regression.

    Science.gov (United States)

    Ni, Karl S; Nguyen, Truong Q

    2007-06-01

    A thorough investigation of the application of support vector regression (SVR) to the superresolution problem is conducted through various frameworks. Prior to the study, the SVR problem is enhanced by finding the optimal kernel. This is done by formulating the kernel learning problem in SVR form as a convex optimization problem, specifically a semi-definite programming (SDP) problem. An additional constraint is added to reduce the SDP to a quadratically constrained quadratic programming (QCQP) problem. After this optimization, investigation of the relevancy of SVR to superresolution proceeds with the possibility of using a single and general support vector regression for all image content, and the results are impressive for small training sets. This idea is improved upon by observing structural properties in the discrete cosine transform (DCT) domain to aid in learning the regression. Further improvement involves a combination of classification and SVR-based techniques, extending works in resolution synthesis. This method, termed kernel resolution synthesis, uses specific regressors for isolated image content to describe the domain through a partitioned look of the vector space, thereby yielding good results.

  9. Vector-model-supported approach in prostate plan optimization

    International Nuclear Information System (INIS)

    Liu, Eva Sau Fan; Wu, Vincent Wing Cheung; Harris, Benjamin; Lehman, Margot; Pryor, David; Chan, Lawrence Wing Chi

    2017-01-01

    Lengthy time consumed in traditional manual plan optimization can limit the use of step-and-shoot intensity-modulated radiotherapy/volumetric-modulated radiotherapy (S&S IMRT/VMAT). A vector model base, retrieving similar radiotherapy cases, was developed with respect to the structural and physiologic features extracted from the Digital Imaging and Communications in Medicine (DICOM) files. Planning parameters were retrieved from the selected similar reference case and applied to the test case to bypass the gradual adjustment of planning parameters. Therefore, the planning time spent on the traditional trial-and-error manual optimization approach in the beginning of optimization could be reduced. Each S&S IMRT/VMAT prostate reference database comprised 100 previously treated cases. Prostate cases were replanned with both traditional optimization and vector-model-supported optimization based on the oncologists' clinical dose prescriptions. A total of 360 plans, which consisted of 30 cases of S&S IMRT, 30 cases of 1-arc VMAT, and 30 cases of 2-arc VMAT plans including first optimization and final optimization with/without vector-model-supported optimization, were compared using the 2-sided t-test and paired Wilcoxon signed rank test, with a significance level of 0.05 and a false discovery rate of less than 0.05. For S&S IMRT, 1-arc VMAT, and 2-arc VMAT prostate plans, there was a significant reduction in the planning time and iteration with vector-model-supported optimization by almost 50%. When the first optimization plans were compared, 2-arc VMAT prostate plans had better plan quality than 1-arc VMAT plans. The volume receiving 35 Gy in the femoral head for 2-arc VMAT plans was reduced with the vector-model-supported optimization compared with the traditional manual optimization approach. Otherwise, the quality of plans from both approaches was comparable. Vector-model-supported optimization was shown to offer much shortened planning time and iteration

  10. Vector-model-supported approach in prostate plan optimization

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Eva Sau Fan [Department of Radiation Oncology, Princess Alexandra Hospital, Brisbane (Australia); Department of Health Technology and Informatics, The Hong Kong Polytechnic University (Hong Kong); Wu, Vincent Wing Cheung [Department of Health Technology and Informatics, The Hong Kong Polytechnic University (Hong Kong); Harris, Benjamin [Department of Radiation Oncology, Princess Alexandra Hospital, Brisbane (Australia); Lehman, Margot; Pryor, David [Department of Radiation Oncology, Princess Alexandra Hospital, Brisbane (Australia); School of Medicine, University of Queensland (Australia); Chan, Lawrence Wing Chi, E-mail: wing.chi.chan@polyu.edu.hk [Department of Health Technology and Informatics, The Hong Kong Polytechnic University (Hong Kong)

    2017-07-01

    Lengthy time consumed in traditional manual plan optimization can limit the use of step-and-shoot intensity-modulated radiotherapy/volumetric-modulated radiotherapy (S&S IMRT/VMAT). A vector model base, retrieving similar radiotherapy cases, was developed with respect to the structural and physiologic features extracted from the Digital Imaging and Communications in Medicine (DICOM) files. Planning parameters were retrieved from the selected similar reference case and applied to the test case to bypass the gradual adjustment of planning parameters. Therefore, the planning time spent on the traditional trial-and-error manual optimization approach in the beginning of optimization could be reduced. Each S&S IMRT/VMAT prostate reference database comprised 100 previously treated cases. Prostate cases were replanned with both traditional optimization and vector-model-supported optimization based on the oncologists' clinical dose prescriptions. A total of 360 plans, which consisted of 30 cases of S&S IMRT, 30 cases of 1-arc VMAT, and 30 cases of 2-arc VMAT plans including first optimization and final optimization with/without vector-model-supported optimization, were compared using the 2-sided t-test and paired Wilcoxon signed rank test, with a significance level of 0.05 and a false discovery rate of less than 0.05. For S&S IMRT, 1-arc VMAT, and 2-arc VMAT prostate plans, there was a significant reduction in the planning time and iteration with vector-model-supported optimization by almost 50%. When the first optimization plans were compared, 2-arc VMAT prostate plans had better plan quality than 1-arc VMAT plans. The volume receiving 35 Gy in the femoral head for 2-arc VMAT plans was reduced with the vector-model-supported optimization compared with the traditional manual optimization approach. Otherwise, the quality of plans from both approaches was comparable. Vector-model-supported optimization was shown to offer much shortened planning time and iteration

  11. A support vector machine approach for detection of microcalcifications.

    Science.gov (United States)

    El-Naqa, Issam; Yang, Yongyi; Wernick, Miles N; Galatsanos, Nikolas P; Nishikawa, Robert M

    2002-12-01

    In this paper, we investigate an approach based on support vector machines (SVMs) for detection of microcalcification (MC) clusters in digital mammograms, and propose a successive enhancement learning scheme for improved performance. SVM is a machine-learning method, based on the principle of structural risk minimization, which performs well when applied to data outside the training set. We formulate MC detection as a supervised-learning problem and apply SVM to develop the detection algorithm. We use the SVM to detect at each location in the image whether an MC is present or not. We tested the proposed method using a database of 76 clinical mammograms containing 1120 MCs. We use free-response receiver operating characteristic curves to evaluate detection performance, and compare the proposed algorithm with several existing methods. In our experiments, the proposed SVM framework outperformed all the other methods tested. In particular, a sensitivity as high as 94% was achieved by the SVM method at an error rate of one false-positive cluster per image. The ability of SVM to out perform several well-known methods developed for the widely studied problem of MC detection suggests that SVM is a promising technique for object detection in a medical imaging application.

  12. A Wavelet Kernel-Based Primal Twin Support Vector Machine for Economic Development Prediction

    Directory of Open Access Journals (Sweden)

    Fang Su

    2013-01-01

    Full Text Available Economic development forecasting allows planners to choose the right strategies for the future. This study is to propose economic development prediction method based on the wavelet kernel-based primal twin support vector machine algorithm. As gross domestic product (GDP is an important indicator to measure economic development, economic development prediction means GDP prediction in this study. The wavelet kernel-based primal twin support vector machine algorithm can solve two smaller sized quadratic programming problems instead of solving a large one as in the traditional support vector machine algorithm. Economic development data of Anhui province from 1992 to 2009 are used to study the prediction performance of the wavelet kernel-based primal twin support vector machine algorithm. The comparison of mean error of economic development prediction between wavelet kernel-based primal twin support vector machine and traditional support vector machine models trained by the training samples with the 3–5 dimensional input vectors, respectively, is given in this paper. The testing results show that the economic development prediction accuracy of the wavelet kernel-based primal twin support vector machine model is better than that of traditional support vector machine.

  13. The Ability–Motivation–Opportunity Framework for Team Innovation: Efficacy Beliefs, Proactive Personalities, Supportive Supervision and Team Innovation

    Directory of Open Access Journals (Sweden)

    Jana Krapež Trošt

    2016-01-01

    Full Text Available Based on ability–motivation–opportunity theoretical framework, the study explores the interplay among team members’ proactive personalities (abilities, collective efficacy (motivation, and supportive supervision (opportunity, and their interaction in predicting team innovation. Multi-level study of 249 employees nested within 64 teams from one German and three Slovenian hi-tech companies showed that collective efficacy was positively related to team innovation. However, the effect of collective efficacy on team innovation was weaker when high levels of supportive supervision and proactivity moderated this relationship. When teams perceived lower levels of collective efficacy, team proactivity, and supportive supervision were more important for achieving higher levels of team innovation as they were when teams perceived lower levels of motivation. We discuss theoretical and practical implications

  14. Prediction of Machine Tool Condition Using Support Vector Machine

    International Nuclear Information System (INIS)

    Wang Peigong; Meng Qingfeng; Zhao Jian; Li Junjie; Wang Xiufeng

    2011-01-01

    Condition monitoring and predicting of CNC machine tools are investigated in this paper. Considering the CNC machine tools are often small numbers of samples, a condition predicting method for CNC machine tools based on support vector machines (SVMs) is proposed, then one-step and multi-step condition prediction models are constructed. The support vector machines prediction models are used to predict the trends of working condition of a certain type of CNC worm wheel and gear grinding machine by applying sequence data of vibration signal, which is collected during machine processing. And the relationship between different eigenvalue in CNC vibration signal and machining quality is discussed. The test result shows that the trend of vibration signal Peak-to-peak value in surface normal direction is most relevant to the trend of surface roughness value. In trends prediction of working condition, support vector machine has higher prediction accuracy both in the short term ('One-step') and long term (multi-step) prediction compared to autoregressive (AR) model and the RBF neural network. Experimental results show that it is feasible to apply support vector machine to CNC machine tool condition prediction.

  15. Phase Space Prediction of Chaotic Time Series with Nu-Support Vector Machine Regression

    International Nuclear Information System (INIS)

    Ye Meiying; Wang Xiaodong

    2005-01-01

    A new class of support vector machine, nu-support vector machine, is discussed which can handle both classification and regression. We focus on nu-support vector machine regression and use it for phase space prediction of chaotic time series. The effectiveness of the method is demonstrated by applying it to the Henon map. This study also compares nu-support vector machine with back propagation (BP) networks in order to better evaluate the performance of the proposed methods. The experimental results show that the nu-support vector machine regression obtains lower root mean squared error than the BP networks and provides an accurate chaotic time series prediction. These results can be attributable to the fact that nu-support vector machine implements the structural risk minimization principle and this leads to better generalization than the BP networks.

  16. Product Quality Modelling Based on Incremental Support Vector Machine

    International Nuclear Information System (INIS)

    Wang, J; Zhang, W; Qin, B; Shi, W

    2012-01-01

    Incremental Support vector machine (ISVM) is a new learning method developed in recent years based on the foundations of statistical learning theory. It is suitable for the problem of sequentially arriving field data and has been widely used for product quality prediction and production process optimization. However, the traditional ISVM learning does not consider the quality of the incremental data which may contain noise and redundant data; it will affect the learning speed and accuracy to a great extent. In order to improve SVM training speed and accuracy, a modified incremental support vector machine (MISVM) is proposed in this paper. Firstly, the margin vectors are extracted according to the Karush-Kuhn-Tucker (KKT) condition; then the distance from the margin vectors to the final decision hyperplane is calculated to evaluate the importance of margin vectors, where the margin vectors are removed while their distance exceed the specified value; finally, the original SVs and remaining margin vectors are used to update the SVM. The proposed MISVM can not only eliminate the unimportant samples such as noise samples, but also can preserve the important samples. The MISVM has been experimented on two public data and one field data of zinc coating weight in strip hot-dip galvanizing, and the results shows that the proposed method can improve the prediction accuracy and the training speed effectively. Furthermore, it can provide the necessary decision supports and analysis tools for auto control of product quality, and also can extend to other process industries, such as chemical process and manufacturing process.

  17. Supportive supervision and constructive relationships with healthcare workers support CHW performance: Use of a qualitative framework to evaluate CHW programming in Uganda.

    Science.gov (United States)

    Ludwick, Teralynn; Turyakira, Eleanor; Kyomuhangi, Teddy; Manalili, Kimberly; Robinson, Sheila; Brenner, Jennifer L

    2018-02-13

    While evidence supports community health worker (CHW) capacity to improve maternal and newborn health in less-resourced countries, key implementation gaps remain. Tools for assessing CHW performance and evidence on what programmatic components affect performance are lacking. This study developed and tested a qualitative evaluative framework and tool to assess CHW team performance in a district program in rural Uganda. A new assessment framework was developed to collect and analyze qualitative evidence based on CHW perspectives on seven program components associated with effectiveness (selection; training; community embeddedness; peer support; supportive supervision; relationship with other healthcare workers; retention and incentive structures). Focus groups were conducted with four high/medium-performing CHW teams and four low-performing CHW teams selected through random, stratified sampling. Content analysis involved organizing focus group transcripts according to the seven program effectiveness components, and assigning scores to each component per focus group. Four components, 'supportive supervision', 'good relationships with other healthcare workers', 'peer support', and 'retention and incentive structures' received the lowest overall scores. Variances in scores between 'high'/'medium'- and 'low'-performing CHW teams were largest for 'supportive supervision' and 'good relationships with other healthcare workers.' Our analysis suggests that in the Bushenyi intervention context, CHW team performance is highly correlated with the quality of supervision and relationships with other healthcare workers. CHWs identified key performance-related issues of absentee supervisors, referral system challenges, and lack of engagement/respect by health workers. Other less-correlated program components warrant further study and may have been impacted by relatively consistent program implementation within our limited study area. Applying process-oriented measurement tools are

  18. Active relearning for robust supervised classification of pulmonary emphysema

    Science.gov (United States)

    Raghunath, Sushravya; Rajagopalan, Srinivasan; Karwoski, Ronald A.; Bartholmai, Brian J.; Robb, Richard A.

    2012-03-01

    Radiologists are adept at recognizing the appearance of lung parenchymal abnormalities in CT scans. However, the inconsistent differential diagnosis, due to subjective aggregation, mandates supervised classification. Towards optimizing Emphysema classification, we introduce a physician-in-the-loop feedback approach in order to minimize uncertainty in the selected training samples. Using multi-view inductive learning with the training samples, an ensemble of Support Vector Machine (SVM) models, each based on a specific pair-wise dissimilarity metric, was constructed in less than six seconds. In the active relearning phase, the ensemble-expert label conflicts were resolved by an expert. This just-in-time feedback with unoptimized SVMs yielded 15% increase in classification accuracy and 25% reduction in the number of support vectors. The generality of relearning was assessed in the optimized parameter space of six different classifiers across seven dissimilarity metrics. The resultant average accuracy improved to 21%. The co-operative feedback method proposed here could enhance both diagnostic and staging throughput efficiency in chest radiology practice.

  19. An Ensemble of Deep Support Vector Machines for Image Categorization

    NARCIS (Netherlands)

    Abdullah, Azizi; Veltkamp, Remco C.; Wiering, Marco

    2009-01-01

    This paper presents the deep support vector machine (D-SVM) inspired by the increasing popularity of deep belief networks for image recognition. Our deep SVM trains an SVM in the standard way and then uses the kernel activations of support vectors as inputs for training another SVM at the next

  20. Twin support vector machines models, extensions and applications

    CERN Document Server

    Jayadeva; Chandra, Suresh

    2017-01-01

    This book provides a systematic and focused study of the various aspects of twin support vector machines (TWSVM) and related developments for classification and regression. In addition to presenting most of the basic models of TWSVM and twin support vector regression (TWSVR) available in the literature, it also discusses the important and challenging applications of this new machine learning methodology. A chapter on “Additional Topics” has been included to discuss kernel optimization and support tensor machine topics, which are comparatively new but have great potential in applications. It is primarily written for graduate students and researchers in the area of machine learning and related topics in computer science, mathematics, electrical engineering, management science and finance.

  1. Indonesian Stock Prediction using Support Vector Machine (SVM

    Directory of Open Access Journals (Sweden)

    Santoso Murtiyanto

    2018-01-01

    Full Text Available This project is part of developing software to provide predictive information technology-based services artificial intelligence (Machine Intelligence or Machine Learning that will be utilized in the money market community. The prediction method used in this early stages uses the combination of Gaussian Mixture Model and Support Vector Machine with Python programming. The system predicts the price of Astra International (stock code: ASII.JK stock data. The data used was taken during 17 yr period of January 2000 until September 2017. Some data was used for training/modeling (80 % of data and the remainder (20 % was used for testing. An integrated model comprising Gaussian Mixture Model and Support Vector Machine system has been tested to predict stock market of ASII.JK for l d in advance. This model has been compared with the Market Cummulative Return. From the results, it is depicts that the Gaussian Mixture Model-Support Vector Machine based stock predicted model, offers significant improvement over the compared models resulting sharpe ratio of 3.22.

  2. Using support vector machine ensembles for target audience classification on Twitter.

    Science.gov (United States)

    Lo, Siaw Ling; Chiong, Raymond; Cornforth, David

    2015-01-01

    The vast amount and diversity of the content shared on social media can pose a challenge for any business wanting to use it to identify potential customers. In this paper, our aim is to investigate the use of both unsupervised and supervised learning methods for target audience classification on Twitter with minimal annotation efforts. Topic domains were automatically discovered from contents shared by followers of an account owner using Twitter Latent Dirichlet Allocation (LDA). A Support Vector Machine (SVM) ensemble was then trained using contents from different account owners of the various topic domains identified by Twitter LDA. Experimental results show that the methods presented are able to successfully identify a target audience with high accuracy. In addition, we show that using a statistical inference approach such as bootstrapping in over-sampling, instead of using random sampling, to construct training datasets can achieve a better classifier in an SVM ensemble. We conclude that such an ensemble system can take advantage of data diversity, which enables real-world applications for differentiating prospective customers from the general audience, leading to business advantage in the crowded social media space.

  3. Using support vector machine ensembles for target audience classification on Twitter.

    Directory of Open Access Journals (Sweden)

    Siaw Ling Lo

    Full Text Available The vast amount and diversity of the content shared on social media can pose a challenge for any business wanting to use it to identify potential customers. In this paper, our aim is to investigate the use of both unsupervised and supervised learning methods for target audience classification on Twitter with minimal annotation efforts. Topic domains were automatically discovered from contents shared by followers of an account owner using Twitter Latent Dirichlet Allocation (LDA. A Support Vector Machine (SVM ensemble was then trained using contents from different account owners of the various topic domains identified by Twitter LDA. Experimental results show that the methods presented are able to successfully identify a target audience with high accuracy. In addition, we show that using a statistical inference approach such as bootstrapping in over-sampling, instead of using random sampling, to construct training datasets can achieve a better classifier in an SVM ensemble. We conclude that such an ensemble system can take advantage of data diversity, which enables real-world applications for differentiating prospective customers from the general audience, leading to business advantage in the crowded social media space.

  4. Incremental Support Vector Machine Framework for Visual Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yuichi Motai

    2007-01-01

    Full Text Available Motivated by the emerging requirements of surveillance networks, we present in this paper an incremental multiclassification support vector machine (SVM technique as a new framework for action classification based on real-time multivideo collected by homogeneous sites. The technique is based on an adaptation of least square SVM (LS-SVM formulation but extends beyond the static image-based learning of current SVM methodologies. In applying the technique, an initial supervised offline learning phase is followed by a visual behavior data acquisition and an online learning phase during which the cluster head performs an ensemble of model aggregations based on the sensor nodes inputs. The cluster head then selectively switches on designated sensor nodes for future incremental learning. Combining sensor data offers an improvement over single camera sensing especially when the latter has an occluded view of the target object. The optimization involved alleviates the burdens of power consumption and communication bandwidth requirements. The resulting misclassification error rate, the iterative error reduction rate of the proposed incremental learning, and the decision fusion technique prove its validity when applied to visual sensor networks. Furthermore, the enabled online learning allows an adaptive domain knowledge insertion and offers the advantage of reducing both the model training time and the information storage requirements of the overall system which makes it even more attractive for distributed sensor networks communication.

  5. Support vector machine for automatic pain recognition

    Science.gov (United States)

    Monwar, Md Maruf; Rezaei, Siamak

    2009-02-01

    Facial expressions are a key index of emotion and the interpretation of such expressions of emotion is critical to everyday social functioning. In this paper, we present an efficient video analysis technique for recognition of a specific expression, pain, from human faces. We employ an automatic face detector which detects face from the stored video frame using skin color modeling technique. For pain recognition, location and shape features of the detected faces are computed. These features are then used as inputs to a support vector machine (SVM) for classification. We compare the results with neural network based and eigenimage based automatic pain recognition systems. The experiment results indicate that using support vector machine as classifier can certainly improve the performance of automatic pain recognition system.

  6. Support vector machine for diagnosis cancer disease: A comparative study

    Directory of Open Access Journals (Sweden)

    Nasser H. Sweilam

    2010-12-01

    Full Text Available Support vector machine has become an increasingly popular tool for machine learning tasks involving classification, regression or novelty detection. Training a support vector machine requires the solution of a very large quadratic programming problem. Traditional optimization methods cannot be directly applied due to memory restrictions. Up to now, several approaches exist for circumventing the above shortcomings and work well. Another learning algorithm, particle swarm optimization, Quantum-behave Particle Swarm for training SVM is introduced. Another approach named least square support vector machine (LSSVM and active set strategy are introduced. The obtained results by these methods are tested on a breast cancer dataset and compared with the exact solution model problem.

  7. Mining protein function from text using term-based support vector machines

    Science.gov (United States)

    Rice, Simon B; Nenadic, Goran; Stapley, Benjamin J

    2005-01-01

    Background Text mining has spurred huge interest in the domain of biology. The goal of the BioCreAtIvE exercise was to evaluate the performance of current text mining systems. We participated in Task 2, which addressed assigning Gene Ontology terms to human proteins and selecting relevant evidence from full-text documents. We approached it as a modified form of the document classification task. We used a supervised machine-learning approach (based on support vector machines) to assign protein function and select passages that support the assignments. As classification features, we used a protein's co-occurring terms that were automatically extracted from documents. Results The results evaluated by curators were modest, and quite variable for different problems: in many cases we have relatively good assignment of GO terms to proteins, but the selected supporting text was typically non-relevant (precision spanning from 3% to 50%). The method appears to work best when a substantial set of relevant documents is obtained, while it works poorly on single documents and/or short passages. The initial results suggest that our approach can also mine annotations from text even when an explicit statement relating a protein to a GO term is absent. Conclusion A machine learning approach to mining protein function predictions from text can yield good performance only if sufficient training data is available, and significant amount of supporting data is used for prediction. The most promising results are for combined document retrieval and GO term assignment, which calls for the integration of methods developed in BioCreAtIvE Task 1 and Task 2. PMID:15960835

  8. Compactly Supported Basis Functions as Support Vector Kernels for Classification.

    Science.gov (United States)

    Wittek, Peter; Tan, Chew Lim

    2011-10-01

    Wavelet kernels have been introduced for both support vector regression and classification. Most of these wavelet kernels do not use the inner product of the embedding space, but use wavelets in a similar fashion to radial basis function kernels. Wavelet analysis is typically carried out on data with a temporal or spatial relation between consecutive data points. We argue that it is possible to order the features of a general data set so that consecutive features are statistically related to each other, thus enabling us to interpret the vector representation of an object as a series of equally or randomly spaced observations of a hypothetical continuous signal. By approximating the signal with compactly supported basis functions and employing the inner product of the embedding L2 space, we gain a new family of wavelet kernels. Empirical results show a clear advantage in favor of these kernels.

  9. Assessing support for supervised injection services among community stakeholders in London, Canada.

    Science.gov (United States)

    Bardwell, Geoff; Scheim, Ayden; Mitra, Sanjana; Kerr, Thomas

    2017-10-01

    Few qualitative studies have examined support for supervised injection services (SIS), and these have been restricted to large cities. This study aimed to assess support for SIS among a diverse representation of community stakeholders in London, a mid-sized city in southwestern Ontario, Canada. This qualitative study was undertaken as part of the Ontario Integrated Supervised Injection Services Feasibility Study. We used purposive sampling methods to recruit a diversity of key informants (n=20) from five sectors: healthcare; social services; government and municipal services; police and emergency services; and the business and community sector. Interview data, collected via one-to-one semi structured interviews, were coded and analyzed using thematic analyses through NVivo 10 software. Interview participants unanimously supported the implementation of SIS in London. However, participant support for SIS was met with some implementation-related preferences and/or conditions. These included centralization or decentralization of SIS; accessibility of SIS for people who inject drugs; proximity of SIS to interview participants; and other services and strategies offered alongside SIS. The results of this study challenge the assumptions that smaller cities like London may be unlikely to support SIS. Community stakeholders were supportive of the implementation of SIS with some preferences or conditions. Interview participants had differing perspectives, but ultimately supported similar end goals of accessibility and reducing community harms associated with injection drug use. Future research and SIS programming should consider these factors when determining optimal service delivery in ways that increase support from a diversity of community stakeholders. Copyright © 2017 Elsevier B.V. All rights reserved.

  10. 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

  11. A support vector density-based importance sampling for reliability assessment

    International Nuclear Information System (INIS)

    Dai, Hongzhe; Zhang, Hao; Wang, Wei

    2012-01-01

    An importance sampling method based on the adaptive Markov chain simulation and support vector density estimation is developed in this paper for efficient structural reliability assessment. The methodology involves the generation of samples that can adaptively populate the important region by the adaptive Metropolis algorithm, and the construction of importance sampling density by support vector density. The use of the adaptive Metropolis algorithm may effectively improve the convergence and stability of the classical Markov chain simulation. The support vector density can approximate the sampling density with fewer samples in comparison to the conventional kernel density estimation. The proposed importance sampling method can effectively reduce the number of structural analysis required for achieving a given accuracy. Examples involving both numerical and practical structural problems are given to illustrate the application and efficiency of the proposed methodology.

  12. How supportive supervision influences immunization session site practices: a quasi-experimental study in Odisha, India.

    Science.gov (United States)

    Panda, Bhuputra; Pati, Sanghamitra; Nallala, Srinivas; Chauhan, Abhimanyu S; Anasuya, Anita; Som, Meena; Zodpey, Sanjay

    2015-01-01

    Routine immunization (RI) is a key child survival intervention. Ensuring acceptable standards of RI service delivery is critical for optimal outcomes. Accumulated evidences suggest that 'supportive supervision' improves the quality of health care services in general. During 2009-2010, the Government of Odisha and UNICEF jointly piloted this strategy in four districts to improve RI program outcomes. The present study aims to assess the effect of this strategy on improvement of skills and practices at immunization session sites. A quasi-experimental 'post-test only' study design was adopted to compare the opinion and practices of frontline health workers and their supervisors in four intervention districts (IDs) with two control districts (CDs). Altogether, we interviewed 111 supervisor-supervisee (health worker) pairs using semi-structured interview schedules and case vignettes. We also directly observed health workers' practices during immunization sessions at 111 sites. Data were analyzed with SPSS version 16.0. The mean knowledge score of supervisors in CDs was significantly higher than in intervention groups. Variegated responses were obtained on case vignettes. The control group performed better in solving certain hypothetically asked problems, whereas the intervention group scored better in others. Health workers in IDs gave a lower rating to their respective supervisors' knowledge, skill, and frequency of supervision. Logistics and vaccine availability were better in CDs. Notwithstanding other limitations, supportive supervision may not have independent effects on improving the quality of immunization services. Addressing systemic issues, such as the availability of essential logistics, supply chain management, timely indenting, and financial resources, could complement the supportive supervision strategy in improving immunization service delivery.

  13. A novel improved fuzzy support vector machine based stock price trend forecast model

    OpenAIRE

    Wang, Shuheng; Li, Guohao; Bao, Yifan

    2018-01-01

    Application of fuzzy support vector machine in stock price forecast. Support vector machine is a new type of machine learning method proposed in 1990s. It can deal with classification and regression problems very successfully. Due to the excellent learning performance of support vector machine, the technology has become a hot research topic in the field of machine learning, and it has been successfully applied in many fields. However, as a new technology, there are many limitations to support...

  14. Support of the extremal measure in a vector equilibrium problem

    International Nuclear Information System (INIS)

    Lapik, M A

    2006-01-01

    A generalization of the Mhaskar-Saff functional is obtained for a vector equilibrium problem with an external field. As an application, the supports of the equilibrium measures are found in a special vector equilibrium problem with Nikishin matrix.

  15. Coal demand prediction based on a support vector machine model

    Energy Technology Data Exchange (ETDEWEB)

    Jia, Cun-liang; Wu, Hai-shan; Gong, Dun-wei [China University of Mining & Technology, Xuzhou (China). School of Information and Electronic Engineering

    2007-01-15

    A forecasting model for coal demand of China using a support vector regression was constructed. With the selected embedding dimension, the output vectors and input vectors were constructed based on the coal demand of China from 1980 to 2002. After compared with lineal kernel and Sigmoid kernel, a radial basis function(RBF) was adopted as the kernel function. By analyzing the relationship between the error margin of prediction and the model parameters, the proper parameters were chosen. The support vector machines (SVM) model with multi-input and single output was proposed. Compared the predictor based on RBF neural networks with test datasets, the results show that the SVM predictor has higher precision and greater generalization ability. In the end, the coal demand from 2003 to 2006 is accurately forecasted. l0 refs., 2 figs., 4 tabs.

  16. Neutron Buildup Factors Calculation for Support Vector Regression Application in Shielding Analysis

    International Nuclear Information System (INIS)

    Duckic, P.; Matijevic, M.; Grgic, D.

    2016-01-01

    In this paper initial set of data for neutron buildup factors determination using Support Vector Regression (SVR) method is prepared. The performance of SVR technique strongly depends on the quality of information used for model training. Thus it is very important to provide representable data to the SVR. SVR is a supervised type of learning so it demands data in the input/output form. In the case of neutron buildup factors estimation, the input parameters are the incident neutron energy, shielding thickness and shielding material and the output parameter is the neutron buildup factor value. So far the initial sets of data for different shielding configurations have been obtained using SCALE4.4 sequence SAS3. However, this results were obtained using group constants, thus the incident neutron energy was determined as the average value for each energy group. Obtained this way, the data provided to the SVR are fewer and therefore insufficient. More valuable information is obtained using SCALE6.2beta5 sequence MAVRIC which can perform calculations for the explicit incident neutron energy, which leads to greater maneuvering possibilities when active learning measures are employed, and consequently improves the quality of the developed SVR model.(author).

  17. Broiler chickens can benefit from machine learning: support vector machine analysis of observational epidemiological data.

    Science.gov (United States)

    Hepworth, Philip J; Nefedov, Alexey V; Muchnik, Ilya B; Morgan, Kenton L

    2012-08-07

    Machine-learning algorithms pervade our daily lives. In epidemiology, supervised machine learning has the potential for classification, diagnosis and risk factor identification. Here, we report the use of support vector machine learning to identify the features associated with hock burn on commercial broiler farms, using routinely collected farm management data. These data lend themselves to analysis using machine-learning techniques. Hock burn, dermatitis of the skin over the hock, is an important indicator of broiler health and welfare. Remarkably, this classifier can predict the occurrence of high hock burn prevalence with accuracy of 0.78 on unseen data, as measured by the area under the receiver operating characteristic curve. We also compare the results with those obtained by standard multi-variable logistic regression and suggest that this technique provides new insights into the data. This novel application of a machine-learning algorithm, embedded in poultry management systems could offer significant improvements in broiler health and welfare worldwide.

  18. Applications of Support Vector Machine (SVM) Learning in Cancer Genomics

    OpenAIRE

    HUANG, SHUJUN; CAI, NIANGUANG; PACHECO, PEDRO PENZUTI; NARANDES, SHAVIRA; WANG, YANG; XU, WAYNE

    2017-01-01

    Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that has been used for cancer genomic classification or subtyping. Today, as advancements in high-throughput technologies lead to production of large amounts of genomic and epigenomic data, the classification feature of SVMs is expanding its use in cancer genomics, leading to the discovery of new biomarkers, new drug targets, and a better ...

  19. A Two-Layer Least Squares Support Vector Machine Approach to Credit Risk Assessment

    Science.gov (United States)

    Liu, Jingli; Li, Jianping; Xu, Weixuan; Shi, Yong

    Least squares support vector machine (LS-SVM) is a revised version of support vector machine (SVM) and has been proved to be a useful tool for pattern recognition. LS-SVM had excellent generalization performance and low computational cost. In this paper, we propose a new method called two-layer least squares support vector machine which combines kernel principle component analysis (KPCA) and linear programming form of least square support vector machine. With this method sparseness and robustness is obtained while solving large dimensional and large scale database. A U.S. commercial credit card database is used to test the efficiency of our method and the result proved to be a satisfactory one.

  20. Hyperspectral image classification using Support Vector Machine

    International Nuclear Information System (INIS)

    Moughal, T A

    2013-01-01

    Classification of land cover hyperspectral images is a very challenging task due to the unfavourable ratio between the number of spectral bands and the number of training samples. The focus in many applications is to investigate an effective classifier in terms of accuracy. The conventional multiclass classifiers have the ability to map the class of interest but the considerable efforts and large training sets are required to fully describe the classes spectrally. Support Vector Machine (SVM) is suggested in this paper to deal with the multiclass problem of hyperspectral imagery. The attraction to this method is that it locates the optimal hyper plane between the class of interest and the rest of the classes to separate them in a new high-dimensional feature space by taking into account only the training samples that lie on the edge of the class distributions known as support vectors and the use of the kernel functions made the classifier more flexible by making it robust against the outliers. A comparative study has undertaken to find an effective classifier by comparing Support Vector Machine (SVM) to the other two well known classifiers i.e. Maximum likelihood (ML) and Spectral Angle Mapper (SAM). At first, the Minimum Noise Fraction (MNF) was applied to extract the best possible features form the hyperspectral imagery and then the resulting subset of the features was applied to the classifiers. Experimental results illustrate that the integration of MNF and SVM technique significantly reduced the classification complexity and improves the classification accuracy.

  1. Landslide susceptibility mapping using support vector machine and ...

    Indian Academy of Sciences (India)

    the prediction rate methods, the validation process was performed by ... support vector machine (SVM); geographical information systems (GIS); ... 2012a), decision tree methods (Akgun .... gence or divergence of water during downhill flow.

  2. Support Vector Machine and Application in Seizure Prediction

    KAUST Repository

    Qiu, Simeng

    2018-04-01

    Nowadays, Machine learning (ML) has been utilized in various kinds of area which across the range from engineering field to business area. In this paper, we first present several kernel machine learning methods of solving classification, regression and clustering problems. These have good performance but also have some limitations. We present examples to each method and analyze the advantages and disadvantages for solving different scenarios. Then we focus on one of the most popular classification methods, Support Vectors Machine (SVM). In addition, we introduce the basic theory, advantages and scenarios of using Support Vector Machine (SVM) deal with classification problems. We also explain a convenient approach of tacking SVM problems which are called Sequential Minimal Optimization (SMO). Moreover, one class SVM can be understood in a different way which is called Support Vector Data Description (SVDD). This is a famous non-linear model problem compared with SVM problems, SVDD can be solved by utilizing Gaussian RBF kernel function combined with SMO. At last, we compared the difference and performance of SVM-SMO implementation and SVM-SVDD implementation. About the application part, we utilized SVM method to handle seizure forecasting in canine epilepsy, after comparing the results from different methods such as random forest, extremely randomized tree, and SVM to classify preictal (pre-seizure) and interictal (interval-seizure) binary data. We draw the conclusion that SVM has the best performance.

  3. Peer Support in Negotiating Multiple Relationships within Supervision among Counselor Education Doctoral Students

    Science.gov (United States)

    Minor, Amanda J.; Pimpleton, Asher; Stinchfield, Tracy; Stevens, Heath; Othman, Nor Asma

    2013-01-01

    Counselor education doctoral students (CEDSs), like other doctoral students, need assistance and support to ensure their self-care. One area markedly affecting self-care is one's relationships with others. The purpose of this article is to examine the multiple relationships involved within CEDSs supervision, the potential areas to utilize peer…

  4. SAM: Support Vector Machine Based Active Queue Management

    International Nuclear Information System (INIS)

    Shah, M.S.

    2014-01-01

    Recent years have seen an increasing interest in the design of AQM (Active Queue Management) controllers. The purpose of these controllers is to manage the network congestion under varying loads, link delays and bandwidth. In this paper, a new AQM controller is proposed which is trained by using the SVM (Support Vector Machine) with the RBF (Radial Basis Function) kernal. The proposed controller is called the support vector based AQM (SAM) controller. The performance of the proposed controller has been compared with three conventional AQM controllers, namely the Random Early Detection, Blue and Proportional Plus Integral Controller. The preliminary simulation studies show that the performance of the proposed controller is comparable to the conventional controllers. However, the proposed controller is more efficient in controlling the queue size than the conventional controllers. (author)

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

    International Nuclear Information System (INIS)

    Scalzo, Fabien; Hu, Xiao

    2013-01-01

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

  6. Prediction of backbone dihedral angles and protein secondary structure using support vector machines

    Directory of Open Access Journals (Sweden)

    Hirst Jonathan D

    2009-12-01

    Full Text Available Abstract Background The prediction of the secondary structure of a protein is a critical step in the prediction of its tertiary structure and, potentially, its function. Moreover, the backbone dihedral angles, highly correlated with secondary structures, provide crucial information about the local three-dimensional structure. Results We predict independently both the secondary structure and the backbone dihedral angles and combine the results in a loop to enhance each prediction reciprocally. Support vector machines, a state-of-the-art supervised classification technique, achieve secondary structure predictive accuracy of 80% on a non-redundant set of 513 proteins, significantly higher than other methods on the same dataset. The dihedral angle space is divided into a number of regions using two unsupervised clustering techniques in order to predict the region in which a new residue belongs. The performance of our method is comparable to, and in some cases more accurate than, other multi-class dihedral prediction methods. Conclusions We have created an accurate predictor of backbone dihedral angles and secondary structure. Our method, called DISSPred, is available online at http://comp.chem.nottingham.ac.uk/disspred/.

  7. Community detection in complex networks using proximate support vector clustering

    Science.gov (United States)

    Wang, Feifan; Zhang, Baihai; Chai, Senchun; Xia, Yuanqing

    2018-03-01

    Community structure, one of the most attention attracting properties in complex networks, has been a cornerstone in advances of various scientific branches. A number of tools have been involved in recent studies concentrating on the community detection algorithms. In this paper, we propose a support vector clustering method based on a proximity graph, owing to which the introduced algorithm surpasses the traditional support vector approach both in accuracy and complexity. Results of extensive experiments undertaken on computer generated networks and real world data sets illustrate competent performances in comparison with the other counterparts.

  8. Research on intrusion detection based on Kohonen network and support vector machine

    Science.gov (United States)

    Shuai, Chunyan; Yang, Hengcheng; Gong, Zeweiyi

    2018-05-01

    In view of the problem of low detection accuracy and the long detection time of support vector machine, which directly applied to the network intrusion detection system. Optimization of SVM parameters can greatly improve the detection accuracy, but it can not be applied to high-speed network because of the long detection time. a method based on Kohonen neural network feature selection is proposed to reduce the optimization time of support vector machine parameters. Firstly, this paper is to calculate the weights of the KDD99 network intrusion data by Kohonen network and select feature by weight. Then, after the feature selection is completed, genetic algorithm (GA) and grid search method are used for parameter optimization to find the appropriate parameters and classify them by support vector machines. By comparing experiments, it is concluded that feature selection can reduce the time of parameter optimization, which has little influence on the accuracy of classification. The experiments suggest that the support vector machine can be used in the network intrusion detection system and reduce the missing rate.

  9. LINEAR KERNEL SUPPORT VECTOR MACHINES FOR MODELING PORE-WATER PRESSURE RESPONSES

    Directory of Open Access Journals (Sweden)

    KHAMARUZAMAN W. YUSOF

    2017-08-01

    Full Text Available Pore-water pressure responses are vital in many aspects of slope management, design and monitoring. Its measurement however, is difficult, expensive and time consuming. Studies on its predictions are lacking. Support vector machines with linear kernel was used here to predict the responses of pore-water pressure to rainfall. Pore-water pressure response data was collected from slope instrumentation program. Support vector machine meta-parameter calibration and model development was carried out using grid search and k-fold cross validation. The mean square error for the model on scaled test data is 0.0015 and the coefficient of determination is 0.9321. Although pore-water pressure response to rainfall is a complex nonlinear process, the use of linear kernel support vector machine can be employed where high accuracy can be sacrificed for computational ease and time.

  10. Implementing the supportive supervision intervention for registered nurses in a long-term care home: a feasibility study.

    Science.gov (United States)

    McGilton, Katherine S; Profetto-McGrath, Joanne; Robinson, Angela

    2013-11-01

    This pilot study was conducted in response to the call in 2009 by the International Association of Gerontology and Geriatrics to focus on effective leadership structures in nursing homes and to develop leadership capacity. Few researchers have evaluated interventions aimed at enhancing the leadership ability of registered nurses in long-term care. The aim of the pilot study was to test the feasibility of a three-part supportive supervisory intervention to improve supervisory skills of registered nurses in long-term care. A repeated measures group design was used. Quantitative data were collected from healthcare aides, licensed practical nurses (i.e., supervised staff), and registered nurses (i.e., supervisors). Focus groups with care managers and supervisors examined perceptions of the intervention. There were nonsignificant changes in both the registered nurse supervisors' job satisfaction and the supervised staff's perception of their supervisors' support. Supervised staff scores indicated an increase in the use of research utilization but did not reflect an increase in job satisfaction. Focus group discussions revealed that the supervisors and care managers perceived the workshop to be valuable; however, the weekly self-reflection, coaching, and mentoring components of the intervention were rare and inconsistent. While the primary outcomes were not influenced by the Supportive Supervision Intervention, further effort is required to understand how best to enhance the supportive supervisory skills of RNs. Examples of how to improve the possibility of a successful intervention are advanced. Effective supervisory skills among registered nurses are crucial for improving the quality of care in long-term care homes. Registered nurses are receptive to interventions that will enhance their roles as supervisors. © 2013 Sigma Theta Tau International.

  11. A Novel Support Vector Machine with Globality-Locality Preserving

    Directory of Open Access Journals (Sweden)

    Cheng-Long Ma

    2014-01-01

    Full Text Available Support vector machine (SVM is regarded as a powerful method for pattern classification. However, the solution of the primal optimal model of SVM is susceptible for class distribution and may result in a nonrobust solution. In order to overcome this shortcoming, an improved model, support vector machine with globality-locality preserving (GLPSVM, is proposed. It introduces globality-locality preserving into the standard SVM, which can preserve the manifold structure of the data space. We complete rich experiments on the UCI machine learning data sets. The results validate the effectiveness of the proposed model, especially on the Wine and Iris databases; the recognition rate is above 97% and outperforms all the algorithms that were developed from SVM.

  12. Supervision, mentorship and peer networks: how Estonian early career researchers get (or fail to get support

    Directory of Open Access Journals (Sweden)

    Jaana Eigi

    2018-03-01

    Full Text Available The paper analyses issues related to supervision and support of early career researchers in Estonian academia. We use nine focus groups interviews conducted in 2015 with representatives of social sciences in order to identify early career researchers’ needs with respect to support, frustrations they may experience, and resources they may have for addressing them. Our crucial contribution is the identification of wider support networks of peers and colleagues that may compensate, partially or even fully, for failures of official supervision. On the basis of our analysis we argue that support for early career researchers should take into account the resources they already possess but also recognise the importance of wider academic culture, including funding and employment patterns, and the roles of supervisors and senior researchers in ensuring successful functioning of support networks. Through analysing the conditions for the development of early career researchers – producers of knowledge – our paper contributes to social epistemology understood as analysis of specific forms of social organisation of knowledge production.

  13. Weighted K-means support vector machine for cancer prediction.

    Science.gov (United States)

    Kim, SungHwan

    2016-01-01

    To date, the support vector machine (SVM) has been widely applied to diverse bio-medical fields to address disease subtype identification and pathogenicity of genetic variants. In this paper, I propose the weighted K-means support vector machine (wKM-SVM) and weighted support vector machine (wSVM), for which I allow the SVM to impose weights to the loss term. Besides, I demonstrate the numerical relations between the objective function of the SVM and weights. Motivated by general ensemble techniques, which are known to improve accuracy, I directly adopt the boosting algorithm to the newly proposed weighted KM-SVM (and wSVM). For predictive performance, a range of simulation studies demonstrate that the weighted KM-SVM (and wSVM) with boosting outperforms the standard KM-SVM (and SVM) including but not limited to many popular classification rules. I applied the proposed methods to simulated data and two large-scale real applications in the TCGA pan-cancer methylation data of breast and kidney cancer. In conclusion, the weighted KM-SVM (and wSVM) increases accuracy of the classification model, and will facilitate disease diagnosis and clinical treatment decisions to benefit patients. A software package (wSVM) is publicly available at the R-project webpage (https://www.r-project.org).

  14. Support vector machine incremental learning triggered by wrongly predicted samples

    Science.gov (United States)

    Tang, Ting-long; Guan, Qiu; Wu, Yi-rong

    2018-05-01

    According to the classic Karush-Kuhn-Tucker (KKT) theorem, at every step of incremental support vector machine (SVM) learning, the newly adding sample which violates the KKT conditions will be a new support vector (SV) and migrate the old samples between SV set and non-support vector (NSV) set, and at the same time the learning model should be updated based on the SVs. However, it is not exactly clear at this moment that which of the old samples would change between SVs and NSVs. Additionally, the learning model will be unnecessarily updated, which will not greatly increase its accuracy but decrease the training speed. Therefore, how to choose the new SVs from old sets during the incremental stages and when to process incremental steps will greatly influence the accuracy and efficiency of incremental SVM learning. In this work, a new algorithm is proposed to select candidate SVs and use the wrongly predicted sample to trigger the incremental processing simultaneously. Experimental results show that the proposed algorithm can achieve good performance with high efficiency, high speed and good accuracy.

  15. Experimental comparison of support vector machines with random ...

    Indian Academy of Sciences (India)

    dient method, support vector machines, and random forests to improve producer accuracy and overall classification accuracy. The performance comparison of these classifiers is valuable for a decision maker ... ping, surveillance system, resource management, tracking ... rocks, water bodies, and anthropogenic elements,.

  16. Lithium-ion battery remaining useful life prediction based on grey support vector machines

    Directory of Open Access Journals (Sweden)

    Xiaogang Li

    2015-12-01

    Full Text Available In this article, an improved grey prediction model is proposed to address low-accuracy prediction issue of grey forecasting model. The first step is using a trigonometric function to transform the original data sequence to smooth the data, which is called smoothness of grey prediction model, and then a grey support vector machine model by integrating the improved grey model with support vector machine is introduced. At the initial stage of the model, trigonometric functions and accumulation generation operation can be used to preprocess the data, which enhances the smoothness of the data and reduces the associated randomness. In addition, support vector machine is implemented to establish a prediction model for the pre-processed data and select the optimal model parameters via genetic algorithms. Finally, the data are restored through the ‘regressive generate’ operation to obtain the forecasting data. To prove that the grey support vector machine model is superior to the other models, the battery life data from the Center for Advanced Life Cycle Engineering are selected, and the presented model is used to predict the remaining useful life of the battery. The predicted result is compared to that of grey model and support vector machines. For a more intuitive comparison of the three models, this article quantifies the root mean square errors for these three different models in the case of different ratio of training samples and prediction samples. The results show that the effect of grey support vector machine model is optimal, and the corresponding root mean square error is only 3.18%.

  17. Prediction of Banking Systemic Risk Based on Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Shouwei Li

    2013-01-01

    Full Text Available Banking systemic risk is a complex nonlinear phenomenon and has shed light on the importance of safeguarding financial stability by recent financial crisis. According to the complex nonlinear characteristics of banking systemic risk, in this paper we apply support vector machine (SVM to the prediction of banking systemic risk in an attempt to suggest a new model with better explanatory power and stability. We conduct a case study of an SVM-based prediction model for Chinese banking systemic risk and find the experiment results showing that support vector machine is an efficient method in such case.

  18. Reconfigurable support vector machine classifier with approximate computing

    NARCIS (Netherlands)

    van Leussen, M.J.; Huisken, J.; Wang, L.; Jiao, H.; De Gyvez, J.P.

    2017-01-01

    Support Vector Machine (SVM) is one of the most popular machine learning algorithms. An energy-efficient SVM classifier is proposed in this paper, where approximate computing is utilized to reduce energy consumption and silicon area. A hardware architecture with reconfigurable kernels and

  19. GenSVM: a generalized multiclass support vector machine

    NARCIS (Netherlands)

    G.J.J. van den Burg (Gertjan); P.J.F. Groenen (Patrick)

    2016-01-01

    textabstractTraditional extensions of the binary support vector machine (SVM) to multiclass problems are either heuristics or require solving a large dual optimization problem. Here, a generalized multiclass SVM is proposed called GenSVM. In this method classification boundaries for a K-class

  20. Sistem Deteksi Retinopati Diabetik Menggunakan Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Wahyudi Setiawan

    2014-02-01

    Full Text Available Diabetic Retinopathy is a complication of Diabetes Melitus. It can be a blindness if untreated settled as early as possible. System created in this thesis is the detection of diabetic retinopathy level of the image obtained from fundus photographs. There are three main steps to resolve the problems, preprocessing, feature extraction and classification. Preprocessing methods that used in this system are Grayscale Green Channel, Gaussian Filter, Contrast Limited Adaptive Histogram Equalization and Masking. Two Dimensional Linear Discriminant Analysis (2DLDA is used for feature extraction. Support Vector Machine (SVM is used for classification. The test result performed by taking a dataset of MESSIDOR with number of images that vary for the training phase, otherwise is used for the testing phase. Test result show the optimal accuracy are 84% .   Keywords : Diabetic Retinopathy, Support Vector Machine, Two Dimensional Linear Discriminant Analysis, MESSIDOR

  1. Multivariate calibration with least-squares support vector machines.

    NARCIS (Netherlands)

    Thissen, U.M.J.; Ustun, B.; Melssen, W.J.; Buydens, L.M.C.

    2004-01-01

    This paper proposes the use of least-squares support vector machines (LS-SVMs) as a relatively new nonlinear multivariate calibration method, capable of dealing with ill-posed problems. LS-SVMs are an extension of "traditional" SVMs that have been introduced recently in the field of chemistry and

  2. Twin Support Vector Machine: A review from 2007 to 2014

    Directory of Open Access Journals (Sweden)

    Divya Tomar

    2015-03-01

    Full Text Available Twin Support Vector Machine (TWSVM is an emerging machine learning method suitable for both classification and regression problems. It utilizes the concept of Generalized Eigen-values Proximal Support Vector Machine (GEPSVM and finds two non-parallel planes for each class by solving a pair of Quadratic Programming Problems. It enhances the computational speed as compared to the traditional Support Vector Machine (SVM. TWSVM was initially constructed to solve binary classification problems; later researchers successfully extended it for multi-class problem domain. TWSVM always gives promising empirical results, due to which it has many attractive features which enhance its applicability. This paper presents the research development of TWSVM in recent years. This study is divided into two main broad categories - variant based and multi-class based TWSVM methods. The paper primarily discusses the basic concept of TWSVM and highlights its applications in recent years. A comparative analysis of various research contributions based on TWSVM is also presented. This is helpful for researchers to effectively utilize the TWSVM as an emergent research methodology and encourage them to work further in the performance enhancement of TWSVM.

  3. Supporting the Emotional Labour Associated with Teaching: Considering a Pluralistic Approach to Group Supervision

    Science.gov (United States)

    Hanley, Terry

    2017-01-01

    This paper puts forward a framework for supervising teaching staff whose roles involve supporting the emotional well-being of young people and young adults. Initially, the increasing focus upon the interface between education and health is outlined and the potential for this "emotional labour" to cause distress to those in helping roles…

  4. Collapse moment estimation by support vector machines for wall-thinned pipe bends and elbows

    International Nuclear Information System (INIS)

    Na, Man Gyun; Kim, Jin Weon; Hwang, In Joon

    2007-01-01

    The collapse moment due to wall-thinned defects is estimated through support vector machines with parameters optimized by a genetic algorithm. The support vector regression models are developed and applied to numerical data obtained from the finite element analysis for wall-thinned defects in piping systems. The support vector regression models are optimized by using both the data sets (training data and optimization data) prepared for training and optimization, and its performance verification is performed by using another data set (test data) different from the training data and the optimization data. In this work, three support vector regression models are developed, respectively, for three data sets divided into the three classes of extrados, intrados, and crown defects, which is because they have different characteristics. The relative root mean square (RMS) errors of the estimated collapse moment are 0.2333% for the training data, 0.5229% for the optimization data and 0.5011% for the test data. It is known from this result that the support vector regression models are sufficiently accurate to be used in the integrity evaluation of wall-thinned pipe bends and elbows

  5. Supervision Experiences of Professional Counselors Providing Crisis Counseling

    Science.gov (United States)

    Dupre, Madeleine; Echterling, Lennis G.; Meixner, Cara; Anderson, Robin; Kielty, Michele

    2014-01-01

    In this phenomenological study, the authors explored supervision experiences of 13 licensed professional counselors in situations requiring crisis counseling. Five themes concerning crisis and supervision were identified from individual interviews. Findings support intensive, immediate crisis supervision and postlicensure clinical supervision.

  6. Infinite ensemble of support vector machines for prediction of ...

    African Journals Online (AJOL)

    user

    the support vector machines (SVMs), a machine learning algorithm used ... work designs so that specific, quantitative workplace assessments can be made ... with SVMs can be obtained by embedding the base learners (hypothesis) into a.

  7. Support vector machine for the diagnosis of malignant mesothelioma

    Science.gov (United States)

    Ushasukhanya, S.; Nithyakalyani, A.; Sivakumar, V.

    2018-04-01

    Harmful mesothelioma is an illness in which threatening (malignancy) cells shape in the covering of the trunk or stomach area. Being presented to asbestos can influence the danger of threatening mesothelioma. Signs and side effects of threatening mesothelioma incorporate shortness of breath and agony under the rib confine. Tests that inspect within the trunk and belly are utilized to recognize (find) and analyse harmful mesothelioma. Certain elements influence forecast (shot of recuperation) and treatment choices. In this review, Support vector machine (SVM) classifiers were utilized for Mesothelioma sickness conclusion. SVM output is contrasted by concentrating on Mesothelioma’s sickness and findings by utilizing similar information set. The support vector machine algorithm gives 92.5% precision acquired by means of 3-overlap cross-approval. The Mesothelioma illness dataset were taken from an organization reports from Turkey.

  8. Chord Recognition Based on Temporal Correlation Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Zhongyang Rao

    2016-05-01

    Full Text Available In this paper, we propose a method called temporal correlation support vector machine (TCSVM for automatic major-minor chord recognition in audio music. We first use robust principal component analysis to separate the singing voice from the music to reduce the influence of the singing voice and consider the temporal correlations of the chord features. Using robust principal component analysis, we expect the low-rank component of the spectrogram matrix to contain the musical accompaniment and the sparse component to contain the vocal signals. Then, we extract a new logarithmic pitch class profile (LPCP feature called enhanced LPCP from the low-rank part. To exploit the temporal correlation among the LPCP features of chords, we propose an improved support vector machine algorithm called TCSVM. We perform this study using the MIREX’09 (Music Information Retrieval Evaluation eXchange Audio Chord Estimation dataset. Furthermore, we conduct comprehensive experiments using different pitch class profile feature vectors to examine the performance of TCSVM. The results of our method are comparable to the state-of-the-art methods that entered the MIREX in 2013 and 2014 for the MIREX’09 Audio Chord Estimation task dataset.

  9. How supportive supervision influences immunization session site practices: a quasi-experimental study in Odisha, India

    Directory of Open Access Journals (Sweden)

    Bhuputra Panda

    2015-01-01

    Full Text Available Background: Routine immunization (RI is a key child survival intervention. Ensuring acceptable standards of RI service delivery is critical for optimal outcomes. Accumulated evidences suggest that ‘supportive supervision’ improves the quality of health care services in general. During 2009–2010, the Government of Odisha and UNICEF jointly piloted this strategy in four districts to improve RI program outcomes. The present study aims to assess the effect of this strategy on improvement of skills and practices at immunization session sites. Design: A quasi-experimental ‘post-test only’ study design was adopted to compare the opinion and practices of frontline health workers and their supervisors in four intervention districts (IDs with two control districts (CDs. Altogether, we interviewed 111 supervisor–supervisee (health worker pairs using semi-structured interview schedules and case vignettes. We also directly observed health workers’ practices during immunization sessions at 111 sites. Data were analyzed with SPSS version 16.0. Results: The mean knowledge score of supervisors in CDs was significantly higher than in intervention groups. Variegated responses were obtained on case vignettes. The control group performed better in solving certain hypothetically asked problems, whereas the intervention group scored better in others. Health workers in IDs gave a lower rating to their respective supervisors’ knowledge, skill, and frequency of supervision. Logistics and vaccine availability were better in CDs. Conclusion: Notwithstanding other limitations, supportive supervision may not have independent effects on improving the quality of immunization services. Addressing systemic issues, such as the availability of essential logistics, supply chain management, timely indenting, and financial resources, could complement the supportive supervision strategy in improving immunization service delivery.

  10. [Possibilities of supervision in medical practice].

    Science.gov (United States)

    Lönnqvist, Jouko

    2014-01-01

    In supervision, a doctor examines in interaction with the supervisor her/his work, work role and collaborative relationships with the aim to develop herself/himself and the associated work community. In clinical supervision, a doctor's way of acting in interactive relationships with the patients is examined through patient cases, based on the doctor's own experience. Supervision can be used to strengthen the physician identity, clarify the work role, assimilate and delve into clinical work, support professional development and working career, manage one's own work and coping at work, develop collaboration and team work, and support the work of medical directors.

  11. Supporting Placement Supervision in Clinical Exercise Physiology

    Science.gov (United States)

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

    2015-01-01

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

  12. 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

  13. Density Based Support Vector Machines for Classification

    OpenAIRE

    Zahra Nazari; Dongshik Kang

    2015-01-01

    Support Vector Machines (SVM) is the most successful algorithm for classification problems. SVM learns the decision boundary from two classes (for Binary Classification) of training points. However, sometimes there are some less meaningful samples amongst training points, which are corrupted by noises or misplaced in wrong side, called outliers. These outliers are affecting on margin and classification performance, and machine should better to discard them. SVM as a popular and widely used cl...

  14. Support Vector Machines: Relevance Feedback and Information Retrieval.

    Science.gov (United States)

    Drucker, Harris; Shahrary, Behzad; Gibbon, David C.

    2002-01-01

    Compares support vector machines (SVMs) to Rocchio, Ide regular and Ide dec-hi algorithms in information retrieval (IR) of text documents using relevancy feedback. If the preliminary search is so poor that one has to search through many documents to find at least one relevant document, then SVM is preferred. Includes nine tables. (Contains 24…

  15. Radio-ecological characterization and radiological assessment in support of regulatory supervision of legacy sites in northwest Russia

    International Nuclear Information System (INIS)

    Sneve, M.K.; Kiselev, M.; Shandala, N.K.

    2014-01-01

    The Norwegian Radiation Protection Authority has been implementing a regulatory cooperation program in the Russian Federation for over 10 years, as part of the Norwegian government's Plan of Action for enhancing nuclear and radiation safety in northwest Russia. The overall long-term objective has been the enhancement of safety culture and includes a special focus on regulatory supervision of nuclear legacy sites. The initial project outputs included appropriate regulatory threat assessments, to determine the hazardous situations and activities which are most in need of enhanced regulatory supervision. In turn, this has led to the development of new and updated norms and standards, and related regulatory procedures, necessary to address the often abnormal conditions at legacy sites. This paper presents the experience gained within the above program with regard to radio-ecological characterization of Sites of Temporary Storage for spent nuclear fuel and radioactive waste at Andreeva Bay and Gremikha in the Kola Peninsula in northwest Russia. Such characterization is necessary to support assessments of the current radiological situation and to support prospective assessments of its evolution. Both types of assessments contribute to regulatory supervision of the sites. Accordingly, they include assessments to support development of regulatory standards and guidance concerning: control of radiation exposures to workers during remediation operations; emergency preparedness and response; planned radionuclide releases to the environment; development of site restoration plans, and waste treatment and disposal. Examples of characterization work are presented which relate to terrestrial and marine environments at Andreeva Bay. The use of this data in assessments is illustrated by means of the visualization and assessment tool (DATAMAP) developed as part of the regulatory cooperation program, specifically to help control radiation exposure in operations and to support

  16. Applications of Support Vector Machine (SVM) Learning in Cancer Genomics.

    Science.gov (United States)

    Huang, Shujun; Cai, Nianguang; Pacheco, Pedro Penzuti; Narrandes, Shavira; Wang, Yang; Xu, Wayne

    2018-01-01

    Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that has been used for cancer genomic classification or subtyping. Today, as advancements in high-throughput technologies lead to production of large amounts of genomic and epigenomic data, the classification feature of SVMs is expanding its use in cancer genomics, leading to the discovery of new biomarkers, new drug targets, and a better understanding of cancer driver genes. Herein we reviewed the recent progress of SVMs in cancer genomic studies. We intend to comprehend the strength of the SVM learning and its future perspective in cancer genomic applications. Copyright© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

  17. Multiscale asymmetric orthogonal wavelet kernel for linear programming support vector learning and nonlinear dynamic systems identification.

    Science.gov (United States)

    Lu, Zhao; Sun, Jing; Butts, Kenneth

    2014-05-01

    Support vector regression for approximating nonlinear dynamic systems is more delicate than the approximation of indicator functions in support vector classification, particularly for systems that involve multitudes of time scales in their sampled data. The kernel used for support vector learning determines the class of functions from which a support vector machine can draw its solution, and the choice of kernel significantly influences the performance of a support vector machine. In this paper, to bridge the gap between wavelet multiresolution analysis and kernel learning, the closed-form orthogonal wavelet is exploited to construct new multiscale asymmetric orthogonal wavelet kernels for linear programming support vector learning. The closed-form multiscale orthogonal wavelet kernel provides a systematic framework to implement multiscale kernel learning via dyadic dilations and also enables us to represent complex nonlinear dynamics effectively. To demonstrate the superiority of the proposed multiscale wavelet kernel in identifying complex nonlinear dynamic systems, two case studies are presented that aim at building parallel models on benchmark datasets. The development of parallel models that address the long-term/mid-term prediction issue is more intricate and challenging than the identification of series-parallel models where only one-step ahead prediction is required. Simulation results illustrate the effectiveness of the proposed multiscale kernel learning.

  18. A vector machine formulation with application to the computer-aided diagnosis of breast cancer from DCE-MRI screening examinations.

    Science.gov (United States)

    Levman, Jacob E D; Warner, Ellen; Causer, Petrina; Martel, Anne L

    2014-02-01

    This study investigates the use of a proposed vector machine formulation with application to dynamic contrast-enhanced magnetic resonance imaging examinations in the context of the computer-aided diagnosis of breast cancer. This paper describes a method for generating feature measurements that characterize a lesion's vascular heterogeneity as well as a supervised learning formulation that represents an improvement over the conventional support vector machine in this application. Spatially varying signal-intensity measures were extracted from the examinations using principal components analysis and the machine learning technique known as the support vector machine (SVM) was used to classify the results. An alternative vector machine formulation was found to improve on the results produced by the established SVM in randomized bootstrap validation trials, yielding a receiver-operating characteristic curve area of 0.82 which represents a statistically significant improvement over the SVM technique in this application.

  19. Voxel-Based Neighborhood for Spatial Shape Pattern Classification of Lidar Point Clouds with Supervised Learning

    Directory of Open Access Journals (Sweden)

    Victoria Plaza-Leiva

    2017-03-01

    Full Text Available Improving the effectiveness of spatial shape features classification from 3D lidar data is very relevant because it is largely used as a fundamental step towards higher level scene understanding challenges of autonomous vehicles and terrestrial robots. In this sense, computing neighborhood for points in dense scans becomes a costly process for both training and classification. This paper proposes a new general framework for implementing and comparing different supervised learning classifiers with a simple voxel-based neighborhood computation where points in each non-overlapping voxel in a regular grid are assigned to the same class by considering features within a support region defined by the voxel itself. The contribution provides offline training and online classification procedures as well as five alternative feature vector definitions based on principal component analysis for scatter, tubular and planar shapes. Moreover, the feasibility of this approach is evaluated by implementing a neural network (NN method previously proposed by the authors as well as three other supervised learning classifiers found in scene processing methods: support vector machines (SVM, Gaussian processes (GP, and Gaussian mixture models (GMM. A comparative performance analysis is presented using real point clouds from both natural and urban environments and two different 3D rangefinders (a tilting Hokuyo UTM-30LX and a Riegl. Classification performance metrics and processing time measurements confirm the benefits of the NN classifier and the feasibility of voxel-based neighborhood.

  20. Voxel-Based Neighborhood for Spatial Shape Pattern Classification of Lidar Point Clouds with Supervised Learning.

    Science.gov (United States)

    Plaza-Leiva, Victoria; Gomez-Ruiz, Jose Antonio; Mandow, Anthony; García-Cerezo, Alfonso

    2017-03-15

    Improving the effectiveness of spatial shape features classification from 3D lidar data is very relevant because it is largely used as a fundamental step towards higher level scene understanding challenges of autonomous vehicles and terrestrial robots. In this sense, computing neighborhood for points in dense scans becomes a costly process for both training and classification. This paper proposes a new general framework for implementing and comparing different supervised learning classifiers with a simple voxel-based neighborhood computation where points in each non-overlapping voxel in a regular grid are assigned to the same class by considering features within a support region defined by the voxel itself. The contribution provides offline training and online classification procedures as well as five alternative feature vector definitions based on principal component analysis for scatter, tubular and planar shapes. Moreover, the feasibility of this approach is evaluated by implementing a neural network (NN) method previously proposed by the authors as well as three other supervised learning classifiers found in scene processing methods: support vector machines (SVM), Gaussian processes (GP), and Gaussian mixture models (GMM). A comparative performance analysis is presented using real point clouds from both natural and urban environments and two different 3D rangefinders (a tilting Hokuyo UTM-30LX and a Riegl). Classification performance metrics and processing time measurements confirm the benefits of the NN classifier and the feasibility of voxel-based neighborhood.

  1. Support Vector Machines for decision support in electricity markets׳ strategic bidding

    DEFF Research Database (Denmark)

    Pinto, Tiago; Sousa, Tiago M.; Praça, Isabel

    2015-01-01

    . The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. This paper presents the application of a Support Vector Machines (SVM) based approach to provide decision support to electricity market players. This strategy is tested and validated...... by being included in ALBidS and then compared with the application of an Artificial Neural Network (ANN), originating promising results: an effective electricity market price forecast in a fast execution time. The proposed approach is tested and validated using real electricity markets data from MIBEL......׳ research group has developed a multi-agent system: Multi-Agent System for Competitive Electricity Markets (MASCEM), which simulates the electricity markets environment. MASCEM is integrated with Adaptive Learning Strategic Bidding System (ALBidS) that works as a decision support system for market players...

  2. Human Supervision of Multiple Autonomous Vehicles

    Science.gov (United States)

    2013-03-22

    AFRL-RH-WP-TR-2013-0143 HUMAN SUPERVISION OF MULTIPLE AUTONOMOUS VEHICLES Heath A. Ruff Ball...REPORT TYPE Interim 3. DATES COVERED (From – To) 09-16-08 – 03-22-13 4. TITLE AND SUBTITLE HUMAN SUPERVISION OF MULTIPLE AUTONOMOUS VEHICLES 5a...Supervision of Multiple Autonomous Vehicles To support the vision of a system that enables a single operator to control multiple next-generation

  3. Embedding systematic quality assessments in supportive supervision at primary healthcare level: application of an electronic Tool to Improve Quality of Healthcare in Tanzania

    NARCIS (Netherlands)

    Mboya, Dominick; Mshana, Christopher; Kessy, Flora; Alba, Sandra; Lengeler, Christian; Renggli, Sabine; Vander Plaetse, Bart; Mohamed, Mohamed A.; Schulze, Alexander

    2016-01-01

    Assessing quality of health services, for example through supportive supervision, is essential for strengthening healthcare delivery. Most systematic health facility assessment mechanisms, however, are not suitable for routine supervision. The objective of this study is to describe a quality

  4. Automated lesion detection on MRI scans using combined unsupervised and supervised methods

    International Nuclear Information System (INIS)

    Guo, Dazhou; Fridriksson, Julius; Fillmore, Paul; Rorden, Christopher; Yu, Hongkai; Zheng, Kang; Wang, Song

    2015-01-01

    Accurate and precise detection of brain lesions on MR images (MRI) is paramount for accurately relating lesion location to impaired behavior. In this paper, we present a novel method to automatically detect brain lesions from a T1-weighted 3D MRI. The proposed method combines the advantages of both unsupervised and supervised methods. First, unsupervised methods perform a unified segmentation normalization to warp images from the native space into a standard space and to generate probability maps for different tissue types, e.g., gray matter, white matter and fluid. This allows us to construct an initial lesion probability map by comparing the normalized MRI to healthy control subjects. Then, we perform non-rigid and reversible atlas-based registration to refine the probability maps of gray matter, white matter, external CSF, ventricle, and lesions. These probability maps are combined with the normalized MRI to construct three types of features, with which we use supervised methods to train three support vector machine (SVM) classifiers for a combined classifier. Finally, the combined classifier is used to accomplish lesion detection. We tested this method using T1-weighted MRIs from 60 in-house stroke patients. Using leave-one-out cross validation, the proposed method can achieve an average Dice coefficient of 73.1 % when compared to lesion maps hand-delineated by trained neurologists. Furthermore, we tested the proposed method on the T1-weighted MRIs in the MICCAI BRATS 2012 dataset. The proposed method can achieve an average Dice coefficient of 66.5 % in comparison to the expert annotated tumor maps provided in MICCAI BRATS 2012 dataset. In addition, on these two test datasets, the proposed method shows competitive performance to three state-of-the-art methods, including Stamatakis et al., Seghier et al., and Sanjuan et al. In this paper, we introduced a novel automated procedure for lesion detection from T1-weighted MRIs by combining both an unsupervised and a

  5. Automated novelty detection in the WISE survey with one-class support vector machines

    Science.gov (United States)

    Solarz, A.; Bilicki, M.; Gromadzki, M.; Pollo, A.; Durkalec, A.; Wypych, M.

    2017-10-01

    Wide-angle photometric surveys of previously uncharted sky areas or wavelength regimes will always bring in unexpected sources - novelties or even anomalies - whose existence and properties cannot be easily predicted from earlier observations. Such objects can be efficiently located with novelty detection algorithms. Here we present an application of such a method, called one-class support vector machines (OCSVM), to search for anomalous patterns among sources preselected from the mid-infrared AllWISE catalogue covering the whole sky. To create a model of expected data we train the algorithm on a set of objects with spectroscopic identifications from the SDSS DR13 database, present also in AllWISE. The OCSVM method detects as anomalous those sources whose patterns - WISE photometric measurements in this case - are inconsistent with the model. Among the detected anomalies we find artefacts, such as objects with spurious photometry due to blending, but more importantly also real sources of genuine astrophysical interest. Among the latter, OCSVM has identified a sample of heavily reddened AGN/quasar candidates distributed uniformly over the sky and in a large part absent from other WISE-based AGN catalogues. It also allowed us to find a specific group of sources of mixed types, mostly stars and compact galaxies. By combining the semi-supervised OCSVM algorithm with standard classification methods it will be possible to improve the latter by accounting for sources which are not present in the training sample, but are otherwise well-represented in the target set. Anomaly detection adds flexibility to automated source separation procedures and helps verify the reliability and representativeness of the training samples. It should be thus considered as an essential step in supervised classification schemes to ensure completeness and purity of produced catalogues. The catalogues of outlier data are only available at the CDS via anonymous ftp to http

  6. Predicting post-translational lysine acetylation using support vector machines

    DEFF Research Database (Denmark)

    Gnad, Florian; Ren, Shubin; Choudhary, Chunaram

    2010-01-01

    spectrometry to identify 3600 lysine acetylation sites on 1750 human proteins covering most of the previously annotated sites and providing the most comprehensive acetylome so far. This dataset should provide an excellent source to train support vector machines (SVMs) allowing the high accuracy in silico...

  7. Identifying saltcedar with hyperspectral data and support vector machines

    Science.gov (United States)

    Saltcedar (Tamarix spp.) are a group of dense phreatophytic shrubs and trees that are invasive to riparian areas throughout the United States. This study determined the feasibility of using hyperspectral data and a support vector machine (SVM) classifier to discriminate saltcedar from other cover t...

  8. Optimized support vector regression for drilling rate of penetration estimation

    Science.gov (United States)

    Bodaghi, Asadollah; Ansari, Hamid Reza; Gholami, Mahsa

    2015-12-01

    In the petroleum industry, drilling optimization involves the selection of operating conditions for achieving the desired depth with the minimum expenditure while requirements of personal safety, environment protection, adequate information of penetrated formations and productivity are fulfilled. Since drilling optimization is highly dependent on the rate of penetration (ROP), estimation of this parameter is of great importance during well planning. In this research, a novel approach called `optimized support vector regression' is employed for making a formulation between input variables and ROP. Algorithms used for optimizing the support vector regression are the genetic algorithm (GA) and the cuckoo search algorithm (CS). Optimization implementation improved the support vector regression performance by virtue of selecting proper values for its parameters. In order to evaluate the ability of optimization algorithms in enhancing SVR performance, their results were compared to the hybrid of pattern search and grid search (HPG) which is conventionally employed for optimizing SVR. The results demonstrated that the CS algorithm achieved further improvement on prediction accuracy of SVR compared to the GA and HPG as well. Moreover, the predictive model derived from back propagation neural network (BPNN), which is the traditional approach for estimating ROP, is selected for comparisons with CSSVR. The comparative results revealed the superiority of CSSVR. This study inferred that CSSVR is a viable option for precise estimation of ROP.

  9. Track Circuit Fault Diagnosis Method based on Least Squares Support Vector

    Science.gov (United States)

    Cao, Yan; Sun, Fengru

    2018-01-01

    In order to improve the troubleshooting efficiency and accuracy of the track circuit, track circuit fault diagnosis method was researched. Firstly, the least squares support vector machine was applied to design the multi-fault classifier of the track circuit, and then the measured track data as training samples was used to verify the feasibility of the methods. Finally, the results based on BP neural network fault diagnosis methods and the methods used in this paper were compared. Results shows that the track fault classifier based on least squares support vector machine can effectively achieve the five track circuit fault diagnosis with less computing time.

  10. Genetic classification of populations using supervised learning.

    Directory of Open Access Journals (Sweden)

    Michael Bridges

    2011-05-01

    Full Text Available 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.

  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. Framing doctoral supervision as formative assessment

    DEFF Research Database (Denmark)

    Kobayashi, Sofie

    Doctoral supervision has been described through a number of models useful for understanding different aspects of supervision. None of these are all-encompassing, but each emphasizes a particular perspective, like the relationship, personal vs. structural support, process vs. product orientation. ...

  13. Infinite ensemble of support vector machines for prediction of ...

    African Journals Online (AJOL)

    Many researchers have demonstrated the use of artificial neural networks (ANNs) to predict musculoskeletal disorders risk associated with occupational exposures. In order to improve the accuracy of LBDs risk classification, this paper proposes to use the support vector machines (SVMs), a machine learning algorithm used ...

  14. Fuzzy-based multi-kernel spherical support vector machine for ...

    Indian Academy of Sciences (India)

    In the proposed classifier, we design a new multi-kernel function based on the fuzzy triangular membership function. Finally, a newly developed multi-kernel function is incorporated into the spherical support vector machine to enhance the performance significantly. The experimental results are evaluated and performance is ...

  15. Parameter Selection Method for Support Vector Regression Based on Adaptive Fusion of the Mixed Kernel Function

    Directory of Open Access Journals (Sweden)

    Hailun Wang

    2017-01-01

    Full Text Available Support vector regression algorithm is widely used in fault diagnosis of rolling bearing. A new model parameter selection method for support vector regression based on adaptive fusion of the mixed kernel function is proposed in this paper. We choose the mixed kernel function as the kernel function of support vector regression. The mixed kernel function of the fusion coefficients, kernel function parameters, and regression parameters are combined together as the parameters of the state vector. Thus, the model selection problem is transformed into a nonlinear system state estimation problem. We use a 5th-degree cubature Kalman filter to estimate the parameters. In this way, we realize the adaptive selection of mixed kernel function weighted coefficients and the kernel parameters, the regression parameters. Compared with a single kernel function, unscented Kalman filter (UKF support vector regression algorithms, and genetic algorithms, the decision regression function obtained by the proposed method has better generalization ability and higher prediction accuracy.

  16. Support Vector Machine Classification of Drunk Driving Behaviour.

    Science.gov (United States)

    Chen, Huiqin; Chen, Lei

    2017-01-23

    Alcohol is the root cause of numerous traffic accidents due to its pharmacological action on the human central nervous system. This study conducted a detection process to distinguish drunk driving from normal driving under simulated driving conditions. The classification was performed by a support vector machine (SVM) classifier trained to distinguish between these two classes by integrating both driving performance and physiological measurements. In addition, principal component analysis was conducted to rank the weights of the features. The standard deviation of R-R intervals (SDNN), the root mean square value of the difference of the adjacent R-R interval series (RMSSD), low frequency (LF), high frequency (HF), the ratio of the low and high frequencies (LF/HF), and average blink duration were the highest weighted features in the study. The results show that SVM classification can successfully distinguish drunk driving from normal driving with an accuracy of 70%. The driving performance data and the physiological measurements reported by this paper combined with air-alcohol concentration could be integrated using the support vector regression classification method to establish a better early warning model, thereby improving vehicle safety.

  17. Support Vector Machine Classification of Drunk Driving Behaviour

    Directory of Open Access Journals (Sweden)

    Huiqin Chen

    2017-01-01

    Full Text Available Alcohol is the root cause of numerous traffic accidents due to its pharmacological action on the human central nervous system. This study conducted a detection process to distinguish drunk driving from normal driving under simulated driving conditions. The classification was performed by a support vector machine (SVM classifier trained to distinguish between these two classes by integrating both driving performance and physiological measurements. In addition, principal component analysis was conducted to rank the weights of the features. The standard deviation of R–R intervals (SDNN, the root mean square value of the difference of the adjacent R–R interval series (RMSSD, low frequency (LF, high frequency (HF, the ratio of the low and high frequencies (LF/HF, and average blink duration were the highest weighted features in the study. The results show that SVM classification can successfully distinguish drunk driving from normal driving with an accuracy of 70%. The driving performance data and the physiological measurements reported by this paper combined with air-alcohol concentration could be integrated using the support vector regression classification method to establish a better early warning model, thereby improving vehicle safety.

  18. DC Algorithm for Extended Robust Support Vector Machine.

    Science.gov (United States)

    Fujiwara, Shuhei; Takeda, Akiko; Kanamori, Takafumi

    2017-05-01

    Nonconvex variants of support vector machines (SVMs) have been developed for various purposes. For example, robust SVMs attain robustness to outliers by using a nonconvex loss function, while extended [Formula: see text]-SVM (E[Formula: see text]-SVM) extends the range of the hyperparameter by introducing a nonconvex constraint. Here, we consider an extended robust support vector machine (ER-SVM), a robust variant of E[Formula: see text]-SVM. ER-SVM combines two types of nonconvexity from robust SVMs and E[Formula: see text]-SVM. Because of the two nonconvexities, the existing algorithm we proposed needs to be divided into two parts depending on whether the hyperparameter value is in the extended range or not. The algorithm also heuristically solves the nonconvex problem in the extended range. In this letter, we propose a new, efficient algorithm for ER-SVM. The algorithm deals with two types of nonconvexity while never entailing more computations than either E[Formula: see text]-SVM or robust SVM, and it finds a critical point of ER-SVM. Furthermore, we show that ER-SVM includes the existing robust SVMs as special cases. Numerical experiments confirm the effectiveness of integrating the two nonconvexities.

  19. Support vector machines classifiers of physical activities in preschoolers

    Science.gov (United States)

    The goal of this study is to develop, test, and compare multinomial logistic regression (MLR) and support vector machines (SVM) in classifying preschool-aged children physical activity data acquired from an accelerometer. In this study, 69 children aged 3-5 years old were asked to participate in a s...

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

    KAUST Repository

    Wang, Jim Jing-Yan

    2014-05-23

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

  1. Guidelines for the training, credentialing, use, and supervision of speech-language pathology assistants. Task Force on Support Personnel.

    Science.gov (United States)

    1996-01-01

    These guidelines are an official statement of the American Speech-Language-Hearing Association. They provide guidance on the training, credentialing, use, and supervision of one category of support personnel in speech-language pathology: speech-language pathology assistants. Guidelines are not official standards of the Association. They were developed by the Task Force on Support Personnel: Dennis J. Arnst, Kenneth D. Barker, Ann Olsen Bird, Sheila Bridges, Linda S. DeYoung, Katherine Formichella, Nena M. Germany, Gilbert C. Hanke, Ann M. Horton, DeAnne M. Owre, Sidney L. Ramsey, Cathy A. Runnels, Brenda Terrell, Gerry W. Werven, Denise West, Patricia A. Mercaitis (consultant), Lisa C. O'Connor (consultant), Frederick T. Spahr (coordinator), Diane Paul-Brown (associate coordinator), Ann L. Carey (Executive Board liaison). The 1994 guidelines supersede the 1981 guidelines entitled, "Guidelines for the Employment and Utilization of Supportive Personnel" (Asha, March 1981, 165-169). Refer to the 1995 position statement on the "Training, Credentialing, Use, and Supervision of Support Personnel in Speech-Language Pathology" (Asha, 37 [Suppl. 14], 21).

  2. Implicit Social Trust Dan Support Vector Regression Untuk Sistem Rekomendasi Berita

    Directory of Open Access Journals (Sweden)

    Melita Widya Ningrum

    2018-01-01

    Full Text Available Situs berita merupakan salah satu situs yang sering diakses masyarakat karena kemampuannya dalam menyajikan informasi terkini dari berbagai topik seperti olahraga, bisnis, politik, teknologi, kesehatan dan hiburan. Masyarakat dapat mencari dan melihat berita yang sedang populer dari seluruh dunia. Di sisi lain, melimpahnya artikel berita yang tersedia dapat menyulitkan pengguna dalam menemukan artikel berita yang sesuai dengan ketertarikannya. Pemilihan artikel berita yang ditampilkan ke halaman utama pengguna menjadi penting karena dapat meningkatkan minat pengguna untuk membaca artikel berita dari situs tersebut. Selain itu, pemilihan artikel berita yang sesuai dapat meminimalisir terjadinya banjir informasi yang tidak relevan. Dalam pemilihan artikel berita dibutuhkan sistem rekomendasi yang memiliki pengetahuan mengenai ketertarikan atau relevansi pengguna akan topik berita tertentu. Pada penelitian ini, peneliti membuat sistem rekomendasi artikel berita pada New York Times berbasis implicit social trust. Social trust dihasilkan dari interaksi antara pengguna dengan teman-temannya  dan bobot kepercayaan teman pengguna pada media sosial Twitter. Data yang diambil merupakan data pengguna Twitter, teman dan jumlah interaksi antar pengguna berupa retweet. Sistem ini memanfaatkan algoritma Support Vector Regression untuk memberikan estimasi penilaian pengguna terhadap suatu topik tertentu. Hasil pengolahan data dengan Support Vector Regression menunjukkan tingkat akurasi dengan MAPE sebesar 0,8243075902233644%.   Keywords : Twitter, Rekomendasi Berita, Social Trust, Support Vector Regression

  3. A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification

    Directory of Open Access Journals (Sweden)

    Wang Lily

    2008-07-01

    Full Text Available Abstract Background Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of gene expression microarray technology with several molecular signatures on their way toward clinical deployment. Use of the most accurate classification algorithms available for microarray gene expression data is a critical ingredient in order to develop the best possible molecular signatures for patient care. As suggested by a large body of literature to date, support vector machines can be considered "best of class" algorithms for classification of such data. Recent work, however, suggests that random forest classifiers may outperform support vector machines in this domain. Results In the present paper we identify methodological biases of prior work comparing random forests and support vector machines and conduct a new rigorous evaluation of the two algorithms that corrects these limitations. Our experiments use 22 diagnostic and prognostic datasets and show that support vector machines outperform random forests, often by a large margin. Our data also underlines the importance of sound research design in benchmarking and comparison of bioinformatics algorithms. Conclusion We found that both on average and in the majority of microarray datasets, random forests are outperformed by support vector machines both in the settings when no gene selection is performed and when several popular gene selection methods are used.

  4. Supervised machine learning and active learning in classification of radiology reports.

    Science.gov (United States)

    Nguyen, Dung H M; Patrick, Jon D

    2014-01-01

    This paper presents an automated system for classifying the results of imaging examinations (CT, MRI, positron emission tomography) into reportable and non-reportable cancer cases. This system is part of an industrial-strength processing pipeline built to extract content from radiology reports for use in the Victorian Cancer Registry. In addition to traditional supervised learning methods such as conditional random fields and support vector machines, active learning (AL) approaches were investigated to optimize training production and further improve classification performance. The project involved two pilot sites in Victoria, Australia (Lake Imaging (Ballarat) and Peter MacCallum Cancer Centre (Melbourne)) and, in collaboration with the NSW Central Registry, one pilot site at Westmead Hospital (Sydney). The reportability classifier performance achieved 98.25% sensitivity and 96.14% specificity on the cancer registry's held-out test set. Up to 92% of training data needed for supervised machine learning can be saved by AL. AL is a promising method for optimizing the supervised training production used in classification of radiology reports. When an AL strategy is applied during the data selection process, the cost of manual classification can be reduced significantly. The most important practical application of the reportability classifier is that it can dramatically reduce human effort in identifying relevant reports from the large imaging pool for further investigation of cancer. The classifier is built on a large real-world dataset and can achieve high performance in filtering relevant reports to support cancer registries. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  5. The impact of a human resource management intervention on the capacity of supervisors to support and supervise their staff at health facility level.

    Science.gov (United States)

    Uduma, Ogenna; Galligan, Marie; Mollel, Henry; Masanja, Honorati; Bradley, Susan; McAuliffe, Eilish

    2017-08-30

    A systematic and structured approach to the support and supervision of health workers can strengthen the human resource management function at the district and health facility levels and may help address the current crisis in human resources for health in sub-Saharan Africa by improving health workers' motivation and retention. A supportive supervision programme including (a) a workshop, (b) intensive training and (c) action learning sets was designed to improve human resource management in districts and health facilities in Tanzania. We conducted a randomised experimental design to evaluate the impact of the intervention. Data on the same measures were collected pre and post the intervention in order to identify any changes that occurred (between baseline and end of project) in the capacity of supervisors in intervention a + b and intervention a + b + c to support and supervise their staff. These were compared to supervisors in a control group in each of Tanga, Iringa and Tabora regions (n = 9). A quantitative survey of 95 and 108 supervisors and 196 and 187 health workers sampled at baseline and end-line, respectively, also contained open-ended responses which were analysed separately. Supervisors assessed their own competency levels pre- and post-intervention. End-line samples generally scored higher compared to the corresponding baseline in both intervention groups for competence activities. Significant differences between baseline and end-line were observed in the total scores on 'maintaining high levels of performance', 'dealing with performance problems', 'counselling a troubled employee' and 'time management' in intervention a + b. In contrast, for intervention a + b + c, a significant difference in distribution of scores was only found on 'counselling a troubled employee', although the end-line mean scores were higher than their corresponding baseline mean scores in all cases. Similar trends to those in the supervisors' reports are seen in

  6. Exploring paraprofessional and classroom factors affecting teacher supervision.

    Science.gov (United States)

    Irvin, Dwight W; Ingram, Paul; Huffman, Jonathan; Mason, Rose; Wills, Howard

    2018-02-01

    Paraprofessionals serve a primary role in supporting students with disabilities in the classroom, which necessitates teachers' supervision as a means to improve their practice. Yet, little is known regarding what factors affect teacher supervision. We sought to identify how paraprofessional competence and classroom type affected the levels of teacher direction. We administered an adapted version of the Paraprofessional Needs, Knowledge & Tasks Survey and the Survey for Teachers Supervising Paraprofessionals to teachers supervising paraprofessionals in elementary schools. Structural Equation Modeling was used to examine the link between paraprofessional competence and classroom factors affecting the level of teacher supervision. Our results indicated that when teachers perceived paraprofessionals as being more skilled, they provided more supervision, and when more supervision was provided the less they thought paraprofessionals should be doing their assigned tasks. Additionally, paraprofessionals working in classrooms with more students with mild disabilities received less supervision than paraprofessionals working in classrooms with more students with moderate-to-severe disabilities. Those paraprofessionals in classrooms serving mostly children with mild disabilities were also perceived as having lower levels of skill competence than those serving in classrooms with students with more moderate-to-severe disabilities. By understanding the factors that affect teacher supervision, policy and professional development opportunities can be refined/developed to better support both supervising teachers and paraprofessionals and, in turn, improve the outcomes of children with disabilities. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. A Multiagent-based Intrusion Detection System with the Support of Multi-Class Supervised Classification

    Science.gov (United States)

    Shyu, Mei-Ling; Sainani, Varsha

    The increasing number of network security related incidents have made it necessary for the organizations to actively protect their sensitive data with network intrusion detection systems (IDSs). IDSs are expected to analyze a large volume of data while not placing a significantly added load on the monitoring systems and networks. This requires good data mining strategies which take less time and give accurate results. In this study, a novel data mining assisted multiagent-based intrusion detection system (DMAS-IDS) is proposed, particularly with the support of multiclass supervised classification. These agents can detect and take predefined actions against malicious activities, and data mining techniques can help detect them. Our proposed DMAS-IDS shows superior performance compared to central sniffing IDS techniques, and saves network resources compared to other distributed IDS with mobile agents that activate too many sniffers causing bottlenecks in the network. This is one of the major motivations to use a distributed model based on multiagent platform along with a supervised classification technique.

  8. A Wavelet Support Vector Machine Combination Model for Singapore Tourist Arrival to Malaysia

    Science.gov (United States)

    Rafidah, A.; Shabri, Ani; Nurulhuda, A.; Suhaila, Y.

    2017-08-01

    In this study, wavelet support vector machine model (WSVM) is proposed and applied for monthly data Singapore tourist time series prediction. The WSVM model is combination between wavelet analysis and support vector machine (SVM). In this study, we have two parts, first part we compare between the kernel function and second part we compare between the developed models with single model, SVM. The result showed that kernel function linear better than RBF while WSVM outperform with single model SVM to forecast monthly Singapore tourist arrival to Malaysia.

  9. On Weighted Support Vector Regression

    DEFF Research Database (Denmark)

    Han, Xixuan; Clemmensen, Line Katrine Harder

    2014-01-01

    We propose a new type of weighted support vector regression (SVR), motivated by modeling local dependencies in time and space in prediction of house prices. The classic weights of the weighted SVR are added to the slack variables in the objective function (OF‐weights). This procedure directly...... shrinks the coefficient of each observation in the estimated functions; thus, it is widely used for minimizing influence of outliers. We propose to additionally add weights to the slack variables in the constraints (CF‐weights) and call the combination of weights the doubly weighted SVR. We illustrate...... the differences and similarities of the two types of weights by demonstrating the connection between the Least Absolute Shrinkage and Selection Operator (LASSO) and the SVR. We show that an SVR problem can be transformed to a LASSO problem plus a linear constraint and a box constraint. We demonstrate...

  10. Local Patch Vectors Encoded by Fisher Vectors for Image Classification

    Directory of Open Access Journals (Sweden)

    Shuangshuang Chen

    2018-02-01

    Full Text Available The objective of this work is image classification, whose purpose is to group images into corresponding semantic categories. Four contributions are made as follows: (i For computational simplicity and efficiency, we directly adopt raw image patch vectors as local descriptors encoded by Fisher vector (FV subsequently; (ii For obtaining representative local features within the FV encoding framework, we compare and analyze three typical sampling strategies: random sampling, saliency-based sampling and dense sampling; (iii In order to embed both global and local spatial information into local features, we construct an improved spatial geometry structure which shows good performance; (iv For reducing the storage and CPU costs of high dimensional vectors, we adopt a new feature selection method based on supervised mutual information (MI, which chooses features by an importance sorting algorithm. We report experimental results on dataset STL-10. It shows very promising performance with this simple and efficient framework compared to conventional methods.

  11. Deep neural mapping support vector machines.

    Science.gov (United States)

    Li, Yujian; Zhang, Ting

    2017-09-01

    The choice of kernel has an important effect on the performance of a support vector machine (SVM). The effect could be reduced by NEUROSVM, an architecture using multilayer perceptron for feature extraction and SVM for classification. In binary classification, a general linear kernel NEUROSVM can be theoretically simplified as an input layer, many hidden layers, and an SVM output layer. As a feature extractor, the sub-network composed of the input and hidden layers is first trained together with a virtual ordinary output layer by backpropagation, then with the output of its last hidden layer taken as input of the SVM classifier for further training separately. By taking the sub-network as a kernel mapping from the original input space into a feature space, we present a novel model, called deep neural mapping support vector machine (DNMSVM), from the viewpoint of deep learning. This model is also a new and general kernel learning method, where the kernel mapping is indeed an explicit function expressed as a sub-network, different from an implicit function induced by a kernel function traditionally. Moreover, we exploit a two-stage procedure of contrastive divergence learning and gradient descent for DNMSVM to jointly training an adaptive kernel mapping instead of a kernel function, without requirement of kernel tricks. As a whole of the sub-network and the SVM classifier, the joint training of DNMSVM is done by using gradient descent to optimize the objective function with the sub-network layer-wise pre-trained via contrastive divergence learning of restricted Boltzmann machines. Compared to the separate training of NEUROSVM, the joint training is a new algorithm for DNMSVM to have advantages over NEUROSVM. Experimental results show that DNMSVM can outperform NEUROSVM and RBFSVM (i.e., SVM with the kernel of radial basis function), demonstrating its effectiveness. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. Estimating transmitted waves of floating breakwater using support vector regression model

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; Hegde, A.V.; Kumar, V.; Patil, S.G.

    is first mapped onto an m-dimensional feature space using some fixed (nonlinear) mapping, and then a linear model is constructed in this feature space (Ivanciuc Ovidiu 2007). Using mathematical notation, the linear model in the feature space f(x, w... regressive vector machines, Ocean Engineering Journal, Vol – 36, pp 339 – 347, 2009. 3. Ivanciuc Ovidiu, Applications of support vector machines in chemistry, Review in Computational Chemistry, Eds K. B. Lipkouitz and T. R. Cundari, Vol – 23...

  13. General Dimensional Multiple-Output Support Vector Regressions and Their Multiple Kernel Learning.

    Science.gov (United States)

    Chung, Wooyong; Kim, Jisu; Lee, Heejin; Kim, Euntai

    2015-11-01

    Support vector regression has been considered as one of the most important regression or function approximation methodologies in a variety of fields. In this paper, two new general dimensional multiple output support vector regressions (MSVRs) named SOCPL1 and SOCPL2 are proposed. The proposed methods are formulated in the dual space and their relationship with the previous works is clearly investigated. Further, the proposed MSVRs are extended into the multiple kernel learning and their training is implemented by the off-the-shelf convex optimization tools. The proposed MSVRs are applied to benchmark problems and their performances are compared with those of the previous methods in the experimental section.

  14. Using Skype to support remote clinical supervision for health professionals delivering a sustained maternal early childhood programme: a phenomenographical study.

    Science.gov (United States)

    Bruce, Tracey; Byrne, Fiona; Kemp, Lynn

    2018-02-01

    Skype technology was implemented by the Australian Maternal Early Childhood Sustained Home-visiting (MECSH) Support Service as a tool for the remote provision of clinical supervision for clinicians working in the MECSH program in Seoul, South Korea. To gain a better understanding of the processes underpinning sustainable delivery of remote clinical supervision using digital technologies. A phenomenographical study. Recorded notes and reflections on each supervision session, noting exemplars and characteristics of the experience were read and re-read to derive the characterizations of the experience. The experience has provided learnings in three domains: (1) the processes in using Skype; (2) supervisory processes; and (3) language translation, including managing clarity of, and time for translation. Skype has potential for use in remote provision of clinical supervision, including where translation is required. Further research evaluating the benefit of telesupervision from supervisor and supervisee perspectives is necessary to determine if it is a sustainable process.

  15. Active damage detection method based on support vector machine and impulse response

    International Nuclear Information System (INIS)

    Taniguchi, Ryuta; Mita, Akira

    2004-01-01

    An active damage detection method was proposed to characterize damage in bolted joints. The purpose of this study is to propose a damage detection method that can obtain the detailed information of the damage by creating feature vectors for pattern recognition. In the proposed method, the wavelet transform is applied to the sensor signals, and the feature vectors are defined by second power average of the amplitude. The feature vectors generated by experiments were successfully used as the training data for Support Vector Machine (SVM). By applying the wavelet transform to time-frequency analysis, the accuracy of pattern recognition was raised in both correlation coefficient and SVM applications. Moreover, the SVM could identify the damage with very strong discernment capability than others. Applicability of the proposed method was successfully demonstrated. (author)

  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. 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.

  18. Modeling and prediction of flotation performance using support vector regression

    Directory of Open Access Journals (Sweden)

    Despotović Vladimir

    2017-01-01

    Full Text Available Continuous efforts have been made in recent year to improve the process of paper recycling, as it is of critical importance for saving the wood, water and energy resources. Flotation deinking is considered to be one of the key methods for separation of ink particles from the cellulose fibres. Attempts to model the flotation deinking process have often resulted in complex models that are difficult to implement and use. In this paper a model for prediction of flotation performance based on Support Vector Regression (SVR, is presented. Representative data samples were created in laboratory, under a variety of practical control variables for the flotation deinking process, including different reagents, pH values and flotation residence time. Predictive model was created that was trained on these data samples, and the flotation performance was assessed showing that Support Vector Regression is a promising method even when dataset used for training the model is limited.

  19. Supervised learning with decision margins in pools of spiking neurons.

    Science.gov (United States)

    Le Mouel, Charlotte; Harris, Kenneth D; Yger, Pierre

    2014-10-01

    Learning to categorise sensory inputs by generalising from a few examples whose category is precisely known is a crucial step for the brain to produce appropriate behavioural responses. At the neuronal level, this may be performed by adaptation of synaptic weights under the influence of a training signal, in order to group spiking patterns impinging on the neuron. Here we describe a framework that allows spiking neurons to perform such "supervised learning", using principles similar to the Support Vector Machine, a well-established and robust classifier. Using a hinge-loss error function, we show that requesting a margin similar to that of the SVM improves performance on linearly non-separable problems. Moreover, we show that using pools of neurons to discriminate categories can also increase the performance by sharing the load among neurons.

  20. Radio-ecological characterization and radiological assessment in support of regulatory supervision of legacy sites in northwest Russia.

    Science.gov (United States)

    Sneve, M K; Kiselev, M; Shandala, N K

    2014-05-01

    The Norwegian Radiation Protection Authority has been implementing a regulatory cooperation program in the Russian Federation for over 10 years, as part of the Norwegian government's Plan of Action for enhancing nuclear and radiation safety in northwest Russia. The overall long-term objective has been the enhancement of safety culture and includes a special focus on regulatory supervision of nuclear legacy sites. The initial project outputs included appropriate regulatory threat assessments, to determine the hazardous situations and activities which are most in need of enhanced regulatory supervision. In turn, this has led to the development of new and updated norms and standards, and related regulatory procedures, necessary to address the often abnormal conditions at legacy sites. This paper presents the experience gained within the above program with regard to radio-ecological characterization of Sites of Temporary Storage for spent nuclear fuel and radioactive waste at Andreeva Bay and Gremikha in the Kola Peninsula in northwest Russia. Such characterization is necessary to support assessments of the current radiological situation and to support prospective assessments of its evolution. Both types of assessments contribute to regulatory supervision of the sites. Accordingly, they include assessments to support development of regulatory standards and guidance concerning: control of radiation exposures to workers during remediation operations; emergency preparedness and response; planned radionuclide releases to the environment; development of site restoration plans, and waste treatment and disposal. Examples of characterization work are presented which relate to terrestrial and marine environments at Andreeva Bay. The use of this data in assessments is illustrated by means of the visualization and assessment tool (DATAMAP) developed as part of the regulatory cooperation program, specifically to help control radiation exposure in operations and to support

  1. Prediction of Spirometric Forced Expiratory Volume (FEV1) Data Using Support Vector Regression

    Science.gov (United States)

    Kavitha, A.; Sujatha, C. M.; Ramakrishnan, S.

    2010-01-01

    In this work, prediction of forced expiratory volume in 1 second (FEV1) in pulmonary function test is carried out using the spirometer and support vector regression analysis. Pulmonary function data are measured with flow volume spirometer from volunteers (N=175) using a standard data acquisition protocol. The acquired data are then used to predict FEV1. Support vector machines with polynomial kernel function with four different orders were employed to predict the values of FEV1. The performance is evaluated by computing the average prediction accuracy for normal and abnormal cases. Results show that support vector machines are capable of predicting FEV1 in both normal and abnormal cases and the average prediction accuracy for normal subjects was higher than that of abnormal subjects. Accuracy in prediction was found to be high for a regularization constant of C=10. Since FEV1 is the most significant parameter in the analysis of spirometric data, it appears that this method of assessment is useful in diagnosing the pulmonary abnormalities with incomplete data and data with poor recording.

  2. Quantum optimization for training support vector machines.

    Science.gov (United States)

    Anguita, Davide; Ridella, Sandro; Rivieccio, Fabio; Zunino, Rodolfo

    2003-01-01

    Refined concepts, such as Rademacher estimates of model complexity and nonlinear criteria for weighting empirical classification errors, represent recent and promising approaches to characterize the generalization ability of Support Vector Machines (SVMs). The advantages of those techniques lie in both improving the SVM representation ability and yielding tighter generalization bounds. On the other hand, they often make Quadratic-Programming algorithms no longer applicable, and SVM training cannot benefit from efficient, specialized optimization techniques. The paper considers the application of Quantum Computing to solve the problem of effective SVM training, especially in the case of digital implementations. The presented research compares the behavioral aspects of conventional and enhanced SVMs; experiments in both a synthetic and real-world problems support the theoretical analysis. At the same time, the related differences between Quadratic-Programming and Quantum-based optimization techniques are considered.

  3. Support-Vector-based Least Squares for learning non-linear dynamics

    NARCIS (Netherlands)

    de Kruif, B.J.; de Vries, Theodorus J.A.

    2002-01-01

    A function approximator is introduced that is based on least squares support vector machines (LSSVM) and on least squares (LS). The potential indicators for the LS method are chosen as the kernel functions of all the training samples similar to LSSVM. By selecting these as indicator functions the

  4. Comparison of ν-support vector regression and logistic equation for ...

    African Journals Online (AJOL)

    Due to the complexity and high non-linearity of bioprocess, most simple mathematical models fail to describe the exact behavior of biochemistry systems. As a novel type of learning method, support vector regression (SVR) owns the powerful capability to characterize problems via small sample, nonlinearity, high dimension ...

  5. Implementing a sustainable clinical supervision model for Isles nurses in Orkney.

    Science.gov (United States)

    Hall, Ian

    2018-03-02

    The Isles Network of Care (INOC) community nurses work at the extreme of the remote and rural continuum, working mostly as lone practitioners. Following the development of sustainable clinical supervision model for Isles nurses in Orkney, clinical supervision was found to improve both peer support and governance for this group of isolated staff. A literature overview identified the transition of clinical supervision in general nursing over 24 years from 'carrot' to 'stick'. The study included a questionnaire survey that was sent to the 2017 Queen's Nursing Institute Scotland cohort to elicit information about the nurses' experience of clinical supervision. The survey found that 55% provide supervision and 40% receive it. Health board encouragement of its use was found to be disappointingly low at 40%. The INOC nurses were surveyed about the new peer-support (restorative) model, which relies on video-conference contact to allow face to face interaction between isolated isles nurses. Feedback prompted a review of clinical supervision pairings, and the frequency and methods of meeting. The need for supervisor training led to agreement with the Remote and Rural Health Education Alliance to provide relevant support. The perceived benefits of supervision included increased support and reflection, and improved relationships with isolated colleagues.

  6. Soft-sensing model of temperature for aluminum reduction cell on improved twin support vector regression

    Science.gov (United States)

    Li, Tao

    2018-06-01

    The complexity of aluminum electrolysis process leads the temperature for aluminum reduction cells hard to measure directly. However, temperature is the control center of aluminum production. To solve this problem, combining some aluminum plant's practice data, this paper presents a Soft-sensing model of temperature for aluminum electrolysis process on Improved Twin Support Vector Regression (ITSVR). ITSVR eliminates the slow learning speed of Support Vector Regression (SVR) and the over-fit risk of Twin Support Vector Regression (TSVR) by introducing a regularization term into the objective function of TSVR, which ensures the structural risk minimization principle and lower computational complexity. Finally, the model with some other parameters as auxiliary variable, predicts the temperature by ITSVR. The simulation result shows Soft-sensing model based on ITSVR has short time-consuming and better generalization.

  7. Single-Trial Classification of Bistable Perception by Integrating Empirical Mode Decomposition, Clustering, and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Hualou Liang

    2008-04-01

    Full Text Available We propose an empirical mode decomposition (EMD- based method to extract features from the multichannel recordings of local field potential (LFP, collected from the middle temporal (MT visual cortex in a macaque monkey, for decoding its bistable structure-from-motion (SFM perception. The feature extraction approach consists of three stages. First, we employ EMD to decompose nonstationary single-trial time series into narrowband components called intrinsic mode functions (IMFs with time scales dependent on the data. Second, we adopt unsupervised K-means clustering to group the IMFs and residues into several clusters across all trials and channels. Third, we use the supervised common spatial patterns (CSP approach to design spatial filters for the clustered spatiotemporal signals. We exploit the support vector machine (SVM classifier on the extracted features to decode the reported perception on a single-trial basis. We demonstrate that the CSP feature of the cluster in the gamma frequency band outperforms the features in other frequency bands and leads to the best decoding performance. We also show that the EMD-based feature extraction can be useful for evoked potential estimation. Our proposed feature extraction approach may have potential for many applications involving nonstationary multivariable time series such as brain-computer interfaces (BCI.

  8. Slope Deformation Prediction Based on Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Lei JIA

    2013-07-01

    Full Text Available This paper principally studies the prediction of slope deformation based on Support Vector Machine (SVM. In the prediction process,explore how to reconstruct the phase space. The geological body’s displacement data obtained from chaotic time series are used as SVM’s training samples. Slope displacement caused by multivariable coupling is predicted by means of single variable. Results show that this model is of high fitting accuracy and generalization, and provides reference for deformation prediction in slope engineering.

  9. Vector-model-supported optimization in volumetric-modulated arc stereotactic radiotherapy planning for brain metastasis

    International Nuclear Information System (INIS)

    Liu, Eva Sau Fan; Wu, Vincent Wing Cheung; Harris, Benjamin; Foote, Matthew; Lehman, Margot; Chan, Lawrence Wing Chi

    2017-01-01

    Long planning time in volumetric-modulated arc stereotactic radiotherapy (VMA-SRT) cases can limit its clinical efficiency and use. A vector model could retrieve previously successful radiotherapy cases that share various common anatomic features with the current case. The prsent study aimed to develop a vector model that could reduce planning time by applying the optimization parameters from those retrieved reference cases. Thirty-six VMA-SRT cases of brain metastasis (gender, male [n = 23], female [n = 13]; age range, 32 to 81 years old) were collected and used as a reference database. Another 10 VMA-SRT cases were planned with both conventional optimization and vector-model-supported optimization, following the oncologists' clinical dose prescriptions. Planning time and plan quality measures were compared using the 2-sided paired Wilcoxon signed rank test with a significance level of 0.05, with positive false discovery rate (pFDR) of less than 0.05. With vector-model-supported optimization, there was a significant reduction in the median planning time, a 40% reduction from 3.7 to 2.2 hours (p = 0.002, pFDR = 0.032), and for the number of iterations, a 30% reduction from 8.5 to 6.0 (p = 0.006, pFDR = 0.047). The quality of plans from both approaches was comparable. From these preliminary results, vector-model-supported optimization can expedite the optimization of VMA-SRT for brain metastasis while maintaining plan quality.

  10. Vector-model-supported optimization in volumetric-modulated arc stereotactic radiotherapy planning for brain metastasis

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Eva Sau Fan [Department of Radiation Oncology, Princess Alexandra Hospital, Brisbane (Australia); Department of Health Technology and Informatics, The Hong Kong Polytechnic University (Hong Kong); Wu, Vincent Wing Cheung [Department of Health Technology and Informatics, The Hong Kong Polytechnic University (Hong Kong); Harris, Benjamin [Department of Radiation Oncology, Princess Alexandra Hospital, Brisbane (Australia); Foote, Matthew; Lehman, Margot [Department of Radiation Oncology, Princess Alexandra Hospital, Brisbane (Australia); School of Medicine, University of Queensland (Australia); Chan, Lawrence Wing Chi, E-mail: wing.chi.chan@polyu.edu.hk [Department of Health Technology and Informatics, The Hong Kong Polytechnic University (Hong Kong)

    2017-07-01

    Long planning time in volumetric-modulated arc stereotactic radiotherapy (VMA-SRT) cases can limit its clinical efficiency and use. A vector model could retrieve previously successful radiotherapy cases that share various common anatomic features with the current case. The prsent study aimed to develop a vector model that could reduce planning time by applying the optimization parameters from those retrieved reference cases. Thirty-six VMA-SRT cases of brain metastasis (gender, male [n = 23], female [n = 13]; age range, 32 to 81 years old) were collected and used as a reference database. Another 10 VMA-SRT cases were planned with both conventional optimization and vector-model-supported optimization, following the oncologists' clinical dose prescriptions. Planning time and plan quality measures were compared using the 2-sided paired Wilcoxon signed rank test with a significance level of 0.05, with positive false discovery rate (pFDR) of less than 0.05. With vector-model-supported optimization, there was a significant reduction in the median planning time, a 40% reduction from 3.7 to 2.2 hours (p = 0.002, pFDR = 0.032), and for the number of iterations, a 30% reduction from 8.5 to 6.0 (p = 0.006, pFDR = 0.047). The quality of plans from both approaches was comparable. From these preliminary results, vector-model-supported optimization can expedite the optimization of VMA-SRT for brain metastasis while maintaining plan quality.

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

    Directory of Open Access Journals (Sweden)

    Michel J. Anzanello

    2014-09-01

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

  12. An immune-inspired semi-supervised algorithm for breast cancer diagnosis.

    Science.gov (United States)

    Peng, Lingxi; Chen, Wenbin; Zhou, Wubai; Li, Fufang; Yang, Jin; Zhang, Jiandong

    2016-10-01

    Breast cancer is the most frequently and world widely diagnosed life-threatening cancer, which is the leading cause of cancer death among women. Early accurate diagnosis can be a big plus in treating breast cancer. Researchers have approached this problem using various data mining and machine learning techniques such as support vector machine, artificial neural network, etc. The computer immunology is also an intelligent method inspired by biological immune system, which has been successfully applied in pattern recognition, combination optimization, machine learning, etc. However, most of these diagnosis methods belong to a supervised diagnosis method. It is very expensive to obtain labeled data in biology and medicine. In this paper, we seamlessly integrate the state-of-the-art research on life science with artificial intelligence, and propose a semi-supervised learning algorithm to reduce the need for labeled data. We use two well-known benchmark breast cancer datasets in our study, which are acquired from the UCI machine learning repository. Extensive experiments are conducted and evaluated on those two datasets. Our experimental results demonstrate the effectiveness and efficiency of our proposed algorithm, which proves that our algorithm is a promising automatic diagnosis method for breast cancer. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  13. Automated labelling of cancer textures in colorectal histopathology slides using quasi-supervised learning.

    Science.gov (United States)

    Onder, Devrim; Sarioglu, Sulen; Karacali, Bilge

    2013-04-01

    Quasi-supervised learning is a statistical learning algorithm that contrasts two datasets by computing estimate for the posterior probability of each sample in either dataset. This method has not been applied to histopathological images before. The purpose of this study is to evaluate the performance of the method to identify colorectal tissues with or without adenocarcinoma. Light microscopic digital images from histopathological sections were obtained from 30 colorectal radical surgery materials including adenocarcinoma and non-neoplastic regions. The texture features were extracted by using local histograms and co-occurrence matrices. The quasi-supervised learning algorithm operates on two datasets, one containing samples of normal tissues labelled only indirectly, and the other containing an unlabeled collection of samples of both normal and cancer tissues. As such, the algorithm eliminates the need for manually labelled samples of normal and cancer tissues for conventional supervised learning and significantly reduces the expert intervention. Several texture feature vector datasets corresponding to different extraction parameters were tested within the proposed framework. The Independent Component Analysis dimensionality reduction approach was also identified as the one improving the labelling performance evaluated in this series. In this series, the proposed method was applied to the dataset of 22,080 vectors with reduced dimensionality 119 from 132. Regions containing cancer tissue could be identified accurately having false and true positive rates up to 19% and 88% respectively without using manually labelled ground-truth datasets in a quasi-supervised strategy. The resulting labelling performances were compared to that of a conventional powerful supervised classifier using manually labelled ground-truth data. The supervised classifier results were calculated as 3.5% and 95% for the same case. The results in this series in comparison with the benchmark

  14. Dual linear structured support vector machine tracking method via scale correlation filter

    Science.gov (United States)

    Li, Weisheng; Chen, Yanquan; Xiao, Bin; Feng, Chen

    2018-01-01

    Adaptive tracking-by-detection methods based on structured support vector machine (SVM) performed well on recent visual tracking benchmarks. However, these methods did not adopt an effective strategy of object scale estimation, which limits the overall tracking performance. We present a tracking method based on a dual linear structured support vector machine (DLSSVM) with a discriminative scale correlation filter. The collaborative tracker comprised of a DLSSVM model and a scale correlation filter obtains good results in tracking target position and scale estimation. The fast Fourier transform is applied for detection. Extensive experiments show that our tracking approach outperforms many popular top-ranking trackers. On a benchmark including 100 challenging video sequences, the average precision of the proposed method is 82.8%.

  15. Support vector machines in analysis of top quark production

    International Nuclear Information System (INIS)

    Vaiciulis, A.

    2003-01-01

    The Support Vector Machine (SVM) learning algorithm is a new alternative to multivariate methods such as neural networks. Potential applications of SVMs in high energy physics include the common classification problem of signal/background discrimination as well as particle identification. A comparison of a conventional method and an SVM algorithm is presented here for the case of identifying top quark events in Run II physics at the CDF experiment

  16. Oblique decision trees using embedded support vector machines in classifier ensembles

    NARCIS (Netherlands)

    Menkovski, V.; Christou, I.; Efremidis, S.

    2008-01-01

    Classifier ensembles have emerged in recent years as a promising research area for boosting pattern recognition systems' performance. We present a new base classifier that utilizes oblique decision tree technology based on support vector machines for the construction of oblique (non-axis parallel)

  17. Application of higher order spectral features and support vector machines for bearing faults classification.

    Science.gov (United States)

    Saidi, Lotfi; Ben Ali, Jaouher; Fnaiech, Farhat

    2015-01-01

    Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals. Copyright © 2014 ISA

  18. Subspace identification of Hammer stein models using support vector machines

    International Nuclear Information System (INIS)

    Al-Dhaifallah, Mujahed

    2011-01-01

    System identification is the art of finding mathematical tools and algorithms that build an appropriate mathematical model of a system from measured input and output data. Hammerstein model, consisting of a memoryless nonlinearity followed by a dynamic linear element, is often a good trade-off as it can represent some dynamic nonlinear systems very accurately, but is nonetheless quite simple. Moreover, the extensive knowledge about LTI system representations can be applied to the dynamic linear block. On the other hand, finding an effective representation for the nonlinearity is an active area of research. Recently, support vector machines (SVMs) and least squares support vector machines (LS-SVMs) have demonstrated powerful abilities in approximating linear and nonlinear functions. In contrast with other approximation methods, SVMs do not require a-priori structural information. Furthermore, there are well established methods with guaranteed convergence (ordinary least squares, quadratic programming) for fitting LS-SVMs and SVMs. The general objective of this research is to develop new subspace algorithms for Hammerstein systems based on SVM regression.

  19. Phytoplankton global mapping from space with a support vector machine algorithm

    Science.gov (United States)

    de Boissieu, Florian; Menkes, Christophe; Dupouy, Cécile; Rodier, Martin; Bonnet, Sophie; Mangeas, Morgan; Frouin, Robert J.

    2014-11-01

    In recent years great progress has been made in global mapping of phytoplankton from space. Two main trends have emerged, the recognition of phytoplankton functional types (PFT) based on reflectance normalized to chlorophyll-a concentration, and the recognition of phytoplankton size class (PSC) based on the relationship between cell size and chlorophyll-a concentration. However, PFTs and PSCs are not decorrelated, and one approach can complement the other in a recognition task. In this paper, we explore the recognition of several dominant PFTs by combining reflectance anomalies, chlorophyll-a concentration and other environmental parameters, such as sea surface temperature and wind speed. Remote sensing pixels are labeled thanks to coincident in-situ pigment data from GeP&CO, NOMAD and MAREDAT datasets, covering various oceanographic environments. The recognition is made with a supervised Support Vector Machine classifier trained on the labeled pixels. This algorithm enables a non-linear separation of the classes in the input space and is especially adapted for small training datasets as available here. Moreover, it provides a class probability estimate, allowing one to enhance the robustness of the classification results through the choice of a minimum probability threshold. A greedy feature selection associated to a 10-fold cross-validation procedure is applied to select the most discriminative input features and evaluate the classification performance. The best classifiers are finally applied on daily remote sensing datasets (SeaWIFS, MODISA) and the resulting dominant PFT maps are compared with other studies. Several conclusions are drawn: (1) the feature selection highlights the weight of temperature, chlorophyll-a and wind speed variables in phytoplankton recognition; (2) the classifiers show good results and dominant PFT maps in agreement with phytoplankton distribution knowledge; (3) classification on MODISA data seems to perform better than on SeaWIFS data

  20. Improved Anomaly Detection using Integrated Supervised and Unsupervised Processing

    Science.gov (United States)

    Hunt, B.; Sheppard, D. G.; Wetterer, C. J.

    There are two broad technologies of signal processing applicable to space object feature identification using nonresolved imagery: supervised processing analyzes a large set of data for common characteristics that can be then used to identify, transform, and extract information from new data taken of the same given class (e.g. support vector machine); unsupervised processing utilizes detailed physics-based models that generate comparison data that can then be used to estimate parameters presumed to be governed by the same models (e.g. estimation filters). Both processes have been used in non-resolved space object identification and yield similar results yet arrived at using vastly different processes. The goal of integrating the results of the two is to seek to achieve an even greater performance by building on the process diversity. Specifically, both supervised processing and unsupervised processing will jointly operate on the analysis of brightness (radiometric flux intensity) measurements reflected by space objects and observed by a ground station to determine whether a particular day conforms to a nominal operating mode (as determined from a training set) or exhibits anomalous behavior where a particular parameter (e.g. attitude, solar panel articulation angle) has changed in some way. It is demonstrated in a variety of different scenarios that the integrated process achieves a greater performance than each of the separate processes alone.

  1. Online Support Vector Regression with Varying Parameters for Time-Dependent Data

    International Nuclear Information System (INIS)

    Omitaomu, Olufemi A.; Jeong, Myong K.; Badiru, Adedeji B.

    2011-01-01

    Support vector regression (SVR) is a machine learning technique that continues to receive interest in several domains including manufacturing, engineering, and medicine. In order to extend its application to problems in which datasets arrive constantly and in which batch processing of the datasets is infeasible or expensive, an accurate online support vector regression (AOSVR) technique was proposed. The AOSVR technique efficiently updates a trained SVR function whenever a sample is added to or removed from the training set without retraining the entire training data. However, the AOSVR technique assumes that the new samples and the training samples are of the same characteristics; hence, the same value of SVR parameters is used for training and prediction. This assumption is not applicable to data samples that are inherently noisy and non-stationary such as sensor data. As a result, we propose Accurate On-line Support Vector Regression with Varying Parameters (AOSVR-VP) that uses varying SVR parameters rather than fixed SVR parameters, and hence accounts for the variability that may exist in the samples. To accomplish this objective, we also propose a generalized weight function to automatically update the weights of SVR parameters in on-line monitoring applications. The proposed function allows for lower and upper bounds for SVR parameters. We tested our proposed approach and compared results with the conventional AOSVR approach using two benchmark time series data and sensor data from nuclear power plant. The results show that using varying SVR parameters is more applicable to time dependent data.

  2. An implementation of support vector machine on sentiment classification of movie reviews

    Science.gov (United States)

    Yulietha, I. M.; Faraby, S. A.; Adiwijaya; Widyaningtyas, W. C.

    2018-03-01

    With technological advances, all information about movie is available on the internet. If the information is processed properly, it will get the quality of the information. This research proposes to the classify sentiments on movie review documents. This research uses Support Vector Machine (SVM) method because it can classify high dimensional data in accordance with the data used in this research in the form of text. Support Vector Machine is a popular machine learning technique for text classification because it can classify by learning from a collection of documents that have been classified previously and can provide good result. Based on number of datasets, the 90-10 composition has the best result that is 85.6%. Based on SVM kernel, kernel linear with constant 1 has the best result that is 84.9%

  3. Modeling and prediction of Turkey's electricity consumption using Support Vector Regression

    International Nuclear Information System (INIS)

    Kavaklioglu, Kadir

    2011-01-01

    Support Vector Regression (SVR) methodology is used to model and predict Turkey's electricity consumption. Among various SVR formalisms, ε-SVR method was used since the training pattern set was relatively small. Electricity consumption is modeled as a function of socio-economic indicators such as population, Gross National Product, imports and exports. In order to facilitate future predictions of electricity consumption, a separate SVR model was created for each of the input variables using their current and past values; and these models were combined to yield consumption prediction values. A grid search for the model parameters was performed to find the best ε-SVR model for each variable based on Root Mean Square Error. Electricity consumption of Turkey is predicted until 2026 using data from 1975 to 2006. The results show that electricity consumption can be modeled using Support Vector Regression and the models can be used to predict future electricity consumption. (author)

  4. Extraction of inland Nypa fruticans (Nipa Palm) using Support Vector Machine

    Science.gov (United States)

    Alberto, R. T.; Serrano, S. C.; Damian, G. B.; Camaso, E. E.; Biagtan, A. R.; Panuyas, N. Z.; Quibuyen, J. S.

    2017-09-01

    Mangroves are considered as one of the major habitats in coastal ecosystem, providing a lot of economic and ecological services in human society. Nypa fruticans (Nipa palm) is one of the important species of mangroves because of its versatility and uniqueness as halophytic palm. However, nipas are not only adaptable in saline areas, they can also managed to thrive away from the coastline depending on the favorable soil types available in the area. Because of this, mapping of this species are not limited alone in the near shore areas, but in areas where this species are present as well. The extraction process of Nypa fruticans were carried out using the available LiDAR data. Support Vector Machine (SVM) classification process was used to extract nipas in inland areas. The SVM classification process in mapping Nypa fruticans produced high accuracy of 95+%. The Support Vector Machine classification process to extract inland nipas was proven to be effective by utilizing different terrain derivatives from LiDAR data.

  5. Automatic Detection of P and S Phases by Support Vector Machine

    Science.gov (United States)

    Jiang, Y.; Ning, J.; Bao, T.

    2017-12-01

    Many methods in seismology rely on accurately picked phases. A well performed program on automatically phase picking will assure the application of these methods. Related researches before mostly focus on finding different characteristics between noise and phases, which are all not enough successful. We have developed a new method which mainly based on support vector machine to detect P and S phases. In it, we first input some waveform pieces into the support vector machine, then employ it to work out a hyper plane which can divide the space into two parts: respectively noise and phase. We further use the same method to find a hyper plane which can separate the phase space into P and S parts based on the three components' cross-correlation matrix. In order to further improve the ability of phase detection, we also employ array data. At last, we show that the overall effect of our method is robust by employing both synthetic and real data.

  6. Prediction of protein binding sites using physical and chemical descriptors and the support vector machine regression method

    International Nuclear Information System (INIS)

    Sun Zhong-Hua; Jiang Fan

    2010-01-01

    In this paper a new continuous variable called core-ratio is defined to describe the probability for a residue to be in a binding site, thereby replacing the previous binary description of the interface residue using 0 and 1. So we can use the support vector machine regression method to fit the core-ratio value and predict the protein binding sites. We also design a new group of physical and chemical descriptors to characterize the binding sites. The new descriptors are more effective, with an averaging procedure used. Our test shows that much better prediction results can be obtained by the support vector regression (SVR) method than by the support vector classification method. (rapid communication)

  7. Enhancing the intrinsic work motivation of community nutrition educators: how supportive supervision and job design foster autonomy.

    Science.gov (United States)

    Dickin, Katherine L; Dollahite, Jamie S; Habicht, Jean-Pierre

    2011-01-01

    Mixed-methods research investigated the work motivation of paraprofessional community nutrition educators (CNEs) delivering a long-running public health nutrition program. In interviews, CNEs (n = 9) emphasized "freedom," supportive supervision, and "making a difference" as key sources of motivation. Community nutrition educator surveys (n = 115) confirmed high levels of autonomy, which was associated with supervisors' delegation and support, CNE decision-making on scheduling and curricula, and job satisfaction. Supervisors (n = 32) rated CNEs' job design as having inherently motivating characteristics comparable to professional jobs. Supervisory strategies can complement job design to create structured, supportive contexts that maintain fidelity, while granting autonomy to paraprofessionals to enhance intrinsic work motivation.

  8. Support vector machine based fault classification and location of a long transmission line

    Directory of Open Access Journals (Sweden)

    Papia Ray

    2016-09-01

    Full Text Available This paper investigates support vector machine based fault type and distance estimation scheme in a long transmission line. The planned technique uses post fault single cycle current waveform and pre-processing of the samples is done by wavelet packet transform. Energy and entropy are obtained from the decomposed coefficients and feature matrix is prepared. Then the redundant features from the matrix are taken out by the forward feature selection method and normalized. Test and train data are developed by taking into consideration variables of a simulation situation like fault type, resistance path, inception angle, and distance. In this paper 10 different types of short circuit fault are analyzed. The test data are examined by support vector machine whose parameters are optimized by particle swarm optimization method. The anticipated method is checked on a 400 kV, 300 km long transmission line with voltage source at both the ends. Two cases were examined with the proposed method. The first one is fault very near to both the source end (front and rear and the second one is support vector machine with and without optimized parameter. Simulation result indicates that the anticipated method for fault classification gives high accuracy (99.21% and least fault distance estimation error (0.29%.

  9. 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.

  10. Penerapan Support Vector Machine (SVM untuk Pengkategorian Penelitian

    Directory of Open Access Journals (Sweden)

    Fithri Selva Jumeilah

    2017-07-01

    Full Text Available Research every college will continue to grow. Research will be stored in softcopy and hardcopy. The preparation of the research should be categorized in order to facilitate the search for people who need reference. To categorize the research, we need a method for text mining, one of them is with the implementation of Support Vector Machines (SVM. The data used to recognize the characteristics of each category then it takes secondary data which is a collection of abstracts of research. The data will be pre-processed with several stages: case folding converts all the letters into lowercase, stop words removal removal of very common words, tokenizing discard punctuation, and stemming searching for root words by removing the prefix and suffix. Further data that has undergone preprocessing will be converted into a numerical form with for the term weighting stage that is the weighting contribution of each word. From the results of term weighting then obtained data that can be used for data training and test data. The training process is done by providing input in the form of text data that is known to the class or category. Then by using the Support Vector Machines algorithm, the input data is transformed into a rule, function, or knowledge model that can be used in the prediction process. From the results of this study obtained that the categorization of research produced by SVM has been very good. This is proven by the results of the test which resulted in an accuracy of 90%.

  11. Discussion About Nonlinear Time Series Prediction Using Least Squares Support Vector Machine

    International Nuclear Information System (INIS)

    Xu Ruirui; Bian Guoxing; Gao Chenfeng; Chen Tianlun

    2005-01-01

    The least squares support vector machine (LS-SVM) is used to study the nonlinear time series prediction. First, the parameter γ and multi-step prediction capabilities of the LS-SVM network are discussed. Then we employ clustering method in the model to prune the number of the support values. The learning rate and the capabilities of filtering noise for LS-SVM are all greatly improved.

  12. Support Vector Machines as tools for mortality graduation

    Directory of Open Access Journals (Sweden)

    Alberto Olivares

    2011-01-01

    Full Text Available A topic of interest in demographic and biostatistical analysis as well as in actuarial practice,is the graduation of the age-specific mortality pattern. A classical graduation technique is to fit parametric models. Recently, particular emphasis has been given to graduation using nonparametric techniques. Support Vector Machines (SVM is an innovative methodology that could be utilized for mortality graduation purposes. This paper evaluates SVM techniques as tools for graduating mortality rates. We apply SVM to empirical death rates from a variety of populations and time periods. For comparison, we also apply standard graduation techniques to the same data.

  13. A comparison study of support vector machines and hidden Markov models in machinery condition monitoring

    International Nuclear Information System (INIS)

    Miao, Qiang; Huang, Hong Zhong; Fan, Xianfeng

    2007-01-01

    Condition classification is an important step in machinery fault detection, which is a problem of pattern recognition. Currently, there are a lot of techniques in this area and the purpose of this paper is to investigate two popular recognition techniques, namely hidden Markov model and support vector machine. At the beginning, we briefly introduced the procedure of feature extraction and the theoretical background of this paper. The comparison experiment was conducted for gearbox fault detection and the analysis results from this work showed that support vector machine has better classification performance in this area

  14. Classification of Motor Imagery EEG Signals with Support Vector Machines and Particle Swarm Optimization

    Science.gov (United States)

    Ma, Yuliang; Ding, Xiaohui; She, Qingshan; Luo, Zhizeng; Potter, Thomas; Zhang, Yingchun

    2016-01-01

    Support vector machines are powerful tools used to solve the small sample and nonlinear classification problems, but their ultimate classification performance depends heavily upon the selection of appropriate kernel and penalty parameters. In this study, we propose using a particle swarm optimization algorithm to optimize the selection of both the kernel and penalty parameters in order to improve the classification performance of support vector machines. The performance of the optimized classifier was evaluated with motor imagery EEG signals in terms of both classification and prediction. Results show that the optimized classifier can significantly improve the classification accuracy of motor imagery EEG signals. PMID:27313656

  15. Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR for Load Forecasting

    Directory of Open Access Journals (Sweden)

    Cheng-Wen Lee

    2016-10-01

    Full Text Available Hybridizing chaotic evolutionary algorithms with support vector regression (SVR to improve forecasting accuracy is a hot topic in electricity load forecasting. Trapping at local optima and premature convergence are critical shortcomings of the tabu search (TS algorithm. This paper investigates potential improvements of the TS algorithm by applying quantum computing mechanics to enhance the search information sharing mechanism (tabu memory to improve the forecasting accuracy. This article presents an SVR-based load forecasting model that integrates quantum behaviors and the TS algorithm with the support vector regression model (namely SVRQTS to obtain a more satisfactory forecasting accuracy. Numerical examples demonstrate that the proposed model outperforms the alternatives.

  16. Strengthening malaria service delivery through supportive supervision and community mobilization in an endemic Indian setting: an evaluation of nested delivery models.

    Science.gov (United States)

    Das, Ashis; Friedman, Jed; Kandpal, Eeshani; Ramana, Gandham N V; Gupta, Rudra Kumar Das; Pradhan, Madan M; Govindaraj, Ramesh

    2014-12-08

    Malaria continues to be a prominent global public health challenge. This study tested the effectiveness of two service delivery models for reducing the malaria burden, e.g. supportive supervision of community health workers (CHW) and community mobilization in promoting appropriate health-seeking behaviour for febrile illnesses in Odisha, India. The study population comprised 120 villages from two purposively chosen malaria-endemic districts, with 40 villages randomly assigned to each of the two treatment arms, one with both supportive supervision and community mobilization and one with community mobilization alone, as well as an observational control arm. Outcome measures included changes in the utilization of bed nets and timely care-seeking for fever from a trained provider compared to the control group. Analysis was by intention-to-treat. Significant improvements were observed in the reported utilization of bed nets in both intervention arms (84.5% in arm A and 82.4% in arm B versus 78.6% in the control arm; p skilled provider within 24 hours than fever cases from the control arm (50.1%). In particular, women from the combined interventions arm were more likely to have received timely treatment from a skilled provider (61.6% vs. 47.2%; p = 0.028). A community-based intervention combining the supportive supervision of community health workers with intensive community mobilization and can be effective in improving care-seeking and preventive behaviour and may be used to strengthen the national malaria control programme.

  17. Towards automatic lithological classification from remote sensing data using support vector machines

    Science.gov (United States)

    Yu, Le; Porwal, Alok; Holden, Eun-Jung; Dentith, Michael

    2010-05-01

    Remote sensing data can be effectively used as a mean to build geological knowledge for poorly mapped terrains. Spectral remote sensing data from space- and air-borne sensors have been widely used to geological mapping, especially in areas of high outcrop density in arid regions. However, spectral remote sensing information by itself cannot be efficiently used for a comprehensive lithological classification of an area due to (1) diagnostic spectral response of a rock within an image pixel is conditioned by several factors including the atmospheric effects, spectral and spatial resolution of the image, sub-pixel level heterogeneity in chemical and mineralogical composition of the rock, presence of soil and vegetation cover; (2) only surface information and is therefore highly sensitive to the noise due to weathering, soil cover, and vegetation. Consequently, for efficient lithological classification, spectral remote sensing data needs to be supplemented with other remote sensing datasets that provide geomorphological and subsurface geological information, such as digital topographic model (DEM) and aeromagnetic data. Each of the datasets contain significant information about geology that, in conjunction, can potentially be used for automated lithological classification using supervised machine learning algorithms. In this study, support vector machine (SVM), which is a kernel-based supervised learning method, was applied to automated lithological classification of a study area in northwestern India using remote sensing data, namely, ASTER, DEM and aeromagnetic data. Several digital image processing techniques were used to produce derivative datasets that contained enhanced information relevant to lithological discrimination. A series of SVMs (trained using k-folder cross-validation with grid search) were tested using various combinations of input datasets selected from among 50 datasets including the original 14 ASTER bands and 36 derivative datasets (including 14

  18. Credit Scoring by Fuzzy Support Vector Machines with a Novel Membership Function

    Directory of Open Access Journals (Sweden)

    Jian Shi

    2016-11-01

    Full Text Available Due to the recent financial crisis and European debt crisis, credit risk evaluation has become an increasingly important issue for financial institutions. Reliable credit scoring models are crucial for commercial banks to evaluate the financial performance of clients and have been widely studied in the fields of statistics and machine learning. In this paper a novel fuzzy support vector machine (SVM credit scoring model is proposed for credit risk analysis, in which fuzzy membership is adopted to indicate different contribution of each input point to the learning of SVM classification hyperplane. Considering the methodological consistency, support vector data description (SVDD is introduced to construct the fuzzy membership function and to reduce the effect of outliers and noises. The SVDD-based fuzzy SVM model is tested against the traditional fuzzy SVM on two real-world datasets and the research results confirm the effectiveness of the presented method.

  19. The Impact of Different Support Vectors on GOSAT-2 CAI-2 L2 Cloud Discrimination

    Directory of Open Access Journals (Sweden)

    Yu Oishi

    2017-11-01

    Full Text Available Greenhouse gases Observing SATellite-2 (GOSAT-2 will be launched in fiscal year 2018. GOSAT-2 will be equipped with two sensors: the Thermal and Near-infrared Sensor for Carbon Observation (TANSO-Fourier Transform Spectrometer 2 (FTS-2 and the TANSO-Cloud and Aerosol Imager 2 (CAI-2. CAI-2 is a push-broom imaging sensor that has forward- and backward-looking bands to observe the optical properties of aerosols and clouds and to monitor the status of urban air pollution and transboundary air pollution over oceans, such as PM2.5 (particles less than 2.5 micrometers in diameter. CAI-2 has important applications for cloud discrimination in each direction. The Cloud and Aerosol Unbiased Decision Intellectual Algorithm (CLAUDIA1, which applies sequential threshold tests to features is used for GOSAT CAI L2 cloud flag processing. If CLAUDIA1 is used with CAI-2, it is necessary to optimize the thresholds in accordance with CAI-2. However, CLAUDIA3 with support vector machines (SVM, a supervised pattern recognition method, was developed, and then we applied CLAUDIA3 for GOSAT-2 CAI-2 L2 cloud discrimination processing. Thus, CLAUDIA3 can automatically find the optimized boundary between clear and cloudy areas. Improvements in CLAUDIA3 using CAI (CLAUDIA3-CAI continue to be made. In this study, we examined the impact of various support vectors (SV on GOSAT-2 CAI-2 L2 cloud discrimination by analyzing (1 the impact of the choice of different time periods for the training data and (2 the impact of different generation procedures for SV on the cloud discrimination efficiency. To generate SV for CLAUDIA3-CAI from MODIS data, there are two times at which features are extracted, corresponding to CAI bands. One procedure is equivalent to generating SV using CAI data. Another procedure generates SV for MODIS cloud discrimination at the beginning, and then extracts decision function, thresholds, and SV corresponding to CAI bands. Our results indicated the following

  20. Noise reduction by support vector regression with a Ricker wavelet kernel

    International Nuclear Information System (INIS)

    Deng, Xiaoying; Yang, Dinghui; Xie, Jing

    2009-01-01

    We propose a noise filtering technology based on the least-squares support vector regression (LS-SVR), to improve the signal-to-noise ratio (SNR) of seismic data. We modified it by using an admissible support vector (SV) kernel, namely the Ricker wavelet kernel, to replace the conventional radial basis function (RBF) kernel in seismic data processing. We investigated the selection of the regularization parameter for the LS-SVR and derived a concise selecting formula directly from the noisy data. We used the proposed method for choosing the regularization parameter which not only had the advantage of high speed but could also obtain almost the same effectiveness as an optimal parameter method. We conducted experiments using synthetic data corrupted by the random noise of different types and levels, and found that our method was superior to the wavelet transform-based approach and the Wiener filtering. We also applied the method to two field seismic data sets and concluded that it was able to effectively suppress the random noise and improve the data quality in terms of SNR

  1. Noise reduction by support vector regression with a Ricker wavelet kernel

    Science.gov (United States)

    Deng, Xiaoying; Yang, Dinghui; Xie, Jing

    2009-06-01

    We propose a noise filtering technology based on the least-squares support vector regression (LS-SVR), to improve the signal-to-noise ratio (SNR) of seismic data. We modified it by using an admissible support vector (SV) kernel, namely the Ricker wavelet kernel, to replace the conventional radial basis function (RBF) kernel in seismic data processing. We investigated the selection of the regularization parameter for the LS-SVR and derived a concise selecting formula directly from the noisy data. We used the proposed method for choosing the regularization parameter which not only had the advantage of high speed but could also obtain almost the same effectiveness as an optimal parameter method. We conducted experiments using synthetic data corrupted by the random noise of different types and levels, and found that our method was superior to the wavelet transform-based approach and the Wiener filtering. We also applied the method to two field seismic data sets and concluded that it was able to effectively suppress the random noise and improve the data quality in terms of SNR.

  2. Adaptive image denoising based on support vector machine and wavelet description

    Science.gov (United States)

    An, Feng-Ping; Zhou, Xian-Wei

    2017-12-01

    Adaptive image denoising method decomposes the original image into a series of basic pattern feature images on the basis of wavelet description and constructs the support vector machine regression function to realize the wavelet description of the original image. The support vector machine method allows the linear expansion of the signal to be expressed as a nonlinear function of the parameters associated with the SVM. Using the radial basis kernel function of SVM, the original image can be extended into a MEXICAN function and a residual trend. This MEXICAN represents a basic image feature pattern. If the residual does not fluctuate, it can also be represented as a characteristic pattern. If the residuals fluctuate significantly, it is treated as a new image and the same decomposition process is repeated until the residuals obtained by the decomposition do not significantly fluctuate. Experimental results show that the proposed method in this paper performs well; especially, it satisfactorily solves the problem of image noise removal. It may provide a new tool and method for image denoising.

  3. Support vector regression model based predictive control of water level of U-tube steam generators

    Energy Technology Data Exchange (ETDEWEB)

    Kavaklioglu, Kadir, E-mail: kadir.kavaklioglu@pau.edu.tr

    2014-10-15

    Highlights: • Water level of U-tube steam generators was controlled in a model predictive fashion. • Models for steam generator water level were built using support vector regression. • Cost function minimization for future optimal controls was performed by using the steepest descent method. • The results indicated the feasibility of the proposed method. - Abstract: A predictive control algorithm using support vector regression based models was proposed for controlling the water level of U-tube steam generators of pressurized water reactors. Steam generator data were obtained using a transfer function model of U-tube steam generators. Support vector regression based models were built using a time series type model structure for five different operating powers. Feedwater flow controls were calculated by minimizing a cost function that includes the level error, the feedwater change and the mismatch between feedwater and steam flow rates. Proposed algorithm was applied for a scenario consisting of a level setpoint change and a steam flow disturbance. The results showed that steam generator level can be controlled at all powers effectively by the proposed method.

  4. Insect cell transformation vectors that support high level expression and promoter assessment in insect cell culture

    Science.gov (United States)

    A somatic transformation vector, pDP9, was constructed that provides a simplified means of producing permanently transformed cultured insect cells that support high levels of protein expression of foreign genes. The pDP9 plasmid vector incorporates DNA sequences from the Junonia coenia densovirus th...

  5. Reliable Fault Classification of Induction Motors Using Texture Feature Extraction and a Multiclass Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Jia Uddin

    2014-01-01

    Full Text Available This paper proposes a method for the reliable fault detection and classification of induction motors using two-dimensional (2D texture features and a multiclass support vector machine (MCSVM. The proposed model first converts time-domain vibration signals to 2D gray images, resulting in texture patterns (or repetitive patterns, and extracts these texture features by generating the dominant neighborhood structure (DNS map. The principal component analysis (PCA is then used for the purpose of dimensionality reduction of the high-dimensional feature vector including the extracted texture features due to the fact that the high-dimensional feature vector can degrade classification performance, and this paper configures an effective feature vector including discriminative fault features for diagnosis. Finally, the proposed approach utilizes the one-against-all (OAA multiclass support vector machines (MCSVMs to identify induction motor failures. In this study, the Gaussian radial basis function kernel cooperates with OAA MCSVMs to deal with nonlinear fault features. Experimental results demonstrate that the proposed approach outperforms three state-of-the-art fault diagnosis algorithms in terms of fault classification accuracy, yielding an average classification accuracy of 100% even in noisy environments.

  6. Broad Absorption Line Quasar catalogues with Supervised Neural Networks

    International Nuclear Information System (INIS)

    Scaringi, Simone; Knigge, Christian; Cottis, Christopher E.; Goad, Michael R.

    2008-01-01

    We have applied a Learning Vector Quantization (LVQ) algorithm to SDSS DR5 quasar spectra in order to create a large catalogue of broad absorption line quasars (BALQSOs). We first discuss the problems with BALQSO catalogues constructed using the conventional balnicity and/or absorption indices (BI and AI), and then describe the supervised LVQ network we have trained to recognise BALQSOs. The resulting BALQSO catalogue should be substantially more robust and complete than BI-or AI-based ones.

  7. Maximum margin semi-supervised learning with irrelevant data.

    Science.gov (United States)

    Yang, Haiqin; Huang, Kaizhu; King, Irwin; Lyu, Michael R

    2015-10-01

    Semi-supervised learning (SSL) is a typical learning paradigms training a model from both labeled and unlabeled data. The traditional SSL models usually assume unlabeled data are relevant to the labeled data, i.e., following the same distributions of the targeted labeled data. In this paper, we address a different, yet formidable scenario in semi-supervised classification, where the unlabeled data may contain irrelevant data to the labeled data. To tackle this problem, we develop a maximum margin model, named tri-class support vector machine (3C-SVM), to utilize the available training data, while seeking a hyperplane for separating the targeted data well. Our 3C-SVM exhibits several characteristics and advantages. First, it does not need any prior knowledge and explicit assumption on the data relatedness. On the contrary, it can relieve the effect of irrelevant unlabeled data based on the logistic principle and maximum entropy principle. That is, 3C-SVM approaches an ideal classifier. This classifier relies heavily on labeled data and is confident on the relevant data lying far away from the decision hyperplane, while maximally ignoring the irrelevant data, which are hardly distinguished. Second, theoretical analysis is provided to prove that in what condition, the irrelevant data can help to seek the hyperplane. Third, 3C-SVM is a generalized model that unifies several popular maximum margin models, including standard SVMs, Semi-supervised SVMs (S(3)VMs), and SVMs learned from the universum (U-SVMs) as its special cases. More importantly, we deploy a concave-convex produce to solve the proposed 3C-SVM, transforming the original mixed integer programming, to a semi-definite programming relaxation, and finally to a sequence of quadratic programming subproblems, which yields the same worst case time complexity as that of S(3)VMs. Finally, we demonstrate the effectiveness and efficiency of our proposed 3C-SVM through systematical experimental comparisons. Copyright

  8. The effects of clinical supervision on supervisees and patients in cognitive behavioral therapy: a systematic review.

    Science.gov (United States)

    Alfonsson, Sven; Parling, Thomas; Spännargård, Åsa; Andersson, Gerhard; Lundgren, Tobias

    2018-05-01

    Clinical supervision is a central part of psychotherapist training but the empirical support for specific supervision theories or features is unclear. The aims of this study were to systematically review the empirical research literature regarding the effects of clinical supervision on therapists' competences and clinical outcomes within Cognitive Behavior Therapy (CBT). A comprehensive database search resulted in 4103 identified publications. Of these, 133 were scrutinized and in the end 5 studies were included in the review for data synthesis. The five studies were heterogeneous in scope and quality and only one provided firm empirical support for the positive effects of clinical supervision on therapists' competence. The remaining four studies suffered from methodological weaknesses, but provided some preliminary support that clinical supervision may be beneficiary for novice therapists. No study could show benefits from supervision for patients. The research literature suggests that clinical supervision may have some potential effects on novice therapists' competence compared to no supervision but the effects on clinical outcomes are still unclear. While bug-in-the-eye live supervision may be more effective than standard delayed supervision, the effects of specific supervision models or features are also unclear. There is a continued need for high-quality empirical studies on the effects of clinical supervision in psychotherapy.

  9. The Influence of Quality Assurance and Supportive Supervision on the Quality of Medical Care in Children’s Hospitals of the Municipal Level of the Rostov Region

    Directory of Open Access Journals (Sweden)

    Tatiana V. Kulichenko

    2017-01-01

    Full Text Available Background. Improving the quality of medical care is the absolute priority of the World Health Organization and all socially-oriented ministries and departments around the world.Objective. Our aim was to evaluate the effectiveness of quality  assurance and supportive supervision in municipal hospitals to improve the quality of medical care for children (by the example of the Rostov region.Methods. The open observational study included 10 second-level hospitals in the Rostov Region. At the start of the project, the quality of inpatient care for children in the region was audited based on recommendations and tools of the World Health Organization, and training of medical personnel was organized. Monitoring visits to hospitals were carried out by experts every 3 months (supportive supervision. Reaudit of the quality of care was conducted a year later.Results. As a result of regular quality assurance and supportive supervision of hospitals during the first year of operation, such indicators of the quality of medical care as the availability of medical equipment for emergency care for children, the infrastructure of children’s departments, the triage and provision of emergency care in children’s departments, managing patients with various acute conditions (fever, diarrhea, respiratory diseases, supporting care, internal quality assurance, accessibility to standards of care and clinical guidelines for the diagnosis and treatment of various diseases, respect for children’s rights were significantly (p < 0.05 improved.Conclusion. Supportive supervision and regular external quality assurances of hospitals contribute to a rapid increase in the quality of medical care for children.

  10. Sentiment Analysis in the Sales Review of Indonesian Marketplace by Utilizing Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Anang Anggono Lutfi

    2018-04-01

    Full Text Available The online store is changing people’s shopping behavior. Despite the fact, the potential customer’s distrust in the quality of products and service is one of the online store’s weaknesses. A review is provided by the online stores to overcome this weakness. Customers often write a review using languages that are not well structured. Sentiment analysis is used to extract the polarity of the unstructured texts. This research attempted to do a sentiment analysis in the sales review. Sentiment analysis in sales reviews can be used as a tool to evaluate the sales. This research intends to conduct a sentiment analysis in the sales review of Indonesian marketplace by utilizing Support Vector Machine and Naive Bayes. The reviews of the data are gathered from one of Indonesian marketplace, Bukalapak. The data are classified into positive or negative class. TF-IDF is used to feature extraction. The experiment shows that Support Vector Machine with linear kernel provides higher accuracy than Naive Bayes. Support Vector Machine shows the highest accuracy average. The generated accuracy is 93.65%. This approach of sentiment analysis in sales review can be used as the base of intelligent sales evaluation for online stores in the future.

  11. 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.

  12. Neutron–gamma discrimination based on the support vector machine method

    International Nuclear Information System (INIS)

    Yu, Xunzhen; Zhu, Jingjun; Lin, ShinTed; Wang, Li; Xing, Haoyang; Zhang, Caixun; Xia, Yuxi; Liu, Shukui; Yue, Qian; Wei, Weiwei; Du, Qiang; Tang, Changjian

    2015-01-01

    In this study, the combination of the support vector machine (SVM) method with the moment analysis method (MAM) is proposed and utilized to perform neutron/gamma (n/γ) discrimination of the pulses from an organic liquid scintillator (OLS). Neutron and gamma events, which can be firmly separated on the scatter plot drawn by the charge comparison method (CCM), are detected to form the training data set and the test data set for the SVM, and the MAM is used to create the feature vectors for individual events in the data sets. Compared to the traditional methods, such as CCM, the proposed method can not only discriminate the neutron and gamma signals, even at lower energy levels, but also provide the corresponding classification accuracy for each event, which is useful in validating the discrimination. Meanwhile, the proposed method can also offer a predication of the classification for the under-energy-limit events

  13. Scorebox extraction from mobile sports videos using Support Vector Machines

    Science.gov (United States)

    Kim, Wonjun; Park, Jimin; Kim, Changick

    2008-08-01

    Scorebox plays an important role in understanding contents of sports videos. However, the tiny scorebox may give the small-display-viewers uncomfortable experience in grasping the game situation. In this paper, we propose a novel framework to extract the scorebox from sports video frames. We first extract candidates by using accumulated intensity and edge information after short learning period. Since there are various types of scoreboxes inserted in sports videos, multiple attributes need to be used for efficient extraction. Based on those attributes, the optimal information gain is computed and top three ranked attributes in terms of information gain are selected as a three-dimensional feature vector for Support Vector Machines (SVM) to distinguish the scorebox from other candidates, such as logos and advertisement boards. The proposed method is tested on various videos of sports games and experimental results show the efficiency and robustness of our proposed method.

  14. Psychiatric nursing menbers' reflections on participating in group-based clinical supervision

    DEFF Research Database (Denmark)

    Buus, Niels; Angel, Sanne; Traynor, Michael

    2011-01-01

    This paper is a report of an interview study exploring psychiatric hospital nursing staff members' reflections on participating in supervision. Clinical supervision is a pedagogical process designed to direct, develop, and support clinical nurses. Participation rates in clinical supervision...... they influence participation rates. Twenty-two psychiatric hospital nursing staff members were interviewed with a semistructured interview guide. Interview transcripts were interpreted by means of Ricoeur's hermeneutic method. The respondents understood clinical supervision to be beneficial, but with very...

  15. Nurses’ perceptions on nursing supervision in Primary Health Care

    Directory of Open Access Journals (Sweden)

    Beatriz Francisco Farah

    2016-01-01

    Full Text Available Objective: to understand the perceptions of nurses on nursing supervision in the work process. Methods: this is a qualitative research, with a semi-structured interview, performed with 16 nurses. Data analysis was performed through content analysis. Results: two meanings topics emerged from the speeches of the participants: Nurses´ activities in Primary Health Care Units and Nurses´ perceptions about nursing supervision. In the first category, the actions listed were filling out forms and reports under the supervision of the nursing service. In the second category, supervision was perceived as a function of management and follow-up of the activities planned by the team, in opposition to the classical supervision concept, which is inspecting. Conclusion: nursing supervision has been configured for primary care nurses as an administrative function that involves planning, organization, coordination, evaluation, follow-up and support for the health team.

  16. Intelligent Design of Metal Oxide Gas Sensor Arrays Using Reciprocal Kernel Support Vector Regression

    Science.gov (United States)

    Dougherty, Andrew W.

    Metal oxides are a staple of the sensor industry. The combination of their sensitivity to a number of gases, and the electrical nature of their sensing mechanism, make the particularly attractive in solid state devices. The high temperature stability of the ceramic material also make them ideal for detecting combustion byproducts where exhaust temperatures can be high. However, problems do exist with metal oxide sensors. They are not very selective as they all tend to be sensitive to a number of reduction and oxidation reactions on the oxide's surface. This makes sensors with large numbers of sensors interesting to study as a method for introducing orthogonality to the system. Also, the sensors tend to suffer from long term drift for a number of reasons. In this thesis I will develop a system for intelligently modeling metal oxide sensors and determining their suitability for use in large arrays designed to analyze exhaust gas streams. It will introduce prior knowledge of the metal oxide sensors' response mechanisms in order to produce a response function for each sensor from sparse training data. The system will use the same technique to model and remove any long term drift from the sensor response. It will also provide an efficient means for determining the orthogonality of the sensor to determine whether they are useful in gas sensing arrays. The system is based on least squares support vector regression using the reciprocal kernel. The reciprocal kernel is introduced along with a method of optimizing the free parameters of the reciprocal kernel support vector machine. The reciprocal kernel is shown to be simpler and to perform better than an earlier kernel, the modified reciprocal kernel. Least squares support vector regression is chosen as it uses all of the training points and an emphasis was placed throughout this research for extracting the maximum information from very sparse data. The reciprocal kernel is shown to be effective in modeling the sensor

  17. Support Vector Machine Diagnosis of Acute Abdominal Pain

    Science.gov (United States)

    Björnsdotter, Malin; Nalin, Kajsa; Hansson, Lars-Erik; Malmgren, Helge

    This study explores the feasibility of a decision-support system for patients seeking care for acute abdominal pain, and, specifically the diagnosis of acute diverticulitis. We used a linear support vector machine (SVM) to separate diverticulitis from all other reported cases of abdominal pain and from the important differential diagnosis non-specific abdominal pain (NSAP). On a database containing 3337 patients, the SVM obtained results comparable to those of the doctors in separating diverticulitis or NSAP from the remaining diseases. The distinction between diverticulitis and NSAP was, however, substantially improved by the SVM. For this patient group, the doctors achieved a sensitivity of 0.714 and a specificity of 0.963. When adjusted to the physicians' results, the SVM sensitivity/specificity was higher at 0.714/0.985 and 0.786/0.963 respectively. Age was found as the most important discriminative variable, closely followed by C-reactive protein level and lower left side pain.

  18. Regolith-geology mapping with support vector machine: A case study over weathered Ni-bearing peridotites, New Caledonia

    Science.gov (United States)

    De Boissieu, Florian; Sevin, Brice; Cudahy, Thomas; Mangeas, Morgan; Chevrel, Stéphane; Ong, Cindy; Rodger, Andrew; Maurizot, Pierre; Laukamp, Carsten; Lau, Ian; Touraivane, Touraivane; Cluzel, Dominique; Despinoy, Marc

    2018-02-01

    Accurate maps of Earth's geology, especially its regolith, are required for managing the sustainable exploration and development of mineral resources. This paper shows how airborne imaging hyperspectral data collected over weathered peridotite rocks in vegetated, mountainous terrane in New Caledonia were processed using a combination of methods to generate a regolith-geology map that could be used for more efficiently targeting Ni exploration. The image processing combined two usual methods, which are spectral feature extraction and support vector machine (SVM). This rationale being the spectral features extraction can rapidly reduce data complexity by both targeting only the diagnostic mineral absorptions and masking those pixels complicated by vegetation, cloud and deep shade. SVM is a supervised classification method able to generate an optimal non-linear classifier with these features that generalises well even with limited training data. Key minerals targeted are serpentine, which is considered as an indicator for hydrolysed peridotitic rock, and iron oxy-hydroxides (hematite and goethite), which are considered as diagnostic of laterite development. The final classified regolith map was assessed against interpreted regolith field sites, which yielded approximately 70% similarity for all unit types, as well as against a regolith-geology map interpreted using traditional datasets (not hyperspectral imagery). Importantly, the hyperspectral derived mineral map provided much greater detail enabling a more precise understanding of the regolith-geological architecture where there are exposed soils and rocks.

  19. Prediction of hourly PM2.5 using a space-time support vector regression model

    Science.gov (United States)

    Yang, Wentao; Deng, Min; Xu, Feng; Wang, Hang

    2018-05-01

    Real-time air quality prediction has been an active field of research in atmospheric environmental science. The existing methods of machine learning are widely used to predict pollutant concentrations because of their enhanced ability to handle complex non-linear relationships. However, because pollutant concentration data, as typical geospatial data, also exhibit spatial heterogeneity and spatial dependence, they may violate the assumptions of independent and identically distributed random variables in most of the machine learning methods. As a result, a space-time support vector regression model is proposed to predict hourly PM2.5 concentrations. First, to address spatial heterogeneity, spatial clustering is executed to divide the study area into several homogeneous or quasi-homogeneous subareas. To handle spatial dependence, a Gauss vector weight function is then developed to determine spatial autocorrelation variables as part of the input features. Finally, a local support vector regression model with spatial autocorrelation variables is established for each subarea. Experimental data on PM2.5 concentrations in Beijing are used to verify whether the results of the proposed model are superior to those of other methods.

  20. DuSK: A Dual Structure-preserving Kernel for Supervised Tensor Learning with Applications to Neuroimages.

    Science.gov (United States)

    He, Lifang; Kong, Xiangnan; Yu, Philip S; Ragin, Ann B; Hao, Zhifeng; Yang, Xiaowei

    With advances in data collection technologies, tensor data is assuming increasing prominence in many applications and the problem of supervised tensor learning has emerged as a topic of critical significance in the data mining and machine learning community. Conventional methods for supervised tensor learning mainly focus on learning kernels by flattening the tensor into vectors or matrices, however structural information within the tensors will be lost. In this paper, we introduce a new scheme to design structure-preserving kernels for supervised tensor learning. Specifically, we demonstrate how to leverage the naturally available structure within the tensorial representation to encode prior knowledge in the kernel. We proposed a tensor kernel that can preserve tensor structures based upon dual-tensorial mapping. The dual-tensorial mapping function can map each tensor instance in the input space to another tensor in the feature space while preserving the tensorial structure. Theoretically, our approach is an extension of the conventional kernels in the vector space to tensor space. We applied our novel kernel in conjunction with SVM to real-world tensor classification problems including brain fMRI classification for three different diseases ( i.e ., Alzheimer's disease, ADHD and brain damage by HIV). Extensive empirical studies demonstrate that our proposed approach can effectively boost tensor classification performances, particularly with small sample sizes.

  1. The Emergence of Family-specific Support Constructs: Cross-level Effects of Family-supportive Supervision and Family-Supportive Organization Perceptions on Individual Outcomes.

    Science.gov (United States)

    Hill, Rachel T; Matthews, Russell A; Walsh, Benjamin M

    2016-12-01

    Implicit to the definitions of both family-supportive supervision (FSS) and family-supportive organization perceptions (FSOP) is the argument that these constructs may manifest at a higher (e.g. group or organizational) level. In line with these conceptualizations, grounded in tenants of conservation of resources theory, we argue that FSS and FSOP, as universal resources, are emergent constructs at the organizational level, which have cross-level effects on work-family conflict and turnover intentions. To test our theoretically derived hypotheses, a multilevel model was examined in which FSS and FSOP at the unit level predict individual work-to-family conflict, which in turn predicts turnover intentions. Our hypothesized model was generally supported. Collectively, our results point to FSOP serving as an explanatory mechanism of the effects that mutual perceptions of FSS have on individual experiences of work-to-family conflict and turnover intentions. Lagged (i.e. overtime) cross-level effects of the model were also confirmed in supplementary analyses. Our results extend our theoretical understanding of FSS and FSOP by demonstrating the utility of conceptualizing them as universal resources, opening up a variety of avenues for future research. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

  2. Who attends clinical supervision? The uptake of clinical supervision by hospital nurses.

    Science.gov (United States)

    Koivu, Aija; Hyrkäs, Kristiina; Saarinen, Pirjo Irmeli

    2011-01-01

    The aim of the present study was to identify which nurses decide to participate in clinical supervision (CS) when it is provided for all nursing staff. Clinical supervision is available today for health care providers in many organisations. However, regardless of evidence showing the benefits of CS, some providers decide not to participate in the sessions. A baseline survey on work and health issues was conducted in 2003 with a 3-year follow-up of the uptake of CS by the respondents. Background characteristics and perceptions of work and health were compared between medical and surgical nurses who had undertaken CS (n=124) and their peers who decided not to undertake it (n=204). Differences in the perceptions of work and dimensions of burnout were found between the two groups. Nurses attracted to CS form a distinctive group in the unit, standing out as self-confident, committed and competent professionals supported by empowering and fair leadership. Facilitating clinical supervision for committed and innovative nurses may be seen as part of the empowering leadership of the nurse manager. © 2010 The Authors. Journal compilation © 2010 Blackwell Publishing Ltd.

  3. T-wave end detection using neural networks and Support Vector Machines.

    Science.gov (United States)

    Suárez-León, Alexander Alexeis; Varon, Carolina; Willems, Rik; Van Huffel, Sabine; Vázquez-Seisdedos, Carlos Román

    2018-05-01

    In this paper we propose a new approach for detecting the end of the T-wave in the electrocardiogram (ECG) using Neural Networks and Support Vector Machines. Both, Multilayer Perceptron (MLP) neural networks and Fixed-Size Least-Squares Support Vector Machines (FS-LSSVM) were used as regression algorithms to determine the end of the T-wave. Different strategies for selecting the training set such as random selection, k-means, robust clustering and maximum quadratic (Rényi) entropy were evaluated. Individual parameters were tuned for each method during training and the results are given for the evaluation set. A comparison between MLP and FS-LSSVM approaches was performed. Finally, a fair comparison of the FS-LSSVM method with other state-of-the-art algorithms for detecting the end of the T-wave was included. The experimental results show that FS-LSSVM approaches are more suitable as regression algorithms than MLP neural networks. Despite the small training sets used, the FS-LSSVM methods outperformed the state-of-the-art techniques. FS-LSSVM can be successfully used as a T-wave end detection algorithm in ECG even with small training set sizes. Copyright © 2018 Elsevier Ltd. All rights reserved.

  4. Automatic Modulation Recognition by Support Vector Machines Using Wavelet Kernel

    Energy Technology Data Exchange (ETDEWEB)

    Feng, X Z; Yang, J; Luo, F L; Chen, J Y; Zhong, X P [College of Mechatronic Engineering and Automation, National University of Defense Technology, Changsha (China)

    2006-10-15

    Automatic modulation identification plays a significant role in electronic warfare, electronic surveillance systems and electronic counter measure. The task of modulation recognition of communication signals is to determine the modulation type and signal parameters. In fact, automatic modulation identification can be range to an application of pattern recognition in communication field. The support vector machines (SVM) is a new universal learning machine which is widely used in the fields of pattern recognition, regression estimation and probability density. In this paper, a new method using wavelet kernel function was proposed, which maps the input vector xi into a high dimensional feature space F. In this feature space F, we can construct the optimal hyperplane that realizes the maximal margin in this space. That is to say, we can use SVM to classify the communication signals into two groups, namely analogue modulated signals and digitally modulated signals. In addition, computer simulation results are given at last, which show good performance of the method.

  5. Automatic Modulation Recognition by Support Vector Machines Using Wavelet Kernel

    International Nuclear Information System (INIS)

    Feng, X Z; Yang, J; Luo, F L; Chen, J Y; Zhong, X P

    2006-01-01

    Automatic modulation identification plays a significant role in electronic warfare, electronic surveillance systems and electronic counter measure. The task of modulation recognition of communication signals is to determine the modulation type and signal parameters. In fact, automatic modulation identification can be range to an application of pattern recognition in communication field. The support vector machines (SVM) is a new universal learning machine which is widely used in the fields of pattern recognition, regression estimation and probability density. In this paper, a new method using wavelet kernel function was proposed, which maps the input vector xi into a high dimensional feature space F. In this feature space F, we can construct the optimal hyperplane that realizes the maximal margin in this space. That is to say, we can use SVM to classify the communication signals into two groups, namely analogue modulated signals and digitally modulated signals. In addition, computer simulation results are given at last, which show good performance of the method

  6. Applying active learning to supervised word sense disambiguation in MEDLINE.

    Science.gov (United States)

    Chen, Yukun; Cao, Hongxin; Mei, Qiaozhu; Zheng, Kai; Xu, Hua

    2013-01-01

    This study was to assess whether active learning strategies can be integrated with supervised word sense disambiguation (WSD) methods, thus reducing the number of annotated samples, while keeping or improving the quality of disambiguation models. We developed support vector machine (SVM) classifiers to disambiguate 197 ambiguous terms and abbreviations in the MSH WSD collection. Three different uncertainty sampling-based active learning algorithms were implemented with the SVM classifiers and were compared with a passive learner (PL) based on random sampling. For each ambiguous term and each learning algorithm, a learning curve that plots the accuracy computed from the test set as a function of the number of annotated samples used in the model was generated. The area under the learning curve (ALC) was used as the primary metric for evaluation. Our experiments demonstrated that active learners (ALs) significantly outperformed the PL, showing better performance for 177 out of 197 (89.8%) WSD tasks. Further analysis showed that to achieve an average accuracy of 90%, the PL needed 38 annotated samples, while the ALs needed only 24, a 37% reduction in annotation effort. Moreover, we analyzed cases where active learning algorithms did not achieve superior performance and identified three causes: (1) poor models in the early learning stage; (2) easy WSD cases; and (3) difficult WSD cases, which provide useful insight for future improvements. This study demonstrated that integrating active learning strategies with supervised WSD methods could effectively reduce annotation cost and improve the disambiguation models.

  7. Efficient Multiplicative Updates for Support Vector Machines

    DEFF Research Database (Denmark)

    Potluru, Vamsi K.; Plis, Sergie N; Mørup, Morten

    2009-01-01

    (NMF) problem. This allows us to derive a novel multiplicative algorithm for solving hard and soft margin SVM. The algorithm follows as a natural extension of the updates for NMF and semi-NMF. No additional parameter setting, such as choosing learning rate, is required. Exploiting the connection......The dual formulation of the support vector machine (SVM) objective function is an instance of a nonnegative quadratic programming problem. We reformulate the SVM objective function as a matrix factorization problem which establishes a connection with the regularized nonnegative matrix factorization...... between SVM and NMF formulation, we show how NMF algorithms can be applied to the SVM problem. Multiplicative updates that we derive for SVM problem also represent novel updates for semi-NMF. Further this unified view yields algorithmic insights in both directions: we demonstrate that the Kernel Adatron...

  8. Optimization of safety production supervision mode of coalmining enterprises

    Energy Technology Data Exchange (ETDEWEB)

    Zhou, M.; Xiao, Z. [China University of Mining and Technology, Xuzhou (China). School of Management

    2005-12-01

    In view of the fact that safety production supervision of coal mines in China features low efficacy, this paper applies principles of cybernetics to simulate the dynamic process of safety supervision, and proposes that institutional variables be controlled to support intermediate goals, which in turn contribute to the ultimate safety production objective. Rather than focussing all attention on safety issues of working faces, supervising departments of coalmines are advised to pay much more attention to institutional factors that may impact people's attitude and behavior, which are responsible for most coalmine accidents. It is believed that such a shift of attention can effectively reduce coalmining production accidents and greatly enhance supervision efficacy. 8 refs., 5 figs.

  9. Shallow water bathymetry mapping using Support Vector Machine (SVM) technique and multispectral imagery

    NARCIS (Netherlands)

    Misra, Ankita; Vojinovic, Zoran; Ramakrishnan, Balaji; Luijendijk, Arjen; Ranasinghe, Roshanka

    2018-01-01

    Satellite imagery along with image processing techniques prove to be efficient tools for bathymetry retrieval as they provide time and cost-effective alternatives to traditional methods of water depth estimation. In this article, a nonlinear machine learning technique of Support Vector Machine (SVM)

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

    Science.gov (United States)

    Fatehi, Moslem; Asadi, Hooshang H.

    2017-04-01

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

  11. Fast Monte Carlo reliability evaluation using support vector machine

    International Nuclear Information System (INIS)

    Rocco, Claudio M.; Moreno, Jose Ali

    2002-01-01

    This paper deals with the feasibility of using support vector machine (SVM) to build empirical models for use in reliability evaluation. The approach takes advantage of the speed of SVM in the numerous model calculations typically required to perform a Monte Carlo reliability evaluation. The main idea is to develop an estimation algorithm, by training a model on a restricted data set, and replace system performance evaluation by a simpler calculation, which provides reasonably accurate model outputs. The proposed approach is illustrated by several examples. Excellent system reliability results are obtained by training a SVM with a small amount of information

  12. Exploring Organizational Barriers to Strengthening Clinical Supervision of Psychiatric Nursing Staff

    DEFF Research Database (Denmark)

    Gonge, Henrik; Buus, Niels

    2016-01-01

    This article reports findings from a longitudinal controlled intervention study of 115 psychiatric nursing staff. The twofold objective of the study was: (a) To test whether the intervention could increase clinical supervision participation and effectiveness of existing supervision practices, and...... in the experienced effectiveness of supervision. It is concluded that organizational support is an imperative for implementation of clinical supervision......., and (b) To explore organizational constraints to implementation of these strengthened practices. Questionnaire responses and registration of participation in clinical supervision were registered prior and subsequent to the intervention consisting of an action learning oriented reflection on staff......'s existing clinical supervision practices. Major organizational changes in the intervention group during the study period obstructed the implementation of strengthened clinical supervision practices, but offered an opportunity for studying the influences of organizational constraints. The main findings were...

  13. Predicting respiratory tumor motion with multi-dimensional adaptive filters and support vector regression

    International Nuclear Information System (INIS)

    Riaz, Nadeem; Wiersma, Rodney; Mao Weihua; Xing Lei; Shanker, Piyush; Gudmundsson, Olafur; Widrow, Bernard

    2009-01-01

    Intra-fraction tumor tracking methods can improve radiation delivery during radiotherapy sessions. Image acquisition for tumor tracking and subsequent adjustment of the treatment beam with gating or beam tracking introduces time latency and necessitates predicting the future position of the tumor. This study evaluates the use of multi-dimensional linear adaptive filters and support vector regression to predict the motion of lung tumors tracked at 30 Hz. We expand on the prior work of other groups who have looked at adaptive filters by using a general framework of a multiple-input single-output (MISO) adaptive system that uses multiple correlated signals to predict the motion of a tumor. We compare the performance of these two novel methods to conventional methods like linear regression and single-input, single-output adaptive filters. At 400 ms latency the average root-mean-square-errors (RMSEs) for the 14 treatment sessions studied using no prediction, linear regression, single-output adaptive filter, MISO and support vector regression are 2.58, 1.60, 1.58, 1.71 and 1.26 mm, respectively. At 1 s, the RMSEs are 4.40, 2.61, 3.34, 2.66 and 1.93 mm, respectively. We find that support vector regression most accurately predicts the future tumor position of the methods studied and can provide a RMSE of less than 2 mm at 1 s latency. Also, a multi-dimensional adaptive filter framework provides improved performance over single-dimension adaptive filters. Work is underway to combine these two frameworks to improve performance.

  14. Beginning therapists’ experiences of what constitutes good and bad psychotherapy supervision

    DEFF Research Database (Denmark)

    Jacobsen, Claus Haugaard; Pedersen, Lene Tanggaard

    2009-01-01

    events. Also included were the importance of peers in the supervision group and the organisational setting of the supervision. The objective was to give detailed descriptions in the form of condensed narratives of each student’s preferences concerning supervision. Furthermore, a cross-sectional analysis...... in the existing literature in the field. The beginning therapist prefer supervision in which advice and clear and specific instructions are given on how to do the job, where theoretical considerations are included, and the supervisor supports, affirms and structures the sessions. However, of particular interest...

  15. FUSION DECISION FOR A BIMODAL BIOMETRIC VERIFICATION SYSTEM USING SUPPORT VECTOR MACHINE AND ITS VARIATIONS

    Directory of Open Access Journals (Sweden)

    A. Teoh

    2017-12-01

    Full Text Available This paw presents fusion detection technique comparisons based on support vector machine and its variations for a bimodal biometric verification system that makes use of face images and speech utterances. The system is essentially constructed by a face expert, a speech expert and a fusion decision module. Each individual expert has been optimized to operate in automatic mode and designed for security access application. Fusion decision schemes considered are linear, weighted Support Vector Machine (SVM and linear SVM with quadratic transformation. The conditions tested include the balanced and unbalanced conditions between the two experts in order to obtain the optimum fusion module from  these techniques best suited to the target application.

  16. Research on image evidence in land supervision and GIS management

    Science.gov (United States)

    Li, Qiu; Wu, Lixin

    2006-10-01

    Land resource development and utilization brings many problems. The numbers, the scale and volume of illegal land use cases are on the increasing. Since the territory is vast, and the land violations are concealment, it is difficulty for an effective land supervision and management. In this paper, the concepts of evidence, and preservation of evidence were described first. The concepts of image evidence (IE), natural evidence (NE), natural preservation of evidence (NPE), general preservation of evidence (GPE) were proposed based on the characteristics of remote sensing image (RSI) which has a characteristic of objectiveness, truthfulness, high spatial resolution, more information included. Using MapObjects and Visual Basic 6.0, under the Access management to implement the conjunction of spatial vector database and attribute data table; taking RSI as the data sources and background layer; combining the powerful management of geographic information system (GIS) for spatial data, and visual analysis, a land supervision and GIS management system was design and implemented based on NPE. The practical use in Beijing shows that the system is running well, and solved some problems in land supervision and management.

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

    Directory of Open Access Journals (Sweden)

    Brian R. King

    2008-01-01

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

  18. Evolutionary-driven support vector machines for determining the degree of liver fibrosis in chronic hepatitis C.

    Science.gov (United States)

    Stoean, Ruxandra; Stoean, Catalin; Lupsor, Monica; Stefanescu, Horia; Badea, Radu

    2011-01-01

    Hepatic fibrosis, the principal pointer to the development of a liver disease within chronic hepatitis C, can be measured through several stages. The correct evaluation of its degree, based on recent different non-invasive procedures, is of current major concern. The latest methodology for assessing it is the Fibroscan and the effect of its employment is impressive. However, the complex interaction between its stiffness indicator and the other biochemical and clinical examinations towards a respective degree of liver fibrosis is hard to be manually discovered. In this respect, the novel, well-performing evolutionary-powered support vector machines are proposed towards an automated learning of the relationship between medical attributes and fibrosis levels. The traditional support vector machines have been an often choice for addressing hepatic fibrosis, while the evolutionary option has been validated on many real-world tasks and proven flexibility and good performance. The evolutionary approach is simple and direct, resulting from the hybridization of the learning component within support vector machines and the optimization engine of evolutionary algorithms. It discovers the optimal coefficients of surfaces that separate instances of distinct classes. Apart from a detached manner of establishing the fibrosis degree for new cases, a resulting formula also offers insight upon the correspondence between the medical factors and the respective outcome. What is more, a feature selection genetic algorithm can be further embedded into the method structure, in order to dynamically concentrate search only on the most relevant attributes. The data set refers 722 patients with chronic hepatitis C infection and 24 indicators. The five possible degrees of fibrosis range from F0 (no fibrosis) to F4 (cirrhosis). Since the standard support vector machines are among the most frequently used methods in recent artificial intelligence studies for hepatic fibrosis staging, the

  19. Application of Support Vector Machine to Forex Monitoring

    Science.gov (United States)

    Kamruzzaman, Joarder; Sarker, Ruhul A.

    Previous studies have demonstrated superior performance of artificial neural network (ANN) based forex forecasting models over traditional regression models. This paper applies support vector machines to build a forecasting model from the historical data using six simple technical indicators and presents a comparison with an ANN based model trained by scaled conjugate gradient (SCG) learning algorithm. The models are evaluated and compared on the basis of five commonly used performance metrics that measure closeness of prediction as well as correctness in directional change. Forecasting results of six different currencies against Australian dollar reveal superior performance of SVM model using simple linear kernel over ANN-SCG model in terms of all the evaluation metrics. The effect of SVM parameter selection on prediction performance is also investigated and analyzed.

  20. Accuracy Analysis Comparison of Supervised Classification Methods for Anomaly Detection on Levees Using SAR Imagery

    Directory of Open Access Journals (Sweden)

    Ramakalavathi Marapareddy

    2017-10-01

    Full Text Available This paper analyzes the use of a synthetic aperture radar (SAR imagery to support levee condition assessment by detecting potential slide areas in an efficient and cost-effective manner. Levees are prone to a failure in the form of internal erosion within the earthen structure and landslides (also called slough or slump slides. If not repaired, slough slides may lead to levee failures. In this paper, we compare the accuracy of the supervised classification methods minimum distance (MD using Euclidean and Mahalanobis distance, support vector machine (SVM, and maximum likelihood (ML, using SAR technology to detect slough slides on earthen levees. In this work, the effectiveness of the algorithms was demonstrated using quad-polarimetric L-band SAR imagery from the NASA Jet Propulsion Laboratory’s (JPL’s uninhabited aerial vehicle synthetic aperture radar (UAVSAR. The study area is a section of the lower Mississippi River valley in the Southern USA, where earthen flood control levees are maintained by the US Army Corps of Engineers.

  1. Effectiveness of Group Supervision versus Combined Group and Individual Supervision.

    Science.gov (United States)

    Ray, Dee; Altekruse, Michael

    2000-01-01

    Investigates the effectiveness of different types of supervision (large group, small group, combined group, individual supervision) with counseling students (N=64). Analyses revealed that all supervision formats resulted in similar progress in counselor effectiveness and counselor development. Participants voiced a preference for individual…

  2. Comparative Analysis of River Flow Modelling by Using Supervised Learning Technique

    Science.gov (United States)

    Ismail, Shuhaida; Mohamad Pandiahi, Siraj; Shabri, Ani; Mustapha, Aida

    2018-04-01

    The goal of this research is to investigate the efficiency of three supervised learning algorithms for forecasting monthly river flow of the Indus River in Pakistan, spread over 550 square miles or 1800 square kilometres. The algorithms include the Least Square Support Vector Machine (LSSVM), Artificial Neural Network (ANN) and Wavelet Regression (WR). The forecasting models predict the monthly river flow obtained from the three models individually for river flow data and the accuracy of the all models were then compared against each other. The monthly river flow of the said river has been forecasted using these three models. The obtained results were compared and statistically analysed. Then, the results of this analytical comparison showed that LSSVM model is more precise in the monthly river flow forecasting. It was found that LSSVM has he higher r with the value of 0.934 compared to other models. This indicate that LSSVM is more accurate and efficient as compared to the ANN and WR model.

  3. A novel representation for apoptosis protein subcellular localization prediction using support vector machine.

    Science.gov (United States)

    Zhang, Li; Liao, Bo; Li, Dachao; Zhu, Wen

    2009-07-21

    Apoptosis, or programmed cell death, plays an important role in development of an organism. Obtaining information on subcellular location of apoptosis proteins is very helpful to understand the apoptosis mechanism. In this paper, based on the concept that the position distribution information of amino acids is closely related with the structure and function of proteins, we introduce the concept of distance frequency [Matsuda, S., Vert, J.P., Ueda, N., Toh, H., Akutsu, T., 2005. A novel representation of protein sequences for prediction of subcellular location using support vector machines. Protein Sci. 14, 2804-2813] and propose a novel way to calculate distance frequencies. In order to calculate the local features, each protein sequence is separated into p parts with the same length in our paper. Then we use the novel representation of protein sequences and adopt support vector machine to predict subcellular location. The overall prediction accuracy is significantly improved by jackknife test.

  4. Support Vector Regression-Based Adaptive Divided Difference Filter for Nonlinear State Estimation Problems

    Directory of Open Access Journals (Sweden)

    Hongjian Wang

    2014-01-01

    Full Text Available We present a support vector regression-based adaptive divided difference filter (SVRADDF algorithm for improving the low state estimation accuracy of nonlinear systems, which are typically affected by large initial estimation errors and imprecise prior knowledge of process and measurement noises. The derivative-free SVRADDF algorithm is significantly simpler to compute than other methods and is implemented using only functional evaluations. The SVRADDF algorithm involves the use of the theoretical and actual covariance of the innovation sequence. Support vector regression (SVR is employed to generate the adaptive factor to tune the noise covariance at each sampling instant when the measurement update step executes, which improves the algorithm’s robustness. The performance of the proposed algorithm is evaluated by estimating states for (i an underwater nonmaneuvering target bearing-only tracking system and (ii maneuvering target bearing-only tracking in an air-traffic control system. The simulation results show that the proposed SVRADDF algorithm exhibits better performance when compared with a traditional DDF algorithm.

  5. Modeling and Forecast Biological Oxygen Demand (BOD using Combination Support Vector Machine with Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Abazar Solgi

    2017-06-01

    given from Fourier transform that was introduced in the nineteenth-century. Overall, concept of wavelet transform for current theory was presented by Morlet and a team under the supervision of Alex Grossman at the Research Center for Theoretical Physics Marcel in France. After the parameters decomposition using wavelet analysis and using principal component analysis (PCA, the main components were determined. These components are then used as input to the support vector machine model to obtain a hybrid model of Wavelet-SVM (WSVM. For this study, a series of monthly of BOD in Karun River in Molasani station and auxiliary variables dissolved oxygen (DO, temperature and monthly river flow in a 13 years period (2002-2014 were used. Results and Discussion: To run the SVM model, seven different combinations were evaluated. Combination 6 which was contained of 4 parameters including BOD, dissolved oxygen (DO, temperature and monthly river flow with a time lag have best performance. The best structure had RMSE equal to 0.0338 and the coefficient of determination equal to 0.84. For achieving the results of the WSVM, the wavelet transform and input parameters were decomposed to sub-signal, then this sub-signals were studied with Principal component analysis (PCA method and important components were entered as inputs to SVM model to obtain the hybrid model WSVM. After numerous run this program in certain modes and compare them with each other, the results was obtained. One of the key points about the choice of the mother wavelet is the time series. So, the patterns of the mother wavelet functions that can better adapt to diagram curved of time series can do the mappings operation and therefore will have better results. In this study, according to different wavelet tests and according to the above note, four types of mother wavelet functions Haar, Db2, Db7 and Sym3 were selected. Conclusions: Compare the results of the monthly modeling indicate that the use of wavelet transforms can

  6. DuSK: A Dual Structure-preserving Kernel for Supervised Tensor Learning with Applications to Neuroimages

    Science.gov (United States)

    He, Lifang; Kong, Xiangnan; Yu, Philip S.; Ragin, Ann B.; Hao, Zhifeng; Yang, Xiaowei

    2015-01-01

    With advances in data collection technologies, tensor data is assuming increasing prominence in many applications and the problem of supervised tensor learning has emerged as a topic of critical significance in the data mining and machine learning community. Conventional methods for supervised tensor learning mainly focus on learning kernels by flattening the tensor into vectors or matrices, however structural information within the tensors will be lost. In this paper, we introduce a new scheme to design structure-preserving kernels for supervised tensor learning. Specifically, we demonstrate how to leverage the naturally available structure within the tensorial representation to encode prior knowledge in the kernel. We proposed a tensor kernel that can preserve tensor structures based upon dual-tensorial mapping. The dual-tensorial mapping function can map each tensor instance in the input space to another tensor in the feature space while preserving the tensorial structure. Theoretically, our approach is an extension of the conventional kernels in the vector space to tensor space. We applied our novel kernel in conjunction with SVM to real-world tensor classification problems including brain fMRI classification for three different diseases (i.e., Alzheimer's disease, ADHD and brain damage by HIV). Extensive empirical studies demonstrate that our proposed approach can effectively boost tensor classification performances, particularly with small sample sizes. PMID:25927014

  7. Kollegial supervision

    DEFF Research Database (Denmark)

    Andersen, Ole Dibbern; Petersson, Erling

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

  8. Ranking Medical Terms to Support Expansion of Lay Language Resources for Patient Comprehension of Electronic Health Record Notes: Adapted Distant Supervision Approach.

    Science.gov (United States)

    Chen, Jinying; Jagannatha, Abhyuday N; Fodeh, Samah J; Yu, Hong

    2017-10-31

    Medical terms are a major obstacle for patients to comprehend their electronic health record (EHR) notes. Clinical natural language processing (NLP) systems that link EHR terms to lay terms or definitions allow patients to easily access helpful information when reading through their EHR notes, and have shown to improve patient EHR comprehension. However, high-quality lay language resources for EHR terms are very limited in the public domain. Because expanding and curating such a resource is a costly process, it is beneficial and even necessary to identify terms important for patient EHR comprehension first. We aimed to develop an NLP system, called adapted distant supervision (ADS), to rank candidate terms mined from EHR corpora. We will give EHR terms ranked as high by ADS a higher priority for lay language annotation-that is, creating lay definitions for these terms. Adapted distant supervision uses distant supervision from consumer health vocabulary and transfer learning to adapt itself to solve the problem of ranking EHR terms in the target domain. We investigated 2 state-of-the-art transfer learning algorithms (ie, feature space augmentation and supervised distant supervision) and designed 5 types of learning features, including distributed word representations learned from large EHR data for ADS. For evaluating ADS, we asked domain experts to annotate 6038 candidate terms as important or nonimportant for EHR comprehension. We then randomly divided these data into the target-domain training data (1000 examples) and the evaluation data (5038 examples). We compared ADS with 2 strong baselines, including standard supervised learning, on the evaluation data. The ADS system using feature space augmentation achieved the best average precision, 0.850, on the evaluation set when using 1000 target-domain training examples. The ADS system using supervised distant supervision achieved the best average precision, 0.819, on the evaluation set when using only 100 target

  9. Supervised Classification Processes for the Characterization of Heritage Elements, Case Study: Cuenca-Ecuador

    Science.gov (United States)

    Briones, J. C.; Heras, V.; Abril, C.; Sinchi, E.

    2017-08-01

    The proper control of built heritage entails many challenges related to the complexity of heritage elements and the extent of the area to be managed, for which the available resources must be efficiently used. In this scenario, the preventive conservation approach, based on the concept that prevent is better than cure, emerges as a strategy to avoid the progressive and imminent loss of monuments and heritage sites. Regular monitoring appears as a key tool to identify timely changes in heritage assets. This research demonstrates that the supervised learning model (Support Vector Machines - SVM) is an ideal tool that supports the monitoring process detecting visible elements in aerial images such as roofs structures, vegetation and pavements. The linear, gaussian and polynomial kernel functions were tested; the lineal function provided better results over the other functions. It is important to mention that due to the high level of segmentation generated by the classification procedure, it was necessary to apply a generalization process through opening a mathematical morphological operation, which simplified the over classification for the monitored elements.

  10. Supervision to Enhance Educational and Vocational Guidance Practice: A Review

    Science.gov (United States)

    Reid, Hazel L.

    2010-01-01

    Supervision to support the work of career practitioners is evident in many countries, but is not universal. This author presents a literature review, intending to emphasise the prime importance of developing supervision for guidance work. The author also considers the issues facing those training to develop the role of supervisors in southeast…

  11. Output-only modal parameter estimator of linear time-varying structural systems based on vector TAR model and least squares support vector machine

    Science.gov (United States)

    Zhou, Si-Da; Ma, Yuan-Chen; Liu, Li; Kang, Jie; Ma, Zhi-Sai; Yu, Lei

    2018-01-01

    Identification of time-varying modal parameters contributes to the structural health monitoring, fault detection, vibration control, etc. of the operational time-varying structural systems. However, it is a challenging task because there is not more information for the identification of the time-varying systems than that of the time-invariant systems. This paper presents a vector time-dependent autoregressive model and least squares support vector machine based modal parameter estimator for linear time-varying structural systems in case of output-only measurements. To reduce the computational cost, a Wendland's compactly supported radial basis function is used to achieve the sparsity of the Gram matrix. A Gamma-test-based non-parametric approach of selecting the regularization factor is adapted for the proposed estimator to replace the time-consuming n-fold cross validation. A series of numerical examples have illustrated the advantages of the proposed modal parameter estimator on the suppression of the overestimate and the short data. A laboratory experiment has further validated the proposed estimator.

  12. Screw Remaining Life Prediction Based on Quantum Genetic Algorithm and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Xiaochen Zhang

    2017-01-01

    Full Text Available To predict the remaining life of ball screw, a screw remaining life prediction method based on quantum genetic algorithm (QGA and support vector machine (SVM is proposed. A screw accelerated test bench is introduced. Accelerometers are installed to monitor the performance degradation of ball screw. Combined with wavelet packet decomposition and isometric mapping (Isomap, the sensitive feature vectors are obtained and stored in database. Meanwhile, the sensitive feature vectors are randomly chosen from the database and constitute training samples and testing samples. Then the optimal kernel function parameter and penalty factor of SVM are searched with the method of QGA. Finally, the training samples are used to train optimized SVM while testing samples are adopted to test the prediction accuracy of the trained SVM so the screw remaining life prediction model can be got. The experiment results show that the screw remaining life prediction model could effectively predict screw remaining life.

  13. Wormholes admitting conformal Killing vectors and supported by generalized Chaplygin gas

    Energy Technology Data Exchange (ETDEWEB)

    Kuhfittig, Peter K.F. [Milwaukee School of Engineering, Department of Mathematics, Milwaukee, WI (United States)

    2015-08-15

    When Morris and Thorne first proposed that traversable wormholes may be actual physical objects, they concentrated on the geometry by specifying the shape and redshift functions. This mathematical approach necessarily raises questions regarding the determination of the required stress-energy tensor. This paper discusses a natural way to obtain a complete wormhole solution by assuming that the wormhole (1) is supported by generalized Chaplygin gas and (2) admits conformal Killing vectors. (orig.)

  14. ROBUSTNESS OF A FACE-RECOGNITION TECHNIQUE BASED ON SUPPORT VECTOR MACHINES

    OpenAIRE

    Prashanth Harshangi; Koshy George

    2010-01-01

    The ever-increasing requirements of security concerns have placed a greater demand for face recognition surveillance systems. However, most current face recognition techniques are not quite robust with respect to factors such as variable illumination, facial expression and detail, and noise in images. In this paper, we demonstrate that face recognition using support vector machines are sufficiently robust to different kinds of noise, does not require image pre-processing, and can be used with...

  15. Water demand prediction using artificial neural networks and support vector regression

    CSIR Research Space (South Africa)

    Msiza, IS

    2008-11-01

    Full Text Available Neural Networks and Support Vector Regression Ishmael S. Msiza1, Fulufhelo V. Nelwamondo1,2, Tshilidzi Marwala3 . 1Modelling and Digital Science, CSIR, Johannesburg,SOUTH AFRICA 2Graduate School of Arts and Sciences, Harvard University, Cambridge..., Massachusetts, USA 3School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, SOUTH AFRICA Email: imsiza@csir.co.za, nelwamon@fas.harvard.edu, tshilidzi.marwala@wits.ac.za Abstract— Computational Intelligence techniques...

  16. Optimization of Support Vector Machine (SVM) for Object Classification

    Science.gov (United States)

    Scholten, Matthew; Dhingra, Neil; Lu, Thomas T.; Chao, Tien-Hsin

    2012-01-01

    The Support Vector Machine (SVM) is a powerful algorithm, useful in classifying data into species. The SVMs implemented in this research were used as classifiers for the final stage in a Multistage Automatic Target Recognition (ATR) system. A single kernel SVM known as SVMlight, and a modified version known as a SVM with K-Means Clustering were used. These SVM algorithms were tested as classifiers under varying conditions. Image noise levels varied, and the orientation of the targets changed. The classifiers were then optimized to demonstrate their maximum potential as classifiers. Results demonstrate the reliability of SVM as a method for classification. From trial to trial, SVM produces consistent results.

  17. Modeling DNA affinity landscape through two-round support vector regression with weighted degree kernels

    KAUST Repository

    Wang, Xiaolei; Kuwahara, Hiroyuki; Gao, Xin

    2014-01-01

    high-quality estimates of such complex affinity landscapes is, thus, essential to the control of gene expression and the advance of synthetic biology. Results: Here, we propose a two-round prediction method that is based on support vector regression

  18. Virtual reality technology for radiation visualisation during the remediation at Andreeva Bay in Russia as support to operational and regulatory supervision - 59400

    International Nuclear Information System (INIS)

    Mark, Niels-Kristian; Sneve, Malgorzata

    2012-01-01

    Document available in abstract form only. Full text of publication follows: The Norwegian Radiation Protection Authorities (NRPA) has since 1996 carried out a program of regulatory support projects, funded by the Norwegian Ministry of Foreign Affairs (NMFA) The objective of the program has been to support the Russian civilian and military regulators, the Federal Medical Biological Authorities of Russia (FMBA) and the Federal Environmental, Industrial and Nuclear Supervision Service of Russia, as well as the corresponding nuclear and radiation safety authority within the Russian Ministry of Defense with development and implementation of methods and tools for better regulatory supervision of the Andreeva Bay and Gremikha Sites of Temporary Storage (STS) for Spent Nuclear Fuel (SNF) and Radioactive Waste (RW). In 2005-2009 regulatory investigations were completed at the Andreeva Bay and Gremikha within the collaborative NRPAFMBA program. The radiation and radio-ecological situation was assessed, and criteria and regulations of the STS remediation were developed, as well as the guidance for their application for three possible options of environmental remediation (conservation, conversion and liquidation). As part of the project the Federal Medical-Biological Centre, a technical support organisation to FMBA, developed the DOSEMAP and DATAMAP applications for the FMBA, supporting radiation risk monitoring, worker radiation exposure control

  19. Pair- ${v}$ -SVR: A Novel and Efficient Pairing nu-Support Vector Regression Algorithm.

    Science.gov (United States)

    Hao, Pei-Yi

    This paper proposes a novel and efficient pairing nu-support vector regression (pair--SVR) algorithm that combines successfully the superior advantages of twin support vector regression (TSVR) and classical -SVR algorithms. In spirit of TSVR, the proposed pair--SVR solves two quadratic programming problems (QPPs) of smaller size rather than a single larger QPP, and thus has faster learning speed than classical -SVR. The significant advantage of our pair--SVR over TSVR is the improvement in the prediction speed and generalization ability by introducing the concepts of the insensitive zone and the regularization term that embodies the essence of statistical learning theory. Moreover, pair--SVR has additional advantage of using parameter for controlling the bounds on fractions of SVs and errors. Furthermore, the upper bound and lower bound functions of the regression model estimated by pair--SVR capture well the characteristics of data distributions, thus facilitating automatic estimation of the conditional mean and predictive variance simultaneously. This may be useful in many cases, especially when the noise is heteroscedastic and depends strongly on the input values. The experimental results validate the superiority of our pair--SVR in both training/prediction speed and generalization ability.This paper proposes a novel and efficient pairing nu-support vector regression (pair--SVR) algorithm that combines successfully the superior advantages of twin support vector regression (TSVR) and classical -SVR algorithms. In spirit of TSVR, the proposed pair--SVR solves two quadratic programming problems (QPPs) of smaller size rather than a single larger QPP, and thus has faster learning speed than classical -SVR. The significant advantage of our pair--SVR over TSVR is the improvement in the prediction speed and generalization ability by introducing the concepts of the insensitive zone and the regularization term that embodies the essence of statistical learning theory

  20. Support vector regression for porosity prediction in a heterogeneous reservoir: A comparative study

    Science.gov (United States)

    Al-Anazi, A. F.; Gates, I. D.

    2010-12-01

    In wells with limited log and core data, porosity, a fundamental and essential property to characterize reservoirs, is challenging to estimate by conventional statistical methods from offset well log and core data in heterogeneous formations. Beyond simple regression, neural networks have been used to develop more accurate porosity correlations. Unfortunately, neural network-based correlations have limited generalization ability and global correlations for a field are usually less accurate compared to local correlations for a sub-region of the reservoir. In this paper, support vector machines are explored as an intelligent technique to correlate porosity to well log data. Recently, support vector regression (SVR), based on the statistical learning theory, have been proposed as a new intelligence technique for both prediction and classification tasks. The underlying formulation of support vector machines embodies the structural risk minimization (SRM) principle which has been shown to be superior to the traditional empirical risk minimization (ERM) principle employed by conventional neural networks and classical statistical methods. This new formulation uses margin-based loss functions to control model complexity independently of the dimensionality of the input space, and kernel functions to project the estimation problem to a higher dimensional space, which enables the solution of more complex nonlinear problem optimization methods to exist for a globally optimal solution. SRM minimizes an upper bound on the expected risk using a margin-based loss function ( ɛ-insensitivity loss function for regression) in contrast to ERM which minimizes the error on the training data. Unlike classical learning methods, SRM, indexed by margin-based loss function, can also control model complexity independent of dimensionality. The SRM inductive principle is designed for statistical estimation with finite data where the ERM inductive principle provides the optimal solution (the

  1. Embedding systematic quality assessments in supportive supervision at primary healthcare level: application of an electronic Tool to Improve Quality of Healthcare in Tanzania.

    Science.gov (United States)

    Mboya, Dominick; Mshana, Christopher; Kessy, Flora; Alba, Sandra; Lengeler, Christian; Renggli, Sabine; Vander Plaetse, Bart; Mohamed, Mohamed A; Schulze, Alexander

    2016-10-13

    Assessing quality of health services, for example through supportive supervision, is essential for strengthening healthcare delivery. Most systematic health facility assessment mechanisms, however, are not suitable for routine supervision. The objective of this study is to describe a quality assessment methodology using an electronic format that can be embedded in supervision activities and conducted by council health staff. An electronic Tool to Improve Quality of Healthcare (e-TIQH) was developed to assess the quality of primary healthcare provision. The e-TIQH contains six sub-tools, each covering one quality dimension: infrastructure and equipment of the facility, its management and administration, job expectations, clinical skills of the staff, staff motivation and client satisfaction. As part of supportive supervision, council health staff conduct quality assessments in all primary healthcare facilities in a given council, including observation of clinical consultations and exit interviews with clients. Using a hand-held device, assessors enter data and view results in real time through automated data analysis, permitting immediate feedback to health workers. Based on the results, quality gaps and potential measures to address them are jointly discussed and actions plans developed. For illustrative purposes, preliminary findings from e-TIQH application are presented from eight councils of Tanzania for the period 2011-2013, with a quality score quality dimensions at baseline. Clinical practice was unsatisfactory in six councils, with more mixed results for availability of infrastructure and equipment, and for administration and management. In contrast, client satisfaction scored surprisingly high. Over time, each council showed a significant overall increase of 3-7 % in mean score, with the most pronounced improvements in staff motivation and job expectations. Given its comprehensiveness, convenient handling and automated statistical reports, e-TIQH enables

  2. Support vector machines and generalisation in HEP

    Science.gov (United States)

    Bevan, Adrian; Gamboa Goñi, Rodrigo; Hays, Jon; Stevenson, Tom

    2017-10-01

    We review the concept of Support Vector Machines (SVMs) and discuss examples of their use in a number of scenarios. Several SVM implementations have been used in HEP and we exemplify this algorithm using the Toolkit for Multivariate Analysis (TMVA) implementation. We discuss examples relevant to HEP including background suppression for H → τ + τ - at the LHC with several different kernel functions. Performance benchmarking leads to the issue of generalisation of hyper-parameter selection. The avoidance of fine tuning (over training or over fitting) in MVA hyper-parameter optimisation, i.e. the ability to ensure generalised performance of an MVA that is independent of the training, validation and test samples, is of utmost importance. We discuss this issue and compare and contrast performance of hold-out and k-fold cross-validation. We have extended the SVM functionality and introduced tools to facilitate cross validation in TMVA and present results based on these improvements.

  3. A support vector machine (SVM) based voltage stability classifier

    Energy Technology Data Exchange (ETDEWEB)

    Dosano, R.D.; Song, H. [Kunsan National Univ., Kunsan, Jeonbuk (Korea, Republic of); Lee, B. [Korea Univ., Seoul (Korea, Republic of)

    2007-07-01

    Power system stability has become even more complex and critical with the advent of deregulated energy markets and the growing desire to completely employ existing transmission and infrastructure. The economic pressure on electricity markets forces the operation of power systems and components to their limit of capacity and performance. System conditions can be more exposed to instability due to greater uncertainty in day to day system operations and increase in the number of potential components for system disturbances potentially resulting in voltage stability. This paper proposed a support vector machine (SVM) based power system voltage stability classifier using local measurements of voltage and active power of load. It described the procedure for fast classification of long-term voltage stability using the SVM algorithm. The application of the SVM based voltage stability classifier was presented with reference to the choice of input parameters; input data preconditioning; moving window for feature vector; determination of learning samples; and other considerations in SVM applications. The paper presented a case study with numerical examples of an 11-bus test system. The test results for the feasibility study demonstrated that the classifier could offer an excellent performance in classification with time-series measurements in terms of long-term voltage stability. 9 refs., 14 figs.

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

    Science.gov (United States)

    Li, Chaoshun; Zhou, Jianzhong

    2014-09-01

    Supervised learning method, like support vector machine (SVM), has been widely applied in diagnosing known faults, however this kind of method fails to work correctly when new or unknown fault occurs. Traditional unsupervised kernel clustering can be used for unknown fault diagnosis, but it could not make use of the historical classification information to improve diagnosis accuracy. In this paper, a semi-supervised kernel clustering model is designed to diagnose known and unknown faults. At first, a novel semi-supervised weighted kernel clustering algorithm based on gravitational search (SWKC-GS) is proposed for clustering of dataset composed of labeled and unlabeled fault samples. The clustering model of SWKC-GS is defined based on wrong classification rate of labeled samples and fuzzy clustering index on the whole dataset. Gravitational search algorithm (GSA) is used to solve the clustering model, while centers of clusters, feature weights and parameter of kernel function are selected as optimization variables. And then, new fault samples are identified and diagnosed by calculating the weighted kernel distance between them and the fault cluster centers. If the fault samples are unknown, they will be added in historical dataset and the SWKC-GS is used to partition the mixed dataset and update the clustering results for diagnosing new fault. In experiments, the proposed method has been applied in fault diagnosis for rotatory bearing, while SWKC-GS has been compared not only with traditional clustering methods, but also with SVM and neural network, for known fault diagnosis. In addition, the proposed method has also been applied in unknown fault diagnosis. The results have shown effectiveness of the proposed method in achieving expected diagnosis accuracy for both known and unknown faults of rotatory bearing. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

  5. Short-term traffic flow prediction model using particle swarm optimization–based combined kernel function-least squares support vector machine combined with chaos theory

    Directory of Open Access Journals (Sweden)

    Qiang Shang

    2016-08-01

    Full Text Available Short-term traffic flow prediction is an important part of intelligent transportation systems research and applications. For further improving the accuracy of short-time traffic flow prediction, a novel hybrid prediction model (multivariate phase space reconstruction–combined kernel function-least squares support vector machine based on multivariate phase space reconstruction and combined kernel function-least squares support vector machine is proposed. The C-C method is used to determine the optimal time delay and the optimal embedding dimension of traffic variables’ (flow, speed, and occupancy time series for phase space reconstruction. The G-P method is selected to calculate the correlation dimension of attractor which is an important index for judging chaotic characteristics of the traffic variables’ series. The optimal input form of combined kernel function-least squares support vector machine model is determined by multivariate phase space reconstruction, and the model’s parameters are optimized by particle swarm optimization algorithm. Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. The experimental results suggest that the new proposed model yields better predictions compared with similar models (combined kernel function-least squares support vector machine, multivariate phase space reconstruction–generalized kernel function-least squares support vector machine, and phase space reconstruction–combined kernel function-least squares support vector machine, which indicates that the new proposed model exhibits stronger prediction ability and robustness.

  6. The Relationships between Doctoral Students’ Perceptions of Supervision and Burnout

    Directory of Open Access Journals (Sweden)

    Solveig Cornér

    2017-06-01

    Full Text Available Aim/Purpose: Both the quality and the quantity of doctoral supervision have been identified as central determinants of the doctoral journey. However, there is a gap in our understanding of how supervision activities are associated with lack of wellbeing, such as burnout, and also to completion of the studies among doctoral students. Background:\tThe study explored doctoral students’ perceptions of different aspects of supervision including the primary sources, frequency, expressed satisfaction and their interrelation with experienced stress, exhaustion and cynicism. Methodology: Altogether 248 doctoral students from three Finnish universities representing social sciences, arts and humanities, and natural and life sciences responded to an adapted version of a Doctoral Experience Survey. A combination of several measures was used to investigate the students’ experiences of supervision and burnout. Contribution:\tThe results showed that students benefit from having several and different kinds of supervision activities. Various sources contribute not only to experiences of the doctoral journey and burnout, but also to the completion of the studies. Findings: Experienced lack of satisfaction with supervision and equality within the researcher community and a low frequency of supervision were related to experiences of burnout. Experiences of burnout were connected to students’ attrition intentions. Attrition intentions were related to source of supervision, the form of thesis, and inadequate supervision frequency. Frequency was related to both experience of burnout and likelihood of attrition. Recommendations for Practitioners: A recommendation developed from this research is to assist doctoral students with sufficient support, especially equality within the scholarly community and frequency of supervision. Further, greater emphasis could be put on group supervision and other collective forms of supervision. It is important that doctoral

  7. A Bayesian least-squares support vector machine method for predicting the remaining useful life of a microwave component

    Directory of Open Access Journals (Sweden)

    Fuqiang Sun

    2017-01-01

    Full Text Available Rapid and accurate lifetime prediction of critical components in a system is important to maintaining the system’s reliable operation. To this end, many lifetime prediction methods have been developed to handle various failure-related data collected in different situations. Among these methods, machine learning and Bayesian updating are the most popular ones. In this article, a Bayesian least-squares support vector machine method that combines least-squares support vector machine with Bayesian inference is developed for predicting the remaining useful life of a microwave component. A degradation model describing the change in the component’s power gain over time is developed, and the point and interval remaining useful life estimates are obtained considering a predefined failure threshold. In our case study, the radial basis function neural network approach is also implemented for comparison purposes. The results indicate that the Bayesian least-squares support vector machine method is more precise and stable in predicting the remaining useful life of this type of components.

  8. Legislation and supervision

    International Nuclear Information System (INIS)

    1998-01-01

    In this part next aspects are described: (1) Legislative and supervision-related framework (reviews of structure of supervisory bodies; legislation; state supervision in the nuclear safety area, and state supervision in the area of health protection against radiation are given); (2) Operator's responsibility

  9. Applying active learning to supervised word sense disambiguation in MEDLINE

    Science.gov (United States)

    Chen, Yukun; Cao, Hongxin; Mei, Qiaozhu; Zheng, Kai; Xu, Hua

    2013-01-01

    Objectives This study was to assess whether active learning strategies can be integrated with supervised word sense disambiguation (WSD) methods, thus reducing the number of annotated samples, while keeping or improving the quality of disambiguation models. Methods We developed support vector machine (SVM) classifiers to disambiguate 197 ambiguous terms and abbreviations in the MSH WSD collection. Three different uncertainty sampling-based active learning algorithms were implemented with the SVM classifiers and were compared with a passive learner (PL) based on random sampling. For each ambiguous term and each learning algorithm, a learning curve that plots the accuracy computed from the test set as a function of the number of annotated samples used in the model was generated. The area under the learning curve (ALC) was used as the primary metric for evaluation. Results Our experiments demonstrated that active learners (ALs) significantly outperformed the PL, showing better performance for 177 out of 197 (89.8%) WSD tasks. Further analysis showed that to achieve an average accuracy of 90%, the PL needed 38 annotated samples, while the ALs needed only 24, a 37% reduction in annotation effort. Moreover, we analyzed cases where active learning algorithms did not achieve superior performance and identified three causes: (1) poor models in the early learning stage; (2) easy WSD cases; and (3) difficult WSD cases, which provide useful insight for future improvements. Conclusions This study demonstrated that integrating active learning strategies with supervised WSD methods could effectively reduce annotation cost and improve the disambiguation models. PMID:23364851

  10. Data-driven process monitoring and diagnosis with support vector data description

    OpenAIRE

    Tafazzoli Moghaddam, Esmaeil

    2011-01-01

    This thesis targets the problem of fault diagnosis of industrial processes with data-drivenapproaches. In this context, a class of problems are considered in which the only informationabout the process is in the form of data and no model is available due to complexity of theprocess. Support vector data description is a kernel based method recently proposed in the fieldof pattern recognition and it is known for its powerful capabilities in nonlinear data classificationwhich can be exploited in...

  11. Fast and robust segmentation of white blood cell images by self-supervised learning.

    Science.gov (United States)

    Zheng, Xin; Wang, Yong; Wang, Guoyou; Liu, Jianguo

    2018-04-01

    A fast and accurate white blood cell (WBC) segmentation remains a challenging task, as different WBCs vary significantly in color and shape due to cell type differences, staining technique variations and the adhesion between the WBC and red blood cells. In this paper, a self-supervised learning approach, consisting of unsupervised initial segmentation and supervised segmentation refinement, is presented. The first module extracts the overall foreground region from the cell image by K-means clustering, and then generates a coarse WBC region by touching-cell splitting based on concavity analysis. The second module further uses the coarse segmentation result of the first module as automatic labels to actively train a support vector machine (SVM) classifier. Then, the trained SVM classifier is further used to classify each pixel of the image and achieve a more accurate segmentation result. To improve its segmentation accuracy, median color features representing the topological structure and a new weak edge enhancement operator (WEEO) handling fuzzy boundary are introduced. To further reduce its time cost, an efficient cluster sampling strategy is also proposed. We tested the proposed approach with two blood cell image datasets obtained under various imaging and staining conditions. The experiment results show that our approach has a superior performance of accuracy and time cost on both datasets. Copyright © 2018 Elsevier Ltd. All rights reserved.

  12. "Unscrambling what's in your head": A mixed method evaluation of clinical supervision for midwives.

    Science.gov (United States)

    Love, Bev; Sidebotham, Mary; Fenwick, Jennifer; Harvey, Susan; Fairbrother, Greg

    2017-08-01

    As a strategy to promote workforce sustainability a number of midwives working in one health district in New South Wales, Australia were trained to offer a reflective model of clinical supervision. The expectation was that these midwives would then be equipped to facilitate clinical supervision for their colleagues with the organisational aim of supporting professional development and promoting emotional well-being. To identify understanding, uptake, perceptions of impact, and the experiences of midwives accessing clinical supervision. Mixed Methods. In phase one 225 midwives were invited to complete a self-administered survey. Descriptive and inferential statistics were used to analyse the data. In phase two 12 midwives were interviewed. Thematic analysis was used to deepen understanding of midwives' experiences of receiving clinical supervision. Sixty percent of midwives responding in phase one had some experience of clinical supervision. Findings from both phases were complementary with midwives reporting a positive impact on their work, interpersonal skills, situational responses and career goals. Midwives described clinical supervision as a formal, structured and confidential space for 'safe reflection' that was valued as an opportunity for self-care. Barriers included misconceptions, perceived work related pressures and a sense that taking time out was unjustifiable. Education, awareness raising and further research into reflective clinical supervision, to support emotional well-being and professional midwifery practice is needed. In addition, health organisations need to design, implement and evaluate strategies that support the embedding of clinical supervision within midwives' clinical practice. Copyright © 2016 Australian College of Midwives. Published by Elsevier Ltd. All rights reserved.

  13. Predicting Tunnel Squeezing Using Multiclass Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Yang Sun

    2018-01-01

    Full Text Available Tunnel squeezing is one of the major geological disasters that often occur during the construction of tunnels in weak rock masses subjected to high in situ stresses. It could cause shield jamming, budget overruns, and construction delays and could even lead to tunnel instability and casualties. Therefore, accurate prediction or identification of tunnel squeezing is extremely important in the design and construction of tunnels. This study presents a modified application of a multiclass support vector machine (SVM to predict tunnel squeezing based on four parameters, that is, diameter (D, buried depth (H, support stiffness (K, and rock tunneling quality index (Q. We compiled a database from the literature, including 117 case histories obtained from different countries such as India, Nepal, and Bhutan, to train the multiclass SVM model. The proposed model was validated using 8-fold cross validation, and the average error percentage was approximately 11.87%. Compared with existing approaches, the proposed multiclass SVM model yields a better performance in predictive accuracy. More importantly, one could estimate the severity of potential squeezing problems based on the predicted squeezing categories/classes.

  14. Guidelines for enhancing clinical supervision: research ...

    African Journals Online (AJOL)

    ... toesighouding behels, maar dat hulle nie die noodsaaklikheid om reflektiewe leer toe te pas tydens die proses van kliniese toesighouding aangedui het nie. Keywords: Clinical supervision, Reflective thinking and learning, Support, Guidance (Health SA Gesondheid: interdisciplinary research journal: 2003 8(4): 12-23) ...

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

    Science.gov (United States)

    Potočnik, Primož; Govekar, Edvard

    2017-09-01

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

  16. Sistem Pakar Otomatisasi Baku Mutu Limbah Pertambangan Nikel Menggunakan Algoritma Supervised Mechine

    Directory of Open Access Journals (Sweden)

    Komang Aryasa

    2016-09-01

    Full Text Available Metode buka tutup pintu pembuangan limbah secara manual  berdasarkan hasil uji laboratorium  membutuhkan waktu relatif lama. Ketika hasil uji laboratorium menyatakan proses pembuangan harus dihentikan, limbah yang tidak memenuhi standar kelayakan sudah ikut terbuang. Tujuan dari penelitian ini adalah  untuk merancang sistem pakar menggunakan algoritma Supervised Learning untuk otomatisasi standar baku limbah pertambangan nikel, algoritma ini digunakan untuk mengklasifikasikan besaran nilai kandungan unsur dalam limbah, dan digunakan untuk mengoptimalkan proses penentuan kelayakan buang limbah. Algoritma ini bekerja setelah menerima data dalam bentuk nilai-nilai kandungan unsur yang dibangkitkan oleh sebuah aplikasi simulator yang mendeteksi kadar kandungan unsur dalam air limbah. Hasil analisis tersebut digunakan untuk menentukan kelayakan pembuangan limbah. Dari 11 unsur yang dianalisis tujuh unsur memiliki bobot nilai tertinggi dalam setiap pengukuran yaitu pH, TSS, Cu, Zn, Cr(6+, Cr Total, dan Fe., sementara Support Vector Machine hanya empat unsur yang memiliki nilai bobot tertinggi dari setiap pengukuran, yaitu unsur Cd, Pb, Ni dan Co.

  17. Collective academic supervision

    DEFF Research Database (Denmark)

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

    2013-01-01

    Supervision of students is a core activity in higher education. Previous research on student supervision in higher education focus on individual and relational aspects in the supervisory relationship rather than collective, pedagogical and methodical aspects of the planning of the supervision...... process. This article fills these gaps by discussing potentials and challenges in “Collective Academic Supervision”, a model for supervision at the Master of Education in Guidance at Aarhus University in Denmark. The pedagogical rationale behind the model is that students’ participation and learning...

  18. The Effect of Supportive Organizational Leadership, Organizational Socialization, and Satisfaction with Supervision on Turnover as Mediated by Organizational Commitment and Job Satisfaction in Faculty

    Science.gov (United States)

    Lowhorn, Greg L.

    2009-01-01

    This study utilized a predictive, multivariate research design to test the relationship between three independent variables--supportive organizational leadership, organizational socialization, and satisfaction with supervision--and the dependent variable--turnover intent--as mediated by organizational commitment and job satisfaction. The…

  19. Fault Diagnosis of a Reconfigurable Crawling–Rolling Robot Based on Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Karthikeyan Elangovan

    2017-10-01

    Full Text Available As robots begin to perform jobs autonomously, with minimal or no human intervention, a new challenge arises: robots also need to autonomously detect errors and recover from faults. In this paper, we present a Support Vector Machine (SVM-based fault diagnosis system for a bio-inspired reconfigurable robot named Scorpio. The diagnosis system needs to detect and classify faults while Scorpio uses its crawling and rolling locomotion modes. Specifically, we classify between faulty and non-faulty conditions by analyzing onboard Inertial Measurement Unit (IMU sensor data. The data capture nine different locomotion gaits, which include rolling and crawling modes, at three different speeds. Statistical methods are applied to extract features and to reduce the dimensionality of original IMU sensor data features. These statistical features were given as inputs for training and testing. Additionally, the c-Support Vector Classification (c-SVC and nu-SVC models of SVM, and their fault classification accuracies, were compared. The results show that the proposed SVM approach can be used to autonomously diagnose locomotion gait faults while the reconfigurable robot is in operation.

  20. Work-supportive family, family-supportive supervision, use of organizational benefits, and problem-focused coping: implications for work-family conflict and employee well-being.

    Science.gov (United States)

    Lapierre, Laurent M; Allen, Tammy D

    2006-04-01

    Employees (n = 230) from multiple organizations and industries were involved in a study assessing how work-family conflict avoidance methods stemming from the family domain (emotional sustenance and instrumental assistance from the family), the work domain (family-supportive supervision, use of telework and flextime), and the individual (use of problem-focused coping) independently relate to different dimensions of work-family conflict and to employees' affective and physical well-being. Results suggest that support from one's family and one's supervisor and the use of problem-focused coping seem most promising in terms of avoiding work-family conflict and/or decreased well-being. Benefits associated with the use of flextime, however, are relatively less evident, and using telework may potentially increase the extent to which family time demands interfere with work responsibilities. (c) 2006 APA, all rights reserved.

  1. Social constructionism and supervision: experiences of AAMFT supervisors and supervised therapists.

    Science.gov (United States)

    Hair, Heather J; Fine, Marshall

    2012-10-01

    A phenomenological research process was used to investigate the supervision experience for supervisors and therapists when supervisors use a social constructionist perspective. Participants of the one-to-one interviews were six AAMFT Approved Supervisors and six therapists providing counseling to individuals, couples and families. The findings suggest supervisors were committed to their self-identified supervision philosophy and intentionally sought out congruence between epistemology and practice. The shared experience of therapists indicates they associated desirable supervision experiences with their supervisors' social constructionist perspective. Our findings also indicated that supervisors' and therapists' understanding of social constructionism included the more controversial concepts of agency and extra-discursiveness. This research has taken an empirical step in the direction of understanding what the social constructionist supervision experience is like for supervisors and therapists. Our findings suggest a linkage between epistemology and supervision practice and a satisfaction with the supervision process. © 2012 American Association for Marriage and Family Therapy.

  2. Security system signal supervision

    International Nuclear Information System (INIS)

    Chritton, M.R.; Matter, J.C.

    1991-09-01

    This purpose of this NUREG is to present technical information that should be useful to NRC licensees for understanding and applying line supervision techniques to security communication links. A review of security communication links is followed by detailed discussions of link physical protection and DC/AC static supervision and dynamic supervision techniques. Material is also presented on security for atmospheric transmission and video line supervision. A glossary of security communication line supervision terms is appended. 16 figs

  3. Non-linear HVAC computations using least square support vector machines

    International Nuclear Information System (INIS)

    Kumar, Mahendra; Kar, I.N.

    2009-01-01

    This paper aims to demonstrate application of least square support vector machines (LS-SVM) to model two complex heating, ventilating and air-conditioning (HVAC) relationships. The two applications considered are the estimation of the predicted mean vote (PMV) for thermal comfort and the generation of psychrometric chart. LS-SVM has the potential for quick, exact representations and also possesses a structure that facilitates hardware implementation. The results show very good agreement between function values computed from conventional model and LS-SVM model in real time. The robustness of LS-SVM models against input noises has also been analyzed.

  4. Single Directional SMO Algorithm for Least Squares Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Xigao Shao

    2013-01-01

    Full Text Available Working set selection is a major step in decomposition methods for training least squares support vector machines (LS-SVMs. In this paper, a new technique for the selection of working set in sequential minimal optimization- (SMO- type decomposition methods is proposed. By the new method, we can select a single direction to achieve the convergence of the optimality condition. A simple asymptotic convergence proof for the new algorithm is given. Experimental comparisons demonstrate that the classification accuracy of the new method is not largely different from the existing methods, but the training speed is faster than existing ones.

  5. SVM-Maj: a majorization approach to linear support vector machines with different hinge errors

    NARCIS (Netherlands)

    P.J.F. Groenen (Patrick); G.I. Nalbantov (Georgi); J.C. Bioch (Cor)

    2007-01-01

    textabstractSupport vector machines (SVM) are becoming increasingly popular for the prediction of a binary dependent variable. SVMs perform very well with respect to competing techniques. Often, the solution of an SVM is obtained by switching to the dual. In this paper, we stick to the primal

  6. Learning Algorithms for Audio and Video Processing: Independent Component Analysis and Support Vector Machine Based Approaches

    National Research Council Canada - National Science Library

    Qi, Yuan

    2000-01-01

    In this thesis, we propose two new machine learning schemes, a subband-based Independent Component Analysis scheme and a hybrid Independent Component Analysis/Support Vector Machine scheme, and apply...

  7. The Weighted Support Vector Machine Based on Hybrid Swarm Intelligence Optimization for Icing Prediction of Transmission Line

    Directory of Open Access Journals (Sweden)

    Xiaomin Xu

    2015-01-01

    Full Text Available Not only can the icing coat on transmission line cause the electrical fault of gap discharge and icing flashover but also it will lead to the mechanical failure of tower, conductor, insulators, and others. It will bring great harm to the people’s daily life and work. Thus, accurate prediction of ice thickness has important significance for power department to control the ice disaster effectively. Based on the analysis of standard support vector machine, this paper presents a weighted support vector machine regression model based on the similarity (WSVR. According to the different importance of samples, this paper introduces the weighted support vector machine and optimizes its parameters by hybrid swarm intelligence optimization algorithm with the particle swarm and ant colony (PSO-ACO, which improves the generalization ability of the model. In the case study, the actual data of ice thickness and climate in a certain area of Hunan province have been used to predict the icing thickness of the area, which verifies the validity and applicability of this proposed method. The predicted results show that the intelligent model proposed in this paper has higher precision and stronger generalization ability.

  8. Man-machine supervision; Supervision homme-machine

    Energy Technology Data Exchange (ETDEWEB)

    Montmain, J. [CEA Valrho, Dir. de l' Energie Nucleaire (DEN), 30 - Marcoule (France)

    2005-05-01

    Today's complexity of systems where man is involved has led to the development of more and more sophisticated information processing systems where decision making has become more and more difficult. The operator task has moved from operation to supervision and the production tool has become indissociable from its numerical instrumentation and control system. The integration of more and more numerous and sophisticated control indicators in the control room does not necessary fulfill the expectations of the operation team. It is preferable to develop cooperative information systems which are real situation understanding aids. The stake is not the automation of operators' cognitive tasks but the supply of a reasoning help. One of the challenges of interactive information systems is the selection, organisation and dynamical display of information. The efficiency of the whole man-machine system depends on the communication interface efficiency. This article presents the principles and specificities of man-machine supervision systems: 1 - principle: operator's role in control room, operator and automation, monitoring and diagnosis, characteristics of useful models for supervision; 2 - qualitative reasoning: origin, trends, evolutions; 3 - causal reasoning: causality, causal graph representation, causal and diagnostic graph; 4 - multi-points of view reasoning: multi flow modeling method, Sagace method; 5 - approximate reasoning: the symbolic numerical interface, the multi-criteria decision; 6 - example of application: supervision in a spent-fuel reprocessing facility. (J.S.)

  9. Landslide susceptibility mapping & prediction using Support Vector Machine for Mandakini River Basin, Garhwal Himalaya, India

    Science.gov (United States)

    Kumar, Deepak; Thakur, Manoj; Dubey, Chandra S.; Shukla, Dericks P.

    2017-10-01

    In recent years, various machine learning techniques have been applied for landslide susceptibility mapping. In this study, three different variants of support vector machine viz., SVM, Proximal Support Vector Machine (PSVM) and L2-Support Vector Machine - Modified Finite Newton (L2-SVM-MFN) have been applied on the Mandakini River Basin in Uttarakhand, India to carry out the landslide susceptibility mapping. Eight thematic layers such as elevation, slope, aspect, drainages, geology/lithology, buffer of thrusts/faults, buffer of streams and soil along with the past landslide data were mapped in GIS environment and used for landslide susceptibility mapping in MATLAB. The study area covering 1625 km2 has merely 0.11% of area under landslides. There are 2009 pixels for past landslides out of which 50% (1000) landslides were considered as training set while remaining 50% as testing set. The performance of these techniques has been evaluated and the computational results show that L2-SVM-MFN obtains higher prediction values (0.829) of receiver operating characteristic curve (AUC-area under the curve) as compared to 0.807 for PSVM model and 0.79 for SVM. The results obtained from L2-SVM-MFN model are found to be superior than other SVM prediction models and suggest the usefulness of this technique to problem of landslide susceptibility mapping where training data is very less. However, these techniques can be used for satisfactory determination of susceptible zones with these inputs.

  10. KOMPARASI MODEL SUPPORT VECTOR MACHINES (SVM DAN NEURAL NETWORK UNTUK MENGETAHUI TINGKAT AKURASI PREDIKSI TERTINGGI HARGA SAHAM

    Directory of Open Access Journals (Sweden)

    R. Hadapiningradja Kusumodestoni

    2017-09-01

    Full Text Available There are many types of investments to make money, one of which is in the form of shares. Shares is a trading company dealing with securities in the global capital markets. Stock Exchange or also called stock market is actually the activities of private companies in the form of buying and selling investments. To avoid losses in investing, we need a model of predictive analysis with high accuracy and supported by data - lots of data and accurately. The correct techniques in the analysis will be able to reduce the risk for investors in investing. There are many models used in the analysis of stock price movement prediction, in this study the researchers used models of neural networks (NN and a model of support vector machine (SVM. Based on the background of the problems that have been mentioned in the previous description it can be formulated the problem as follows: need an algorithm that can predict stock prices, and need a high accuracy rate by adding a data set on the prediction, two algorithms will be investigated expected results last researchers can deduce where the algorithm accuracy rate predictions are the highest or accurate, then the purpose of this study was to mengkomparasi or compare between the two algorithms are algorithms Neural Network algorithm and Support Vector Machine which later on the end result has an accuracy rate forecast stock prices highest to see the error value RMSEnya. After doing research using the model of neural network and model of support vector machine (SVM to predict the stock using the data value of the shares on the stock index hongkong dated July 20, 2016 at 16:26 pm until the date of 15 September 2016 at 17:40 pm as many as 729 data sets within an interval of 5 minute through a process of training, learning, and then continue the process of testing so the result is that by using a neural network model of the prediction accuracy of 0.503 +/- 0.009 (micro 503 while using the model of support vector machine

  11. 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.

  12. Good supervision and PBL

    DEFF Research Database (Denmark)

    Otrel-Cass, Kathrin

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

  13. Large-scale ligand-based predictive modelling using support vector machines.

    Science.gov (United States)

    Alvarsson, Jonathan; Lampa, Samuel; Schaal, Wesley; Andersson, Claes; Wikberg, Jarl E S; Spjuth, Ola

    2016-01-01

    The increasing size of datasets in drug discovery makes it challenging to build robust and accurate predictive models within a reasonable amount of time. In order to investigate the effect of dataset sizes on predictive performance and modelling time, ligand-based regression models were trained on open datasets of varying sizes of up to 1.2 million chemical structures. For modelling, two implementations of support vector machines (SVM) were used. Chemical structures were described by the signatures molecular descriptor. Results showed that for the larger datasets, the LIBLINEAR SVM implementation performed on par with the well-established libsvm with a radial basis function kernel, but with dramatically less time for model building even on modest computer resources. Using a non-linear kernel proved to be infeasible for large data sizes, even with substantial computational resources on a computer cluster. To deploy the resulting models, we extended the Bioclipse decision support framework to support models from LIBLINEAR and made our models of logD and solubility available from within Bioclipse.

  14. A Reflection on the Work of an Educational Psychologist in Providing Supervision for a Team of Community Based Support Workers, Supporting Families with Vulnerable Adolescents at Risk of Exclusion from School

    Science.gov (United States)

    Maxwell, Tim

    2013-01-01

    The evolving role of the educational psychologist (EP) is discussed with an emphasis on the supervision provided for a team of support workers for vulnerable adolescents, working within a Local Service Team. This development is considered in the context of the Every Child Matters (DfES, 2004) agenda and the Farrell, Woods, Lewis, Rooney, Squire…

  15. A semi-supervised approach using label propagation to support citation screening.

    Science.gov (United States)

    Kontonatsios, Georgios; Brockmeier, Austin J; Przybyła, Piotr; McNaught, John; Mu, Tingting; Goulermas, John Y; Ananiadou, Sophia

    2017-08-01

    Citation screening, an integral process within systematic reviews that identifies citations relevant to the underlying research question, is a time-consuming and resource-intensive task. During the screening task, analysts manually assign a label to each citation, to designate whether a citation is eligible for inclusion in the review. Recently, several studies have explored the use of active learning in text classification to reduce the human workload involved in the screening task. However, existing approaches require a significant amount of manually labelled citations for the text classification to achieve a robust performance. In this paper, we propose a semi-supervised method that identifies relevant citations as early as possible in the screening process by exploiting the pairwise similarities between labelled and unlabelled citations to improve the classification performance without additional manual labelling effort. Our approach is based on the hypothesis that similar citations share the same label (e.g., if one citation should be included, then other similar citations should be included also). To calculate the similarity between labelled and unlabelled citations we investigate two different feature spaces, namely a bag-of-words and a spectral embedding based on the bag-of-words. The semi-supervised method propagates the classification codes of manually labelled citations to neighbouring unlabelled citations in the feature space. The automatically labelled citations are combined with the manually labelled citations to form an augmented training set. For evaluation purposes, we apply our method to reviews from clinical and public health. The results show that our semi-supervised method with label propagation achieves statistically significant improvements over two state-of-the-art active learning approaches across both clinical and public health reviews. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  16. Prediction of Skin Sensitization with a Particle Swarm Optimized Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Chenzhong Cao

    2009-07-01

    Full Text Available Skin sensitization is the most commonly reported occupational illness, causing much suffering to a wide range of people. Identification and labeling of environmental allergens is urgently required to protect people from skin sensitization. The guinea pig maximization test (GPMT and murine local lymph node assay (LLNA are the two most important in vivo models for identification of skin sensitizers. In order to reduce the number of animal tests, quantitative structure-activity relationships (QSARs are strongly encouraged in the assessment of skin sensitization of chemicals. This paper has investigated the skin sensitization potential of 162 compounds with LLNA results and 92 compounds with GPMT results using a support vector machine. A particle swarm optimization algorithm was implemented for feature selection from a large number of molecular descriptors calculated by Dragon. For the LLNA data set, the classification accuracies are 95.37% and 88.89% for the training and the test sets, respectively. For the GPMT data set, the classification accuracies are 91.80% and 90.32% for the training and the test sets, respectively. The classification performances were greatly improved compared to those reported in the literature, indicating that the support vector machine optimized by particle swarm in this paper is competent for the identification of skin sensitizers.

  17. Prediction of Skin Sensitization with a Particle Swarm Optimized Support Vector Machine

    Science.gov (United States)

    Yuan, Hua; Huang, Jianping; Cao, Chenzhong

    2009-01-01

    Skin sensitization is the most commonly reported occupational illness, causing much suffering to a wide range of people. Identification and labeling of environmental allergens is urgently required to protect people from skin sensitization. The guinea pig maximization test (GPMT) and murine local lymph node assay (LLNA) are the two most important in vivo models for identification of skin sensitizers. In order to reduce the number of animal tests, quantitative structure-activity relationships (QSARs) are strongly encouraged in the assessment of skin sensitization of chemicals. This paper has investigated the skin sensitization potential of 162 compounds with LLNA results and 92 compounds with GPMT results using a support vector machine. A particle swarm optimization algorithm was implemented for feature selection from a large number of molecular descriptors calculated by Dragon. For the LLNA data set, the classification accuracies are 95.37% and 88.89% for the training and the test sets, respectively. For the GPMT data set, the classification accuracies are 91.80% and 90.32% for the training and the test sets, respectively. The classification performances were greatly improved compared to those reported in the literature, indicating that the support vector machine optimized by particle swarm in this paper is competent for the identification of skin sensitizers. PMID:19742136

  18. Using support vector regression to predict PM10 and PM2.5

    International Nuclear Information System (INIS)

    Weizhen, Hou; Zhengqiang, Li; Yuhuan, Zhang; Hua, Xu; Ying, Zhang; Kaitao, Li; Donghui, Li; Peng, Wei; Yan, Ma

    2014-01-01

    Support vector machine (SVM), as a novel and powerful machine learning tool, can be used for the prediction of PM 10 and PM 2.5 (particulate matter less or equal than 10 and 2.5 micrometer) in the atmosphere. This paper describes the development of a successive over relaxation support vector regress (SOR-SVR) model for the PM 10 and PM 2.5 prediction, based on the daily average aerosol optical depth (AOD) and meteorological parameters (atmospheric pressure, relative humidity, air temperature, wind speed), which were all measured in Beijing during the year of 2010–2012. The Gaussian kernel function, as well as the k-fold crosses validation and grid search method, are used in SVR model to obtain the optimal parameters to get a better generalization capability. The result shows that predicted values by the SOR-SVR model agree well with the actual data and have a good generalization ability to predict PM 10 and PM 2.5 . In addition, AOD plays an important role in predicting particulate matter with SVR model, which should be included in the prediction model. If only considering the meteorological parameters and eliminating AOD from the SVR model, the prediction results of predict particulate matter will be not satisfying

  19. Sentiment Analysis of Comments on Rohingya Movement with Support Vector Machine

    OpenAIRE

    Chowdhury, Hemayet Ahmed; Nibir, Tanvir Alam; Islam, Md. Saiful

    2018-01-01

    The Rohingya Movement and Crisis caused a huge uproar in the political and economic state of Bangladesh. Refugee movement is a recurring event and a large amount of data in the form of opinions remains on social media such as Facebook, with very little analysis done on them.To analyse the comments based on all Rohingya related posts, we had to create and modify a classifier based on the Support Vector Machine algorithm. The code is implemented in python and uses scikit-learn library. A datase...

  20. Using clinical supervision to improve the quality and safety of patient care: a response to Berwick and Francis.

    Science.gov (United States)

    Tomlinson, Jonathon

    2015-06-11

    After widely publicised investigations into excess patient deaths at Mid Staffordshire hospital the UK government commissioned reports from Robert Francis QC and Professor Don Berwick. Among their recommendations to improve the quality and safety of patient care were lifelong learning, professional support and 'just culture'. Clinical supervision is in an excellent position to support these activities but opportunities are in danger of being squeezed out by regulatory and managerial demands. Doctors who have completed their training are responsible for complex professional judgements for which narrative supervision is particularly helpful. With reference to the literature and my own practice I propose that all practicing clinicians should have regular clinical supervision. Clinical supervision has patient-safety and the quality of patient care as its primary purposes. After training is completed, doctors may practice for the rest of their career without any clinical supervision, the implication being that the difficulties dealt with in clinical supervision are no longer difficulties, or are better dealt with some other way. Clinical supervision is sufficiently flexible to be adapted to the needs of experienced clinicians as its forms can be varied, though its functions remain focused on patient safety, good quality clinical care and professional wellbeing. The evidence linking clinical supervision to the quality and safety of patient care reveals that supervision is most effective when its educational and supportive functions are separated from its managerial and evaluative functions. Among supervision's different forms, narrative-based-supervision is particularly useful as it has been developed for clinicians who have completed their training. It provides ways to explore the complexity of clinical judgements and encourages doctors to question one another's authority in a supportive culture. To be successful, supervision should also be professionally led and

  1. 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.

  2. Reflecting reflection in supervision

    DEFF Research Database (Denmark)

    Lystbæk, Christian Tang

    associated with reflection and an exploration of alternative conceptions that view reflection within the context of settings which have a more group- and team-based orientation. Drawing on an action research project on health care supervision, the paper questions whether we should reject earlier views...... of reflection, rehabilitate them in order to capture broader connotations or move to new ways of regarding reflection that are more in keeping with not only reflective but also emotive, normative and formative views on supervision. The paper presents a critical perspective on supervision that challenge...... the current reflective paradigm I supervision and relate this to emotive, normative and formative views supervision. The paper is relevant for Nordic educational research into the supervision and guidance...

  3. Supervised classification of distributed data streams for smart grids

    Energy Technology Data Exchange (ETDEWEB)

    Guarracino, Mario R. [High Performance Computing and Networking - National Research Council of Italy, Naples (Italy); Irpino, Antonio; Verde, Rosanna [Seconda Universita degli Studi di Napoli, Dipartimento di Studi Europei e Mediterranei, Caserta (Italy); Radziukyniene, Neringa [Lithuanian Energy Institute, Laboratory of Systems Control and Automation, Kaunas (Lithuania)

    2012-03-15

    The electricity system inherited from the 19th and 20th centuries has been a reliable but centralized system. With the spreading of local, distributed and intermittent renewable energy resources, top-down central control of the grid no longer meets modern requirements. For these reasons, the power grid has been equipped with smart meters integrating bi-directional communications, advanced power measurement and management capabilities. Smart meters make it possible to remotely turn power on or off to a customer, read usage information, detect a service outage and the unauthorized use of electricity. To fully exploit their capabilities, we foresee the usage of distributed supervised classification algorithms. By gathering data available from meters and other sensors, such algorithms can create local classification models for attack detection, online monitoring, privacy preservation, workload balancing, prediction of energy demand and incoming faults. In this paper we present a decentralized distributed classification algorithm based on proximal support vector machines. The method uses partial knowledge, in form of data streams, to build its local model on each meter. We demonstrate the performance of the proposed scheme on synthetic datasets. (orig.)

  4. An empirical comparison of different approaches for combining multimodal neuroimaging data with support vector machine

    NARCIS (Netherlands)

    Pettersson-Yeo, W.; Benetti, S.; Marquand, A.F.; Joules, R.; Catani, M.; Williams, S.C.; Allen, P.; McGuire, P.; Mechelli, A.

    2014-01-01

    In the pursuit of clinical utility, neuroimaging researchers of psychiatric and neurological illness are increasingly using analyses, such as support vector machine, that allow inference at the single-subject level. Recent studies employing single-modality data, however, suggest that classification

  5. Support vector machine based battery model for electric vehicles

    International Nuclear Information System (INIS)

    Wang Junping; Chen Quanshi; Cao Binggang

    2006-01-01

    The support vector machine (SVM) is a novel type of learning machine based on statistical learning theory that can map a nonlinear function successfully. As a battery is a nonlinear system, it is difficult to establish the relationship between the load voltage and the current under different temperatures and state of charge (SOC). The SVM is used to model the battery nonlinear dynamics in this paper. Tests are performed on an 80Ah Ni/MH battery pack with the Federal Urban Driving Schedule (FUDS) cycle to set up the SVM model. Compared with the Nernst and Shepherd combined model, the SVM model can simulate the battery dynamics better with small amounts of experimental data. The maximum relative error is 3.61%

  6. Perceptions on evaluative and formative functions of external supervision of Rwandan primary healthcare facilities: A qualitative study.

    Science.gov (United States)

    Schriver, Michael; Cubaka, Vincent Kalumire; Itangishaka, Sylvere; Nyirazinyoye, Laetitia; Kallestrup, Per

    2018-01-01

    External supervision of primary healthcare facilities in low- and middle-income countries often has a managerial main purpose in which the role of support for professional development is unclear. To explore how Rwandan primary healthcare supervisors and providers (supervisees) perceive evaluative and formative functions of external supervision. Qualitative, exploratory study. Focus group discussions: three with supervisors, three with providers, and one mixed (n = 31). Findings were discussed with individual and groups of supervisors and providers. Evaluative activities occupied providers' understanding of supervision, including checking, correcting, marking and performance-based financing. These were presented as sources of motivation, that in self-determination theory indicate introjected regulation. Supervisors preferred to highlight their role in formative supervision, which may mask their own and providers' uncontested accounts that systematic performance evaluations predominated supervisors' work. Providers strongly requested larger focus on formative and supportive functions, voiced as well by most supervisors. Impact of performance evaluation on motivation and professional development is discussed. While external supervisors intended to support providers' professional development, our findings indicate serious problems with this in a context of frequent evaluations and performance marking. Separating the role of supporter and evaluator does not appear as the simple solution. If external supervision is to improve health care services, it is essential that supervisors and health centre managers are competent to support providers in a way that transparently accounts for various performance pressures. This includes delivery of proper formative supervision with useful feedback, maintaining an effective supervisory relationship, as well as ensuring providers are aware of the purpose and content of evaluative and formative supervision functions.

  7. Resistance to group clinical supervision

    DEFF Research Database (Denmark)

    Buus, Niels; Delgado, Cynthia; Traynor, Michael

    2018-01-01

    This present study is a report of an interview study exploring personal views on participating in group clinical supervision among mental health nursing staff members who do not participate in supervision. There is a paucity of empirical research on resistance to supervision, which has traditiona......This present study is a report of an interview study exploring personal views on participating in group clinical supervision among mental health nursing staff members who do not participate in supervision. There is a paucity of empirical research on resistance to supervision, which has...... traditionally been theorized as a supervisee's maladaptive coping with anxiety in the supervision process. The aim of the present study was to examine resistance to group clinical supervision by interviewing nurses who did not participate in supervision. In 2015, we conducted semistructured interviews with 24...... Danish mental health nursing staff members who had been observed not to participate in supervision in two periods of 3 months. Interviews were audio-recorded and subjected to discourse analysis. We constructed two discursive positions taken by the informants: (i) 'forced non-participation', where...

  8. Abusive supervision and workplace deviance and the moderating effects of negative reciprocity beliefs.

    Science.gov (United States)

    Mitchell, Marie S; Ambrose, Maureen L

    2007-07-01

    In this study, the authors examine the relationship between abusive supervision and employee workplace deviance. The authors conceptualize abusive supervision as a type of aggression. They use work on retaliation and direct and displaced aggression as a foundation for examining employees' reactions to abusive supervision. The authors predict abusive supervision will be related to supervisor-directed deviance, organizational deviance, and interpersonal deviance. Additionally, the authors examine the moderating effects of negative reciprocity beliefs. They hypothesized that the relationship between abusive supervision and supervisor-directed deviance would be stronger when individuals hold higher negative reciprocity beliefs. The results support this hypothesis. The implications of the results for understanding destructive behaviors in the workplace are examined.

  9. A Novel Empirical Mode Decomposition With Support Vector Regression for Wind Speed Forecasting.

    Science.gov (United States)

    Ren, Ye; Suganthan, Ponnuthurai Nagaratnam; Srikanth, Narasimalu

    2016-08-01

    Wind energy is a clean and an abundant renewable energy source. Accurate wind speed forecasting is essential for power dispatch planning, unit commitment decision, maintenance scheduling, and regulation. However, wind is intermittent and wind speed is difficult to predict. This brief proposes a novel wind speed forecasting method by integrating empirical mode decomposition (EMD) and support vector regression (SVR) methods. The EMD is used to decompose the wind speed time series into several intrinsic mode functions (IMFs) and a residue. Subsequently, a vector combining one historical data from each IMF and the residue is generated to train the SVR. The proposed EMD-SVR model is evaluated with a wind speed data set. The proposed EMD-SVR model outperforms several recently reported methods with respect to accuracy or computational complexity.

  10. Fault trend prediction of device based on support vector regression

    International Nuclear Information System (INIS)

    Song Meicun; Cai Qi

    2011-01-01

    The research condition of fault trend prediction and the basic theory of support vector regression (SVR) were introduced. SVR was applied to the fault trend prediction of roller bearing, and compared with other methods (BP neural network, gray model, and gray-AR model). The results show that BP network tends to overlearn and gets into local minimum so that the predictive result is unstable. It also shows that the predictive result of SVR is stabilization, and SVR is superior to BP neural network, gray model and gray-AR model in predictive precision. SVR is a kind of effective method of fault trend prediction. (authors)

  11. A Study of the Relationship of Perceived Principal Supervision and Support to the Perceived Self-Efficacy of Beginning and Experienced K-12 Teachers

    Science.gov (United States)

    Spearing, Leonard M.

    2013-01-01

    In this quantitative study the author examined the relationship between the perceived level of principal supervision and support to the perceived self-efficacy of K-12 teachers in a suburban public school district. The impact of perceived self-efficacy upon the commitment to remain in teaching was also considered. Finally the differential…

  12. Semi-supervised learning via regularized boosting working on multiple semi-supervised assumptions.

    Science.gov (United States)

    Chen, Ke; Wang, Shihai

    2011-01-01

    Semi-supervised learning concerns the problem of learning in the presence of labeled and unlabeled data. Several boosting algorithms have been extended to semi-supervised learning with various strategies. To our knowledge, however, none of them takes all three semi-supervised assumptions, i.e., smoothness, cluster, and manifold assumptions, together into account during boosting learning. In this paper, we propose a novel cost functional consisting of the margin cost on labeled data and the regularization penalty on unlabeled data based on three fundamental semi-supervised assumptions. Thus, minimizing our proposed cost functional with a greedy yet stagewise functional optimization procedure leads to a generic boosting framework for semi-supervised learning. Extensive experiments demonstrate that our algorithm yields favorite results for benchmark and real-world classification tasks in comparison to state-of-the-art semi-supervised learning algorithms, including newly developed boosting algorithms. Finally, we discuss relevant issues and relate our algorithm to the previous work.

  13. Normal mammogram detection based on local probability difference transforms and support vector machines

    International Nuclear Information System (INIS)

    Chiracharit, W.; Kumhom, P.; Chamnongthai, K.; Sun, Y.; Delp, E.J.; Babbs, C.F

    2007-01-01

    Automatic detection of normal mammograms, as a ''first look'' for breast cancer, is a new approach to computer-aided diagnosis. This approach may be limited, however, by two main causes. The first problem is the presence of poorly separable ''crossed-distributions'' in which the correct classification depends upon the value of each feature. The second problem is overlap of the feature distributions that are extracted from digitized mammograms of normal and abnormal patients. Here we introduce a new Support Vector Machine (SVM) based method utilizing with the proposed uncrossing mapping and Local Probability Difference (LPD). Crossed-distribution feature pairs are identified and mapped into a new features that can be separated by a zero-hyperplane of the new axis. The probability density functions of the features of normal and abnormal mammograms are then sampled and the local probability difference functions are estimated to enhance the features. From 1,000 ground-truth-known mammograms, 250 normal and 250 abnormal cases, including spiculated lesions, circumscribed masses or microcalcifications, are used for training a support vector machine. The classification results tested with another 250 normal and 250 abnormal sets show improved testing performances with 90% sensitivity and 89% specificity. (author)

  14. Ameliorated Austenite Carbon Content Control in Austempered Ductile Irons by Support Vector Regression

    Directory of Open Access Journals (Sweden)

    Chan-Yun Yang

    2013-01-01

    Full Text Available Austempered ductile iron has emerged as a notable material in several engineering fields, including marine applications. The initial austenite carbon content after austenization transform but before austempering process for generating bainite matrix proved critical in controlling the resulted microstructure and thus mechanical properties. In this paper, support vector regression is employed in order to establish a relationship between the initial carbon concentration in the austenite with austenization temperature and alloy contents, thereby exercising improved control in the mechanical properties of the austempered ductile irons. Particularly, the paper emphasizes a methodology tailored to deal with a limited amount of available data with intrinsically contracted and skewed distribution. The collected information from a variety of data sources presents another challenge of highly uncertain variance. The authors present a hybrid model consisting of a procedure of a histogram equalizer and a procedure of a support-vector-machine (SVM- based regression to gain a more robust relationship to respond to the challenges. The results show greatly improved accuracy of the proposed model in comparison to two former established methodologies. The sum squared error of the present model is less than one fifth of that of the two previous models.

  15. Optimal preventive bank supervision

    OpenAIRE

    Belhaj, Mohamed; Klimenko, Nataliya

    2012-01-01

    Early regulator interventions into problem banks is one of the key suggestions of Basel Committee on Banking Supervision. However, no guidance is given on their design. To fill this gap, we outline an incentive-based preventive supervision strategy that eliminates bad asset management in banks. Two supervision techniques are combined: temporary regulatory administration and random audits. Our design ensures good management without excessive supervision costs, through a gradual adjustment of...

  16. Magazine Picture Collage in Group Supervision

    Science.gov (United States)

    Shepard, Blythe C.; Guenette, Francis L.

    2010-01-01

    A magazine picture collage activity was used with three female counsellor education students as a vehicle to support them in processing their experience as counsellors in training. The use of magazine picture collage in group supervision is described, and the benefits and challenges are presented. The collages served as jumping-off points for…

  17. Informal sources of supervision in clinical training.

    Science.gov (United States)

    Farber, Barry A; Hazanov, Valery

    2014-11-01

    Although formal, assigned supervision is a potent source of learning and guidance for psychotherapy trainees, many beginning psychotherapists use other, informal sources of supervision or consultation for advice and support. Results of an online survey of beginning trainees (N = 146) indicate that other than their formally assigned supervisor, trainees most often consult with colleagues in their program, their own psychotherapist, and their significant other; that they're most likely to seek these other sources of help when they're feeling stuck or feel they've made a clinical mistake; that they do so because they need extra reassurance and suggestions; that they feel the advice given from these sources is helpful; and that they don't especially regret sharing this information. Several case examples are used to illustrate these points. Discussing clinical material with informal sources is, apparently, a great deal more common than typically acknowledged, and as such, has implications for training programs (including discussions of ethics) and formal supervision. © 2014 Wiley Periodicals, Inc.

  18. Healthcare students' evaluation of the clinical learning environment and supervision - a cross-sectional study.

    Science.gov (United States)

    Pitkänen, Salla; Kääriäinen, Maria; Oikarainen, Ashlee; Tuomikoski, Anna-Maria; Elo, Satu; Ruotsalainen, Heidi; Saarikoski, Mikko; Kärsämänoja, Taina; Mikkonen, Kristina

    2018-03-01

    The purpose of clinical placements and supervision is to promote the development of healthcare students´ professional skills. High-quality clinical learning environments and supervision were shown to have significant influence on healthcare students´ professional development. This study aimed to describe healthcare students` evaluation of the clinical learning environment and supervision, and to identify the factors that affect these. The study was performed as a cross-sectional study. The data (n = 1973) were gathered through an online survey using the Clinical Learning Environment, Supervision and Nurse Teacher scale during the academic year 2015-2016 from all healthcare students (N = 2500) who completed their clinical placement at a certain university hospital in Finland. The data were analysed using descriptive statistics and binary logistic regression analysis. More than half of the healthcare students had a named supervisor and supervision was completed as planned. The students evaluated the clinical learning environment and supervision as 'good'. The students´ readiness to recommend the unit to other students and the frequency of separate private unscheduled sessions with the supervisor were the main factors that affect healthcare students` evaluation of the clinical learning environment and supervision. Individualized and goal-oriented supervision in which the student had a named supervisor and where supervision was completed as planned in a positive environment that supported learning had a significant impact on student's learning. The clinical learning environment and supervision support the development of future healthcare professionals' clinical competence. The supervisory relationship was shown to have a significant effect on the outcomes of students' experiences. We recommend the planning of educational programmes for supervisors of healthcare students for the enhancement of supervisors' pedagogical competencies in supervising students in

  19. Supervised Learning

    Science.gov (United States)

    Rokach, Lior; Maimon, Oded

    This chapter summarizes the fundamental aspects of supervised methods. The chapter provides an overview of concepts from various interrelated fields used in subsequent chapters. It presents basic definitions and arguments from the supervised machine learning literature and considers various issues, such as performance evaluation techniques and challenges for data mining tasks.

  20. Energy efficiency supervision strategy selection of Chinese large-scale public buildings

    International Nuclear Information System (INIS)

    Jin Zhenxing; Wu Yong; Li Baizhan; Gao Yafeng

    2009-01-01

    This paper discusses energy consumption, building development and building energy consumption in China, and points that energy efficiency management and maintenance of large-scale public buildings is the breakthrough point of building energy saving in China. Three obstacles are lack of basic statistics data, lack of service market for building energy saving, and lack of effective management measures account for the necessity of energy efficiency supervision for large-scale public buildings. And then the paper introduces the supervision aims, the supervision system and the five basic systems' role in the supervision system, and analyzes the working mechanism of the five basic systems. The energy efficiency supervision system of large-scale public buildings takes energy consumption statistics as a data basis, Energy auditing as a technical support, energy consumption ration as a benchmark of energy saving and price increase beyond ration as a price lever, and energy efficiency public-noticing as an amplifier. The supervision system promotes energy efficiency operation and maintenance of large-scale public building, and drives a comprehensive building energy saving in China.

  1. Energy efficiency supervision strategy selection of Chinese large-scale public buildings

    Energy Technology Data Exchange (ETDEWEB)

    Jin, Zhenxing; Li, Baizhan; Gao, Yafeng [The Faculty of Urban Construction and Environmental Engineering, Chongqing University, Chongqing (China); Key Laboratory of the Three Gorges Reservoir Region' s Eco-Environment, Ministry of Education, Chongqing 400045 (China); Wu, Yong [The Department of Science and Technology, Ministry of Construction, Beijing 100835 (China)

    2009-06-15

    This paper discusses energy consumption, building development and building energy consumption in China, and points that energy efficiency management and maintenance of large-scale public buildings is the breakthrough point of building energy saving in China. Three obstacles are lack of basic statistics data, lack of service market for building energy saving, and lack of effective management measures account for the necessity of energy efficiency supervision for large-scale public buildings. And then the paper introduces the supervision aims, the supervision system and the five basic systems' role in the supervision system, and analyzes the working mechanism of the five basic systems. The energy efficiency supervision system of large-scale public buildings takes energy consumption statistics as a data basis, Energy auditing as a technical support, energy consumption ration as a benchmark of energy saving and price increase beyond ration as a price lever, and energy efficiency public-noticing as an amplifier. The supervision system promotes energy efficiency operation and maintenance of large-scale public building, and drives a comprehensive building energy saving in China. (author)

  2. Energy efficiency supervision strategy selection of Chinese large-scale public buildings

    Energy Technology Data Exchange (ETDEWEB)

    Jin Zhenxing [Faculty of Urban Construction and Environmental Engineering, Chongqing University, Chongqing (China); Key Laboratory of the Three Gorges Reservoir Region' s Eco-Environment, Ministry of Education, Chongqing 400045 (China)], E-mail: jinzhenxing33@sina.com; Wu Yong [Department of Science and Technology, Ministry of Construction, Beijing 100835 (China); Li Baizhan; Gao Yafeng [Faculty of Urban Construction and Environmental Engineering, Chongqing University, Chongqing (China); Key Laboratory of the Three Gorges Reservoir Region' s Eco-Environment, Ministry of Education, Chongqing 400045 (China)

    2009-06-15

    This paper discusses energy consumption, building development and building energy consumption in China, and points that energy efficiency management and maintenance of large-scale public buildings is the breakthrough point of building energy saving in China. Three obstacles are lack of basic statistics data, lack of service market for building energy saving, and lack of effective management measures account for the necessity of energy efficiency supervision for large-scale public buildings. And then the paper introduces the supervision aims, the supervision system and the five basic systems' role in the supervision system, and analyzes the working mechanism of the five basic systems. The energy efficiency supervision system of large-scale public buildings takes energy consumption statistics as a data basis, Energy auditing as a technical support, energy consumption ration as a benchmark of energy saving and price increase beyond ration as a price lever, and energy efficiency public-noticing as an amplifier. The supervision system promotes energy efficiency operation and maintenance of large-scale public building, and drives a comprehensive building energy saving in China.

  3. 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…

  4. Psykoterapi og supervision

    DEFF Research Database (Denmark)

    Jacobsen, Claus Haugaard

    2014-01-01

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

  5. Experiments on Supervised Learning Algorithms for Text Categorization

    Science.gov (United States)

    Namburu, Setu Madhavi; Tu, Haiying; Luo, Jianhui; Pattipati, Krishna R.

    2005-01-01

    Modern information society is facing the challenge of handling massive volume of online documents, news, intelligence reports, and so on. How to use the information accurately and in a timely manner becomes a major concern in many areas. While the general information may also include images and voice, we focus on the categorization of text data in this paper. We provide a brief overview of the information processing flow for text categorization, and discuss two supervised learning algorithms, viz., support vector machines (SVM) and partial least squares (PLS), which have been successfully applied in other domains, e.g., fault diagnosis [9]. While SVM has been well explored for binary classification and was reported as an efficient algorithm for text categorization, PLS has not yet been applied to text categorization. Our experiments are conducted on three data sets: Reuter's- 21578 dataset about corporate mergers and data acquisitions (ACQ), WebKB and the 20-Newsgroups. Results show that the performance of PLS is comparable to SVM in text categorization. A major drawback of SVM for multi-class categorization is that it requires a voting scheme based on the results of pair-wise classification. PLS does not have this drawback and could be a better candidate for multi-class text categorization.

  6. Support vector machine used to diagnose the fault of rotor broken bars of induction motors

    DEFF Research Database (Denmark)

    Zhitong, Cao; Jiazhong, Fang; Hongpingn, Chen

    2003-01-01

    for the SVM. After a SVM is trained with learning sample vectors, so each kind of the rotor broken bar faults of induction motors can be classified. Finally the retest is demonstrated, which proves that the SVM really has preferable ability of classification. In this paper we tried applying the SVM......The data-based machine learning is an important aspect of modern intelligent technology, while statistical learning theory (SLT) is a new tool that studies the machine learning methods in the case of a small number of samples. As a common learning method, support vector machine (SVM) is derived...... from the SLT. Here we were done some analogical experiments of the rotor broken bar faults of induction motors used, analyzed the signals of the sample currents with Fourier transform, and constructed the spectrum characteristics from low frequency to high frequency used as learning sample vectors...

  7. Faults Classification Of Power Electronic Circuits Based On A Support Vector Data Description Method

    Directory of Open Access Journals (Sweden)

    Cui Jiang

    2015-06-01

    Full Text Available Power electronic circuits (PECs are prone to various failures, whose classification is of paramount importance. This paper presents a data-driven based fault diagnosis technique, which employs a support vector data description (SVDD method to perform fault classification of PECs. In the presented method, fault signals (e.g. currents, voltages, etc. are collected from accessible nodes of circuits, and then signal processing techniques (e.g. Fourier analysis, wavelet transform, etc. are adopted to extract feature samples, which are subsequently used to perform offline machine learning. Finally, the SVDD classifier is used to implement fault classification task. However, in some cases, the conventional SVDD cannot achieve good classification performance, because this classifier may generate some so-called refusal areas (RAs, and in our design these RAs are resolved with the one-against-one support vector machine (SVM classifier. The obtained experiment results from simulated and actual circuits demonstrate that the improved SVDD has a classification performance close to the conventional one-against-one SVM, and can be applied to fault classification of PECs in practice.

  8. Rethinking Educational Supervision

    OpenAIRE

    Burhanettin DÖNMEZ; Kadir BEYCİOĞLU

    2009-01-01

    The history of educational (school) supervision has been influenced by the history of the interaction of intellectual movements in politics, society, philosophy and industrial movements. The purpose of this conceptual and theoretical study is to have a brief look at the concept of educational supervision with related historical developments in the field. The paper also intends to see the terms and issues critically, and to conceptualize some issues associated with educational supervision in...

  9. Gradient Evolution-based Support Vector Machine Algorithm for Classification

    Science.gov (United States)

    Zulvia, Ferani E.; Kuo, R. J.

    2018-03-01

    This paper proposes a classification algorithm based on a support vector machine (SVM) and gradient evolution (GE) algorithms. SVM algorithm has been widely used in classification. However, its result is significantly influenced by the parameters. Therefore, this paper aims to propose an improvement of SVM algorithm which can find the best SVMs’ parameters automatically. The proposed algorithm employs a GE algorithm to automatically determine the SVMs’ parameters. The GE algorithm takes a role as a global optimizer in finding the best parameter which will be used by SVM algorithm. The proposed GE-SVM algorithm is verified using some benchmark datasets and compared with other metaheuristic-based SVM algorithms. The experimental results show that the proposed GE-SVM algorithm obtains better results than other algorithms tested in this paper.

  10. Particle swarm optimization based support vector machine for damage level prediction of non-reshaped berm breakwater

    Digital Repository Service at National Institute of Oceanography (India)

    Harish, N.; Mandal, S.; Rao, S.; Patil, S.G.

    breakwater. Soft computing tools like Artificial Neural Network, Fuzzy Logic, Support Vector Machine (SVM), etc, are successfully used to solve complex problems. In the present study, SVM and hybrid of Particle Swarm Optimization (PSO) with SVM (PSO...

  11. PROVISION OF RESEARCH SUPPORT SERVICES TO ODL LEARNERS BY TUTORS: A Focus on the Zimbabwe Open University’s Bachelor of Education (Educational Management Research Students’ Supervision Experiences

    Directory of Open Access Journals (Sweden)

    Tichaona MAPOLISA

    2012-04-01

    Full Text Available The study examined the ODL learners’ perceptions of the quality of provision of research support services to the ODL learners by tutors. It focused on the Zimbabwe Open University’s (ZOU Bachelor of Education (Educational Management research students’ experiences. It was a qualitative multiple case study of four of the 10 Regional Centres of the ZOU. It purposively sampled 40 out of 160 research participants because they possessed desirable research characteristics for this study. The study was deemed significant in influencing tutors and policy makers to consider their research students’ supervision experiences as a basis for improving the quality of services for future research supervision practices and research projects. The study was guided by a two fold theory namely, thee Facilitation Theory (Nyawaranda, 2005 and the Nurturing Theory (Anderson, Pay and Mac Laughlin, 2006. Both theories advocate for the need of the supervisors to give their students a big heart. In terms of research supervision services offered by tutors the study indicated the time students were offered to meet research supervisors, prompt returns of marked work, and tutor student motivation and counselling as key services. In connection with the joys about research supervision, the students highly regarded: the manner in which tutors motivated them, tutors guidance in choosing research topic, tutor mentorship during research supervision and provision of workshops to polish up their research skills. In line with the challenges to the provision of research support services, three categories of challenges emerged. First, student-related challenges included lack of time, lack of money, lack of library facilities, lack of motivation and commitment to do research, lack of adequate theory in the area being researched on and family problems. Second, supervisor-related challenges included: too little direction, too little practical help given, too few meeting with students

  12. Time-frequency feature analysis and recognition of fission neutrons signal based on support vector machine

    International Nuclear Information System (INIS)

    Jin Jing; Wei Biao; Feng Peng; Tang Yuelin; Zhou Mi

    2010-01-01

    Based on the interdependent relationship between fission neutrons ( 252 Cf) and fission chain ( 235 U system), the paper presents the time-frequency feature analysis and recognition in fission neutron signal based on support vector machine (SVM) through the analysis on signal characteristics and the measuring principle of the 252 Cf fission neutron signal. The time-frequency characteristics and energy features of the fission neutron signal are extracted by using wavelet decomposition and de-noising wavelet packet decomposition, and then applied to training and classification by means of support vector machine based on statistical learning theory. The results show that, it is effective to obtain features of nuclear signal via wavelet decomposition and de-noising wavelet packet decomposition, and the latter can reflect the internal characteristics of the fission neutron system better. With the training accomplished, the SVM classifier achieves an accuracy rate above 70%, overcoming the lack of training samples, and verifying the effectiveness of the algorithm. (authors)

  13. A Modified Method Combined with a Support Vector Machine and Bayesian Algorithms in Biological Information

    Directory of Open Access Journals (Sweden)

    Wen-Gang Zhou

    2015-06-01

    Full Text Available With the deep research of genomics and proteomics, the number of new protein sequences has expanded rapidly. With the obvious shortcomings of high cost and low efficiency of the traditional experimental method, the calculation method for protein localization prediction has attracted a lot of attention due to its convenience and low cost. In the machine learning techniques, neural network and support vector machine (SVM are often used as learning tools. Due to its complete theoretical framework, SVM has been widely applied. In this paper, we make an improvement on the existing machine learning algorithm of the support vector machine algorithm, and a new improved algorithm has been developed, combined with Bayesian algorithms. The proposed algorithm can improve calculation efficiency, and defects of the original algorithm are eliminated. According to the verification, the method has proved to be valid. At the same time, it can reduce calculation time and improve prediction efficiency.

  14. Geodesic Flow Kernel Support Vector Machine for Hyperspectral Image Classification by Unsupervised Subspace Feature Transfer

    Directory of Open Access Journals (Sweden)

    Alim Samat

    2016-03-01

    Full Text Available In order to deal with scenarios where the training data, used to deduce a model, and the validation data have different statistical distributions, we study the problem of transformed subspace feature transfer for domain adaptation (DA in the context of hyperspectral image classification via a geodesic Gaussian flow kernel based support vector machine (GFKSVM. To show the superior performance of the proposed approach, conventional support vector machines (SVMs and state-of-the-art DA algorithms, including information-theoretical learning of discriminative cluster for domain adaptation (ITLDC, joint distribution adaptation (JDA, and joint transfer matching (JTM, are also considered. Additionally, unsupervised linear and nonlinear subspace feature transfer techniques including principal component analysis (PCA, randomized nonlinear principal component analysis (rPCA, factor analysis (FA and non-negative matrix factorization (NNMF are investigated and compared. Experiments on two real hyperspectral images show the cross-image classification performances of the GFKSVM, confirming its effectiveness and suitability when applied to hyperspectral images.

  15. MULTIPERIOD BANKING SUPERVISION

    OpenAIRE

    KARL-THEODOR EISELE; PHILIPPE ARTZNER

    2013-01-01

    This paper is based on a general method for multiperiod prudential supervision of companies submitted to hedgeable and non-hedgeable risks. Having treated the case of insurance in an earlier paper, we now consider a quantitative approach to supervision of commercial banks. The various elements under supervision are the bank’s current amount of tradeable assets, the deposit amount, and four flow processes: future trading risk exposures, deposit flows, flows of loan repayments and of deposit re...

  16. A support vector machine approach to detect financial statement fraud in South Africa: A first look

    CSIR Research Space (South Africa)

    Moepya, SO

    2014-04-01

    Full Text Available Auditors face the difficult task of detecting companies that issue manipulated financial statements. In recent years, machine learning methods have provided a feasible solution to this task. This study develops support vector machine (SVM) models...

  17. Initial experiences and innovations in supervising community health workers for maternal, newborn, and child health in Morogoro region, Tanzania.

    Science.gov (United States)

    Roberton, Timothy; Applegate, Jennifer; Lefevre, Amnesty E; Mosha, Idda; Cooper, Chelsea M; Silverman, Marissa; Feldhaus, Isabelle; Chebet, Joy J; Mpembeni, Rose; Semu, Helen; Killewo, Japhet; Winch, Peter; Baqui, Abdullah H; George, Asha S

    2015-04-09

    Supervision is meant to improve the performance and motivation of community health workers (CHWs). However, most evidence on supervision relates to facility health workers. The Integrated Maternal, Newborn, and Child Health (MNCH) Program in Morogoro region, Tanzania, implemented a CHW pilot with a cascade supervision model where facility health workers were trained in supportive supervision for volunteer CHWs, supported by regional and district staff, and with village leaders to further support CHWs. We examine the initial experiences of CHWs, their supervisors, and village leaders to understand the strengths and challenges of such a supervision model for CHWs. Quantitative and qualitative data were collected concurrently from CHWs, supervisors, and village leaders. A survey was administered to 228 (96%) of the CHWs in the Integrated MNCH Program and semi-structured interviews were conducted with 15 CHWs, 8 supervisors, and 15 village leaders purposefully sampled to represent different actor perspectives from health centre catchment villages in Morogoro region. Descriptive statistics analysed the frequency and content of CHW supervision, while thematic content analysis explored CHW, supervisor, and village leader experiences with CHW supervision. CHWs meet with their facility-based supervisors an average of 1.2 times per month. CHWs value supervision and appreciate the sense of legitimacy that arises when supervisors visit them in their village. Village leaders and district staff are engaged and committed to supporting CHWs. Despite these successes, facility-based supervisors visit CHWs in their village an average of only once every 2.8 months, CHWs and supervisors still see supervision primarily as an opportunity to check reports, and meetings with district staff are infrequent and not well scheduled. Supervision of CHWs could be strengthened by streamlining supervision protocols to focus less on report checking and more on problem solving and skills development

  18. Breast cancer risk assessment and diagnosis model using fuzzy support vector machine based expert system

    Science.gov (United States)

    Dheeba, J.; Jaya, T.; Singh, N. Albert

    2017-09-01

    Classification of cancerous masses is a challenging task in many computerised detection systems. Cancerous masses are difficult to detect because these masses are obscured and subtle in mammograms. This paper investigates an intelligent classifier - fuzzy support vector machine (FSVM) applied to classify the tissues containing masses on mammograms for breast cancer diagnosis. The algorithm utilises texture features extracted using Laws texture energy measures and a FSVM to classify the suspicious masses. The new FSVM treats every feature as both normal and abnormal samples, but with different membership. By this way, the new FSVM have more generalisation ability to classify the masses in mammograms. The classifier analysed 219 clinical mammograms collected from breast cancer screening laboratory. The tests made on the real clinical mammograms shows that the proposed detection system has better discriminating power than the conventional support vector machine. With the best combination of FSVM and Laws texture features, the area under the Receiver operating characteristic curve reached .95, which corresponds to a sensitivity of 93.27% with a specificity of 87.17%. The results suggest that detecting masses using FSVM contribute to computer-aided detection of breast cancer and as a decision support system for radiologists.

  19. Product demand forecasts using wavelet kernel support vector machine and particle swarm optimization in manufacture system

    Science.gov (United States)

    Wu, Qi

    2010-03-01

    Demand forecasts play a crucial role in supply chain management. The future demand for a certain product is the basis for the respective replenishment systems. Aiming at demand series with small samples, seasonal character, nonlinearity, randomicity and fuzziness, the existing support vector kernel does not approach the random curve of the sales time series in the space (quadratic continuous integral space). In this paper, we present a hybrid intelligent system combining the wavelet kernel support vector machine and particle swarm optimization for demand forecasting. The results of application in car sale series forecasting show that the forecasting approach based on the hybrid PSOWv-SVM model is effective and feasible, the comparison between the method proposed in this paper and other ones is also given, which proves that this method is, for the discussed example, better than hybrid PSOv-SVM and other traditional methods.

  20. Ranking Support Vector Machine with Kernel Approximation.

    Science.gov (United States)

    Chen, Kai; Li, Rongchun; Dou, Yong; Liang, Zhengfa; Lv, Qi

    2017-01-01

    Learning to rank algorithm has become important in recent years due to its successful application in information retrieval, recommender system, and computational biology, and so forth. Ranking support vector machine (RankSVM) is one of the state-of-art ranking models and has been favorably used. Nonlinear RankSVM (RankSVM with nonlinear kernels) can give higher accuracy than linear RankSVM (RankSVM with a linear kernel) for complex nonlinear ranking problem. However, the learning methods for nonlinear RankSVM are still time-consuming because of the calculation of kernel matrix. In this paper, we propose a fast ranking algorithm based on kernel approximation to avoid computing the kernel matrix. We explore two types of kernel approximation methods, namely, the Nyström method and random Fourier features. Primal truncated Newton method is used to optimize the pairwise L2-loss (squared Hinge-loss) objective function of the ranking model after the nonlinear kernel approximation. Experimental results demonstrate that our proposed method gets a much faster training speed than kernel RankSVM and achieves comparable or better performance over state-of-the-art ranking algorithms.

  1. Ranking Support Vector Machine with Kernel Approximation

    Directory of Open Access Journals (Sweden)

    Kai Chen

    2017-01-01

    Full Text Available Learning to rank algorithm has become important in recent years due to its successful application in information retrieval, recommender system, and computational biology, and so forth. Ranking support vector machine (RankSVM is one of the state-of-art ranking models and has been favorably used. Nonlinear RankSVM (RankSVM with nonlinear kernels can give higher accuracy than linear RankSVM (RankSVM with a linear kernel for complex nonlinear ranking problem. However, the learning methods for nonlinear RankSVM are still time-consuming because of the calculation of kernel matrix. In this paper, we propose a fast ranking algorithm based on kernel approximation to avoid computing the kernel matrix. We explore two types of kernel approximation methods, namely, the Nyström method and random Fourier features. Primal truncated Newton method is used to optimize the pairwise L2-loss (squared Hinge-loss objective function of the ranking model after the nonlinear kernel approximation. Experimental results demonstrate that our proposed method gets a much faster training speed than kernel RankSVM and achieves comparable or better performance over state-of-the-art ranking algorithms.

  2. DNBR Prediction Using a Support Vector Regression

    International Nuclear Information System (INIS)

    Yang, Heon Young; Na, Man Gyun

    2008-01-01

    PWRs (Pressurized Water Reactors) generally operate in the nucleate boiling state. However, the conversion of nucleate boiling into film boiling with conspicuously reduced heat transfer induces a boiling crisis that may cause the fuel clad melting in the long run. This type of boiling crisis is called Departure from Nucleate Boiling (DNB) phenomena. Because the prediction of minimum DNBR in a reactor core is very important to prevent the boiling crisis such as clad melting, a lot of research has been conducted to predict DNBR values. The object of this research is to predict minimum DNBR applying support vector regression (SVR) by using the measured signals of a reactor coolant system (RCS). The SVR has extensively and successfully been applied to nonlinear function approximation like the proposed problem for estimating DNBR values that will be a function of various input variables such as reactor power, reactor pressure, core mass flowrate, control rod positions and so on. The minimum DNBR in a reactor core is predicted using these various operating condition data as the inputs to the SVR. The minimum DBNR values predicted by the SVR confirm its correctness compared with COLSS values

  3. A fuzzy regression with support vector machine approach to the estimation of horizontal global solar radiation

    International Nuclear Information System (INIS)

    Baser, Furkan; Demirhan, Haydar

    2017-01-01

    Accurate estimation of the amount of horizontal global solar radiation for a particular field is an important input for decision processes in solar radiation investments. In this article, we focus on the estimation of yearly mean daily horizontal global solar radiation by using an approach that utilizes fuzzy regression functions with support vector machine (FRF-SVM). This approach is not seriously affected by outlier observations and does not suffer from the over-fitting problem. To demonstrate the utility of the FRF-SVM approach in the estimation of horizontal global solar radiation, we conduct an empirical study over a dataset collected in Turkey and applied the FRF-SVM approach with several kernel functions. Then, we compare the estimation accuracy of the FRF-SVM approach to an adaptive neuro-fuzzy system and a coplot supported-genetic programming approach. We observe that the FRF-SVM approach with a Gaussian kernel function is not affected by both outliers and over-fitting problem and gives the most accurate estimates of horizontal global solar radiation among the applied approaches. Consequently, the use of hybrid fuzzy functions and support vector machine approaches is found beneficial in long-term forecasting of horizontal global solar radiation over a region with complex climatic and terrestrial characteristics. - Highlights: • A fuzzy regression functions with support vector machines approach is proposed. • The approach is robust against outlier observations and over-fitting problem. • Estimation accuracy of the model is superior to several existent alternatives. • A new solar radiation estimation model is proposed for the region of Turkey. • The model is useful under complex terrestrial and climatic conditions.

  4. Graduating the age-specific fertility pattern using Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Anastasia Kostaki

    2009-06-01

    Full Text Available A topic of interest in demographic literature is the graduation of the age-specific fertility pattern. A standard graduation technique extensively used by demographers is to fit parametric models that accurately reproduce it. Non-parametric statistical methodology might be alternatively used for this graduation purpose. Support Vector Machines (SVM is a non-parametric methodology that could be utilized for fertility graduation purposes. This paper evaluates the SVM techniques as tools for graduating fertility rates In that we apply these techniques to empirical age specific fertility rates from a variety of populations, time period, and cohorts. Additionally, for comparison reasons we also fit known parametric models to the same empirical data sets.

  5. An assessment of support vector machines for land cover classification

    Science.gov (United States)

    Huang, C.; Davis, L.S.; Townshend, J.R.G.

    2002-01-01

    The support vector machine (SVM) is a group of theoretically superior machine learning algorithms. It was found competitive with the best available machine learning algorithms in classifying high-dimensional data sets. This paper gives an introduction to the theoretical development of the SVM and an experimental evaluation of its accuracy, stability and training speed in deriving land cover classifications from satellite images. The SVM was compared to three other popular classifiers, including the maximum likelihood classifier (MLC), neural network classifiers (NNC) and decision tree classifiers (DTC). The impacts of kernel configuration on the performance of the SVM and of the selection of training data and input variables on the four classifiers were also evaluated in this experiment.

  6. 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. (c) 2015 APA, all rights reserved).

  7. A Supervision of Solidarity

    Science.gov (United States)

    Reynolds, Vikki

    2010-01-01

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

  8. Estimation of the wind turbine yaw error by support vector machines

    DEFF Research Database (Denmark)

    Sheibat-Othman, Nida; Othman, Sami; Tayari, Raoaa

    2015-01-01

    Wind turbine yaw error information is of high importance in controlling wind turbine power and structural load. Normally used wind vanes are imprecise. In this work, the estimation of yaw error in wind turbines is studied using support vector machines for regression (SVR). As the methodology...... is data-based, simulated data from a high fidelity aero-elastic model is used for learning. The model simulates a variable speed horizontal-axis wind turbine composed of three blades and a full converter. Both partial load (blade angles fixed at 0 deg) and full load zones (active pitch actuators...

  9. BANKING SUPERVISION IN EUROPEAN UNION

    Directory of Open Access Journals (Sweden)

    Lavinia Mihaela GUȚU

    2013-10-01

    Full Text Available The need for prudential supervision imposed to banks by law arises from the action that banking market’s basic factors have. Therefore, it is about banks’ role in economy. The normal functioning of banks in all their important duties maintains the stability of banking system. Further, the stability of the entire economy depends on the stability of the banking system. Under conditions of imbalance regarding treasury or liquidity, banks are faced with unmanageable crisis and the consequences can be fatal. To ensure long-term stability of the banking system, supervisory regulations were constituted in order to prevent banks focusing on achieving rapidly high profits and protect the interests of depositors. Starting from this point, this paper will carry out a study on existing models of supervision in the European Union’s Member States. A comparison between them will support identifying the advantages and disadvantages of each of them.

  10. Groundwater vulnerability indices conditioned by Supervised Intelligence Committee Machine (SICM).

    Science.gov (United States)

    Nadiri, Ata Allah; Gharekhani, Maryam; Khatibi, Rahman; Sadeghfam, Sina; Moghaddam, Asghar Asghari

    2017-01-01

    This research presents a Supervised Intelligent Committee Machine (SICM) model to assess groundwater vulnerability indices of an aquifer. SICM uses Artificial Neural Networks (ANN) to overarch three Artificial Intelligence (AI) models: Support Vector Machine (SVM), Neuro-Fuzzy (NF) and Gene Expression Programming (GEP). Each model uses the DRASTIC index, the acronym of 7 geological, hydrological and hydrogeological parameters, which collectively represents intrinsic (or natural) vulnerability and gives a sense of contaminants, such as nitrate-N, penetrating aquifers from the surface. These models are trained to modify or condition their DRASTIC index values by measured nitrate-N concentration. The three AI-techniques often perform similarly but have differences as well and therefore SICM exploits the situation to improve the modeled values by producing a hybrid modeling results through selecting better performing SVM, NF and GEP components. The models of the study area at Ardabil aquifer show that the vulnerability indices by the DRASTIC framework produces sharp fronts but AI models smoothen the fronts and reflect a better correlation with observed nitrate values; SICM improves on the performances of three AI models and cope well with heterogeneity and uncertain parameters. Copyright © 2016 Elsevier B.V. All rights reserved.

  11. A National Survey of School Counselor Supervision Practices: Administrative, Clinical, Peer, and Technology Mediated Supervision

    Science.gov (United States)

    Perera-Diltz, Dilani M.; Mason, Kimberly L.

    2012-01-01

    Supervision is vital for personal and professional development of counselors. Practicing school counselors (n = 1557) across the nation were surveyed to explore current supervision practices. Results indicated that 41.1% of school counselors provide supervision. Although 89% receive some type of supervision, only 10.3% of school counselors receive…

  12. Ensemble learning with trees and rules: supervised, semi-supervised, unsupervised

    Science.gov (United States)

    In this article, we propose several new approaches for post processing a large ensemble of conjunctive rules for supervised and semi-supervised learning problems. We show with various examples that for high dimensional regression problems the models constructed by the post processing the rules with ...

  13. Automated valve fault detection based on acoustic emission parameters and support vector machine

    Directory of Open Access Journals (Sweden)

    Salah M. Ali

    2018-03-01

    Full Text Available Reciprocating compressors are one of the most used types of compressors with wide applications in industry. The most common failure in reciprocating compressors is always related to the valves. Therefore, a reliable condition monitoring method is required to avoid the unplanned shutdown in this category of machines. Acoustic emission (AE technique is one of the effective recent methods in the field of valve condition monitoring. However, a major challenge is related to the analysis of AE signal which perhaps only depends on the experience and knowledge of technicians. This paper proposes automated fault detection method using support vector machine (SVM and AE parameters in an attempt to reduce human intervention in the process. Experiments were conducted on a single stage reciprocating air compressor by combining healthy and faulty valve conditions to acquire the AE signals. Valve functioning was identified through AE waveform analysis. SVM faults detection model was subsequently devised and validated based on training and testing samples respectively. The results demonstrated automatic valve fault detection model with accuracy exceeding 98%. It is believed that valve faults can be detected efficiently without human intervention by employing the proposed model for a single stage reciprocating compressor. Keywords: Condition monitoring, Faults detection, Signal analysis, Acoustic emission, Support vector machine

  14. Adequate supervision for children and adolescents.

    Science.gov (United States)

    Anderst, James; Moffatt, Mary

    2014-11-01

    Primary care providers (PCPs) have the opportunity to improve child health and well-being by addressing supervision issues before an injury or exposure has occurred and/or after an injury or exposure has occurred. Appropriate anticipatory guidance on supervision at well-child visits can improve supervision of children, and may prevent future harm. Adequate supervision varies based on the child's development and maturity, and the risks in the child's environment. Consideration should be given to issues as wide ranging as swimming pools, falls, dating violence, and social media. By considering the likelihood of harm and the severity of the potential harm, caregivers may provide adequate supervision by minimizing risks to the child while still allowing the child to take "small" risks as needed for healthy development. Caregivers should initially focus on direct (visual, auditory, and proximity) supervision of the young child. Gradually, supervision needs to be adjusted as the child develops, emphasizing a safe environment and safe social interactions, with graduated independence. PCPs may foster adequate supervision by providing concrete guidance to caregivers. In addition to preventing injury, supervision includes fostering a safe, stable, and nurturing relationship with every child. PCPs should be familiar with age/developmentally based supervision risks, adequate supervision based on those risks, characteristics of neglectful supervision based on age/development, and ways to encourage appropriate supervision throughout childhood. Copyright 2014, SLACK Incorporated.

  15. A multi-label learning based kernel automatic recommendation method for support vector machine.

    Science.gov (United States)

    Zhang, Xueying; Song, Qinbao

    2015-01-01

    Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance.

  16. 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-establish......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...... influenced other areas of GPs' professional lives as well. However, more studies are needed to assess the impact of supervision groups....

  17. Integrating principal component analysis and vector quantization with support vector regression for sulfur content prediction in HDS process

    Directory of Open Access Journals (Sweden)

    Shokri Saeid

    2015-01-01

    Full Text Available An accurate prediction of sulfur content is very important for the proper operation and product quality control in hydrodesulfurization (HDS process. For this purpose, a reliable data- driven soft sensors utilizing Support Vector Regression (SVR was developed and the effects of integrating Vector Quantization (VQ with Principle Component Analysis (PCA were studied on the assessment of this soft sensor. First, in pre-processing step the PCA and VQ techniques were used to reduce dimensions of the original input datasets. Then, the compressed datasets were used as input variables for the SVR model. Experimental data from the HDS setup were employed to validate the proposed integrated model. The integration of VQ/PCA techniques with SVR model was able to increase the prediction accuracy of SVR. The obtained results show that integrated technique (VQ-SVR was better than (PCA-SVR in prediction accuracy. Also, VQ decreased the sum of the training and test time of SVR model in comparison with PCA. For further evaluation, the performance of VQ-SVR model was also compared to that of SVR. The obtained results indicated that VQ-SVR model delivered the best satisfactory predicting performance (AARE= 0.0668 and R2= 0.995 in comparison with investigated models.

  18. Measurement of Charmless B to Vector-Vector decays at BaBar

    International Nuclear Information System (INIS)

    Olaiya, Emmanuel

    2011-01-01

    The authors present results of B → vector-vector (VV) and B → vector-axial vector (VA) decays B 0 → φX(X = φ,ρ + or ρ 0 ), B + → φK (*)+ , B 0 → K*K*, B 0 → ρ + b 1 - and B + → K* 0 α 1 + . The largest dataset used for these results is based on 465 x 10 6 Υ(4S) → B(bar B) decays, collected with the BABAR detector at the PEP-II B meson factory located at the Stanford Linear Accelerator Center (SLAC). Using larger datasets, the BABAR experiment has provided more precise B → VV measurements, further supporting the smaller than expected longitudinal polarization fraction of B → φK*. Additional B meson to vector-vector and vector-axial vector decays have also been studied with a view to shedding light on the polarization anomaly. Taking into account the available errors, we find no disagreement between theory and experiment for these additional decays.

  19. Evaluering af kollegial supervision

    DEFF Research Database (Denmark)

    Petersen, Anne Line Bjerre Folsgaard; Bager, Lene Tortzen; Jørgensen, Mette Eg

    2015-01-01

    Videoen er en evaluering af arbejdet med en metodisk tilgang til kollegial supervision på VIA Ergoterapeutuddannelsen gennem et par år. Evalueringen sætter fokus på selve metoden, der er anvendt til kollegial supervision. Derudover er der fokus på erfaringer og udbytte af at arbejde systematisk med...... kollegial supervision blandt undervisere på VIA Ergoterapeutuddannelsen....

  20. Rethinking Educational Supervision

    Directory of Open Access Journals (Sweden)

    Burhanettin DÖNMEZ

    2009-08-01

    Full Text Available The history of educational (school supervision has been influenced by the history of the interaction of intellectual movements in politics, society, philosophy and industrial movements. The purpose of this conceptual and theoretical study is to have a brief look at the concept of educational supervision with related historical developments in the field. The paper also intends to see the terms and issues critically, and to conceptualize some issues associated with educational supervision in practice. In the paper, the issues are discussed and a number of suggestions are addressed for debate.

  1. Assessing the human cardiovascular response to moderate exercise: feature extraction by support vector regression

    International Nuclear Information System (INIS)

    Wang, Lu; Su, Steven W; Celler, Branko G; Chan, Gregory S H; Cheng, Teddy M; Savkin, Andrey V

    2009-01-01

    This study aims to quantitatively describe the steady-state relationships among percentage changes in key central cardiovascular variables (i.e. stroke volume, heart rate (HR), total peripheral resistance and cardiac output), measured using non-invasive means, in response to moderate exercise, and the oxygen uptake rate, using a new nonlinear regression approach—support vector regression. Ten untrained normal males exercised in an upright position on an electronically braked cycle ergometer with constant workloads ranging from 25 W to 125 W. Throughout the experiment, .VO 2 was determined breath by breath and the HR was monitored beat by beat. During the last minute of each exercise session, the cardiac output was measured beat by beat using a novel non-invasive ultrasound-based device and blood pressure was measured using a tonometric measurement device. Based on the analysis of experimental data, nonlinear steady-state relationships between key central cardiovascular variables and .VO 2 were qualitatively observed except for the HR which increased linearly as a function of increasing .VO 2 . Quantitative descriptions of these complex nonlinear behaviour were provided by nonparametric models which were obtained by using support vector regression

  2. SVM Classifier - a comprehensive java interface for support vector machine classification of microarray data.

    Science.gov (United States)

    Pirooznia, Mehdi; Deng, Youping

    2006-12-12

    Graphical user interface (GUI) software promotes novelty by allowing users to extend the functionality. SVM Classifier is a cross-platform graphical application that handles very large datasets well. The purpose of this study is to create a GUI application that allows SVM users to perform SVM training, classification and prediction. The GUI provides user-friendly access to state-of-the-art SVM methods embodied in the LIBSVM implementation of Support Vector Machine. We implemented the java interface using standard swing libraries. We used a sample data from a breast cancer study for testing classification accuracy. We achieved 100% accuracy in classification among the BRCA1-BRCA2 samples with RBF kernel of SVM. We have developed a java GUI application that allows SVM users to perform SVM training, classification and prediction. We have demonstrated that support vector machines can accurately classify genes into functional categories based upon expression data from DNA microarray hybridization experiments. Among the different kernel functions that we examined, the SVM that uses a radial basis kernel function provides the best performance. The SVM Classifier is available at http://mfgn.usm.edu/ebl/svm/.

  3. Social construction : discursive perspective towards supervision

    OpenAIRE

    Naujanienė, Rasa

    2010-01-01

    The aim of publication is to discuss the development of supervision theory in relation with social and social work theory and practice. Main focus in the analysis is done to social constructionist ideas and its’ relevance to supervision practice. The development of supervision is related with supervision practice. Starting in 19th century supervision from giving practical advices supervision came to 21st century as dialog based on critical and philosophical reflection. Different theory and pr...

  4. Digital communication to support clinical supervision: considering the human factors.

    Science.gov (United States)

    Mather, Carey; Marlow, Annette; Cummings, Elizabeth

    2013-01-01

    During the last three years the School of Nursing and Midwifery at the University of Tasmania has used a needs assessment survey to explore the needs of organizations and nursing professionals that facilitate and clinically supervise Bachelor of Nursing students in the workplace. Findings from the survey indicated that staff at healthcare organizations wanted a communication strategy that was easily accessible by clinicians who supervised students during work integrated learning placements. In particular they wanted to receive timely information related to the role and function of supervisors in practice. The development of the digital strategy to strengthen the development of a community of practice between the University, organizations, facilities and clinical supervisors was identified as the key method of improving communication. Blogging and micro blogging were selected as methods of choice for the implementation of the digital strategy because they were easy to set up, use and enable equity of access to geographically dispersed practitioners in urban and rural areas. Change champions were identified to disseminate information about the strategy within their workplaces. Although clinicians indicated electronic communication as their preferred method, there were a number of human factors at a systems and individual level identified to be challenges when communicating with clinical supervisors who were based off-campus. Information communication technology policies and embedded culture towards social presence were impediments to using this approach in some organizations. Additionally, it was found that it is necessary for this group of clinicians to be educated about using digital methods to undertake their role as clinical supervisors in their varied clinical practice environments.

  5. Image Jacobian Matrix Estimation Based on Online Support Vector Regression

    Directory of Open Access Journals (Sweden)

    Shangqin Mao

    2012-10-01

    Full Text Available Research into robotics visual servoing is an important area in the field of robotics. It has proven difficult to achieve successful results for machine vision and robotics in unstructured environments without using any a priori camera or kinematic models. In uncalibrated visual servoing, image Jacobian matrix estimation methods can be divided into two groups: the online method and the offline method. The offline method is not appropriate for most natural environments. The online method is robust but rough. Moreover, if the images feature configuration changes, it needs to restart the approximating procedure. A novel approach based on an online support vector regression (OL-SVR algorithm is proposed which overcomes the drawbacks and combines the virtues just mentioned.

  6. Public Supervision over Private Relationships : Towards European Supervision Private Law?

    NARCIS (Netherlands)

    Cherednychenko, O.O.

    2014-01-01

    The rise of public supervision over private relationships in many areas of private law has led to the development of what, in the author’s view, could be called ‘European supervision private law’. This emerging body of law forms part of European regulatory private law and is made up of

  7. Gear fault diagnosis under variable conditions with intrinsic time-scale decomposition-singular value decomposition and support vector machine

    Energy Technology Data Exchange (ETDEWEB)

    Xing, Zhanqiang; Qu, Jianfeng; Chai, Yi; Tang, Qiu; Zhou, Yuming [Chongqing University, Chongqing (China)

    2017-02-15

    The gear vibration signal is nonlinear and non-stationary, gear fault diagnosis under variable conditions has always been unsatisfactory. To solve this problem, an intelligent fault diagnosis method based on Intrinsic time-scale decomposition (ITD)-Singular value decomposition (SVD) and Support vector machine (SVM) is proposed in this paper. The ITD method is adopted to decompose the vibration signal of gearbox into several Proper rotation components (PRCs). Subsequently, the singular value decomposition is proposed to obtain the singular value vectors of the proper rotation components and improve the robustness of feature extraction under variable conditions. Finally, the Support vector machine is applied to classify the fault type of gear. According to the experimental results, the performance of ITD-SVD exceeds those of the time-frequency analysis methods with EMD and WPT combined with SVD for feature extraction, and the classifier of SVM outperforms those for K-nearest neighbors (K-NN) and Back propagation (BP). Moreover, the proposed approach can accurately diagnose and identify different fault types of gear under variable conditions.

  8. A relevance vector machine technique for the automatic detection of clustered microcalcifications (Honorable Mention Poster Award)

    Science.gov (United States)

    Wei, Liyang; Yang, Yongyi; Nishikawa, Robert M.

    2005-04-01

    Microcalcification (MC) clusters in mammograms can be important early signs of breast cancer in women. Accurate detection of MC clusters is an important but challenging problem. In this paper, we propose the use of a recently developed machine learning technique -- relevance vector machine (RVM) -- for automatic detection of MCs in digitized mammograms. RVM is based on Bayesian estimation theory, and as a feature it can yield a decision function that depends on only a very small number of so-called relevance vectors. We formulate MC detection as a supervised-learning problem, and use RVM to classify if an MC object is present or not at each location in a mammogram image. MC clusters are then identified by grouping the detected MC objects. The proposed method is tested using a database of 141 clinical mammograms, and compared with a support vector machine (SVM) classifier which we developed previously. The detection performance is evaluated using the free-response receiver operating characteristic (FROC) curves. It is demonstrated that the RVM classifier matches closely with the SVM classifier in detection performance, and does so with a much sparser kernel representation than the SVM classifier. Consequently, the RVM classifier greatly reduces the computational complexity, making it more suitable for real-time processing of MC clusters in mammograms.

  9. Support vector machine multiuser receiver for DS-CDMA signals in multipath channels.

    Science.gov (United States)

    Chen, S; Samingan, A K; Hanzo, L

    2001-01-01

    The problem of constructing an adaptive multiuser detector (MUD) is considered for direct sequence code division multiple access (DS-CDMA) signals transmitted through multipath channels. The emerging learning technique, called support vector machines (SVM), is proposed as a method of obtaining a nonlinear MUD from a relatively small training data block. Computer simulation is used to study this SVM MUD, and the results show that it can closely match the performance of the optimal Bayesian one-shot detector. Comparisons with an adaptive radial basis function (RBF) MUD trained by an unsupervised clustering algorithm are discussed.

  10. Forskellighed i supervision

    DEFF Research Database (Denmark)

    Petersen, Birgitte; Beck, Emma

    2009-01-01

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

  11. Supervision af psykoterapi

    DEFF Research Database (Denmark)

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

  12. A Hybrid Least Square Support Vector Machine Model with Parameters Optimization for Stock Forecasting

    Directory of Open Access Journals (Sweden)

    Jian Chai

    2015-01-01

    Full Text Available This paper proposes an EMD-LSSVM (empirical mode decomposition least squares support vector machine model to analyze the CSI 300 index. A WD-LSSVM (wavelet denoising least squares support machine is also proposed as a benchmark to compare with the performance of EMD-LSSVM. Since parameters selection is vital to the performance of the model, different optimization methods are used, including simplex, GS (grid search, PSO (particle swarm optimization, and GA (genetic algorithm. Experimental results show that the EMD-LSSVM model with GS algorithm outperforms other methods in predicting stock market movement direction.

  13. Linear and support vector regressions based on geometrical correlation of data

    Directory of Open Access Journals (Sweden)

    Kaijun Wang

    2007-10-01

    Full Text Available Linear regression (LR and support vector regression (SVR are widely used in data analysis. Geometrical correlation learning (GcLearn was proposed recently to improve the predictive ability of LR and SVR through mining and using correlations between data of a variable (inner correlation. This paper theoretically analyzes prediction performance of the GcLearn method and proves that GcLearn LR and SVR will have better prediction performance than traditional LR and SVR for prediction tasks when good inner correlations are obtained and predictions by traditional LR and SVR are far away from their neighbor training data under inner correlation. This gives the applicable condition of GcLearn method.

  14. Environmental noise forecasting based on support vector machine

    Science.gov (United States)

    Fu, Yumei; Zan, Xinwu; Chen, Tianyi; Xiang, Shihan

    2018-01-01

    As an important pollution source, the noise pollution is always the researcher's focus. Especially in recent years, the noise pollution is seriously harmful to the human beings' environment, so the research about the noise pollution is a very hot spot. Some noise monitoring technologies and monitoring systems are applied in the environmental noise test, measurement and evaluation. But, the research about the environmental noise forecasting is weak. In this paper, a real-time environmental noise monitoring system is introduced briefly. This monitoring system is working in Mianyang City, Sichuan Province. It is monitoring and collecting the environmental noise about more than 20 enterprises in this district. Based on the large amount of noise data, the noise forecasting by the Support Vector Machine (SVM) is studied in detail. Compared with the time series forecasting model and the artificial neural network forecasting model, the SVM forecasting model has some advantages such as the smaller data size, the higher precision and stability. The noise forecasting results based on the SVM can provide the important and accuracy reference to the prevention and control of the environmental noise.

  15. A technique to identify some typical radio frequency interference using support vector machine

    Science.gov (United States)

    Wang, Yuanchao; Li, Mingtao; Li, Dawei; Zheng, Jianhua

    2017-07-01

    In this paper, we present a technique to automatically identify some typical radio frequency interference from pulsar surveys using support vector machine. The technique has been tested by candidates. In these experiments, to get features of SVM, we use principal component analysis for mosaic plots and its classification accuracy is 96.9%; while we use mathematical morphology operation for smog plots and horizontal stripes plots and its classification accuracy is 86%. The technique is simple, high accurate and useful.

  16. Performance and optimization of support vector machines in high-energy physics classification problems

    International Nuclear Information System (INIS)

    Sahin, M.Ö.; Krücker, D.; Melzer-Pellmann, I.-A.

    2016-01-01

    In this paper we promote the use of Support Vector Machines (SVM) as a machine learning tool for searches in high-energy physics. As an example for a new-physics search we discuss the popular case of Supersymmetry at the Large Hadron Collider. We demonstrate that the SVM is a valuable tool and show that an automated discovery-significance based optimization of the SVM hyper-parameters is a highly efficient way to prepare an SVM for such applications.

  17. Performance and optimization of support vector machines in high-energy physics classification problems

    Energy Technology Data Exchange (ETDEWEB)

    Sahin, Mehmet Oezguer; Kruecker, Dirk; Melzer-Pellmann, Isabell [DESY, Hamburg (Germany)

    2016-07-01

    In this talk, the use of Support Vector Machines (SVM) is promoted for new-physics searches in high-energy physics. We developed an interface, called SVM HEP Interface (SVM-HINT), for a popular SVM library, LibSVM, and introduced a statistical-significance based hyper-parameter optimization algorithm for the new-physics searches. As example case study, a search for Supersymmetry at the Large Hadron Collider is given to demonstrate the capabilities of SVM using SVM-HINT.

  18. Performance and optimization of support vector machines in high-energy physics classification problems

    Energy Technology Data Exchange (ETDEWEB)

    Sahin, M.Ö., E-mail: ozgur.sahin@desy.de; Krücker, D., E-mail: dirk.kruecker@desy.de; Melzer-Pellmann, I.-A., E-mail: isabell.melzer@desy.de

    2016-12-01

    In this paper we promote the use of Support Vector Machines (SVM) as a machine learning tool for searches in high-energy physics. As an example for a new-physics search we discuss the popular case of Supersymmetry at the Large Hadron Collider. We demonstrate that the SVM is a valuable tool and show that an automated discovery-significance based optimization of the SVM hyper-parameters is a highly efficient way to prepare an SVM for such applications.

  19. Adding Cross-Platform Support to a High-Throughput Software Stack and Exploration of Vectorization Libraries

    CERN Document Server

    AUTHOR|(CDS)2258962

    This master thesis is written at the LHCb experiment at CERN. It is part of the initiative for improving software in view of the upcoming upgrade in 2021 which will significantly increase the amount of acquired data. This thesis consists of two parts. The first part is about the exploration of different vectorization libraries and their usefulness for the LHCb collaboration. The second part is about adding cross-platform support to the LHCb software stack. Here, the LHCb stack is successfully ported to ARM (aarch64) and its performance is analyzed. At the end of the thesis, the port to PowerPC(ppc64le) awaits the performance analysis. The main goal of porting the stack is the cost-performance evaluation for the different platforms to get the most cost efficient hardware for the new server farm for the upgrade. For this, selected vectorization libraries are extended to support the PowerPC and ARM platform. And though the same compiler is used, platform-specific changes to the compilation flags are required. In...

  20. Soft sensor development and optimization of the commercial petrochemical plant integrating support vector regression and genetic algorithm

    Directory of Open Access Journals (Sweden)

    S.K. Lahiri

    2009-09-01

    Full Text Available Soft sensors have been widely used in the industrial process control to improve the quality of the product and assure safety in the production. The core of a soft sensor is to construct a soft sensing model. This paper introduces support vector regression (SVR, a new powerful machine learning methodbased on a statistical learning theory (SLT into soft sensor modeling and proposes a new soft sensing modeling method based on SVR. This paper presents an artificial intelligence based hybrid soft sensormodeling and optimization strategies, namely support vector regression – genetic algorithm (SVR-GA for modeling and optimization of mono ethylene glycol (MEG quality variable in a commercial glycol plant. In the SVR-GA approach, a support vector regression model is constructed for correlating the process data comprising values of operating and performance variables. Next, model inputs describing the process operating variables are optimized using genetic algorithm with a view to maximize the process performance. The SVR-GA is a new strategy for soft sensor modeling and optimization. The major advantage of the strategies is that modeling and optimization can be conducted exclusively from the historic process data wherein the detailed knowledge of process phenomenology (reaction mechanism, kinetics etc. is not required. Using SVR-GA strategy, a number of sets of optimized operating conditions were found. The optimized solutions, when verified in an actual plant, resulted in a significant improvement in the quality.

  1. Noise model based ν-support vector regression with its application to short-term wind speed forecasting.

    Science.gov (United States)

    Hu, Qinghua; Zhang, Shiguang; Xie, Zongxia; Mi, Jusheng; Wan, Jie

    2014-09-01

    Support vector regression (SVR) techniques are aimed at discovering a linear or nonlinear structure hidden in sample data. Most existing regression techniques take the assumption that the error distribution is Gaussian. However, it was observed that the noise in some real-world applications, such as wind power forecasting and direction of the arrival estimation problem, does not satisfy Gaussian distribution, but a beta distribution, Laplacian distribution, or other models. In these cases the current regression techniques are not optimal. According to the Bayesian approach, we derive a general loss function and develop a technique of the uniform model of ν-support vector regression for the general noise model (N-SVR). The Augmented Lagrange Multiplier method is introduced to solve N-SVR. Numerical experiments on artificial data sets, UCI data and short-term wind speed prediction are conducted. The results show the effectiveness of the proposed technique. Copyright © 2014 Elsevier Ltd. All rights reserved.

  2. Support vector regression model for the estimation of γ-ray buildup factors for multi-layer shields

    International Nuclear Information System (INIS)

    Trontl, Kresimir; Smuc, Tomislav; Pevec, Dubravko

    2007-01-01

    The accuracy of the point-kernel method, which is a widely used practical tool for γ-ray shielding calculations, strongly depends on the quality and accuracy of buildup factors used in the calculations. Although, buildup factors for single-layer shields comprised of a single material are well known, calculation of buildup factors for stratified shields, each layer comprised of different material or a combination of materials, represent a complex physical problem. Recently, a new compact mathematical model for multi-layer shield buildup factor representation has been suggested for embedding into point-kernel codes thus replacing traditionally generated complex mathematical expressions. The new regression model is based on support vector machines learning technique, which is an extension of Statistical Learning Theory. The paper gives complete description of the novel methodology with results pertaining to realistic engineering multi-layer shielding geometries. The results based on support vector regression machine learning confirm that this approach provides a framework for general, accurate and computationally acceptable multi-layer buildup factor model

  3. Objective coding of content and techniques in workplace-based supervision of an EBT in public mental health.

    Science.gov (United States)

    Dorsey, Shannon; Kerns, Suzanne E U; Lucid, Leah; Pullmann, Michael D; Harrison, Julie P; Berliner, Lucy; Thompson, Kelly; Deblinger, Esther

    2018-01-24

    Workplace-based clinical supervision as an implementation strategy to support evidence-based treatment (EBT) in public mental health has received limited research attention. A commonly provided infrastructure support, it may offer a relatively cost-neutral implementation strategy for organizations. However, research has not objectively examined workplace-based supervision of EBT and specifically how it might differ from EBT supervision provided in efficacy and effectiveness trials. Data come from a descriptive study of supervision in the context of a state-funded EBT implementation effort. Verbal interactions from audio recordings of 438 supervision sessions between 28 supervisors and 70 clinicians from 17 public mental health organizations (in 23 offices) were objectively coded for presence and intensity coverage of 29 supervision strategies (16 content and 13 technique items), duration, and temporal focus. Random effects mixed models estimated proportion of variance in content and techniques attributable to the supervisor and clinician levels. Interrater reliability among coders was excellent. EBT cases averaged 12.4 min of supervision per session. Intensity of coverage for EBT content varied, with some discussed frequently at medium or high intensity (exposure) and others infrequently discussed or discussed only at low intensity (behavior management; assigning/reviewing client homework). Other than fidelity assessment, supervision techniques common in treatment trials (e.g., reviewing actual practice, behavioral rehearsal) were used rarely or primarily at low intensity. In general, EBT content clustered more at the clinician level; different techniques clustered at either the clinician or supervisor level. Workplace-based clinical supervision may be a feasible implementation strategy for supporting EBT implementation, yet it differs from supervision in treatment trials. Time allotted per case is limited, compressing time for EBT coverage. Techniques that

  4. Researching online supervision

    DEFF Research Database (Denmark)

    Bengtsen, Søren S. E.; Mathiasen, Helle

    2014-01-01

    Online supervision and the use of digital media in supervisory dialogues is a fast increasing practice in higher education today. However, the concepts in our pedagogical repertoire often reflect the digital tools used for supervision purposes as either a prolongation of the face-to-face contact...

  5. Implementation of Instructional Supervision in Secondary School ...

    African Journals Online (AJOL)

    Science, Technology and Arts Research Journal ... Supervision is critical in the development of any educational program in both developed and ... Clinical Supervision, Collegial Supervision, Self-directive supervision, Informal Supervision etc.

  6. The validation of the Supervision of Thesis Questionnaire (STQ).

    Science.gov (United States)

    Henricson, Maria; Fridlund, Bengt; Mårtensson, Jan; Hedberg, Berith

    2018-06-01

    The supervision process is characterized by differences between the supervisors' and the students' expectations before the start of writing a bachelor thesis as well as after its completion. A review of the literature did not reveal any scientifically tested questionnaire for evaluating nursing students' expectations of the supervision process when writing a bachelor thesis. The aim of the study was to determine the construct validity and internal consistency reliability of a questionnaire for measuring nursing students' expectations of the bachelor thesis supervision process. The study had a developmental and methodological design carried out in four steps including construct validity and internal consistency reliability statistical procedures: construction of the items, assessment of face validity, data collection and data analysis. This study was conducted at a university in southern Sweden, where students on the "Nursing student thesis, 15 ECTS" course were consecutively selected for participation. Of the 512 questionnaires distributed, 327 were returned, a response rate of 64%. Five factors with a total variance of 74% and good communalities, ≥0.64, were extracted from the 10-item STQ. The internal consistency of the 10 items was 0.68. The five factors were labelled: The nature of the supervision process, The supervisor's role as a coach, The students' progression to self-support, The interaction between students and supervisor and supervisor competence. A didactic, useful and secure questionnaire measuring nursing students' expectations of the bachelor thesis supervision process based on three main forms of supervision was created. Copyright © 2018 Elsevier Ltd. All rights reserved.

  7. Distributed data collection and supervision based on web sensor

    Science.gov (United States)

    He, Pengju; Dai, Guanzhong; Fu, Lei; Li, Xiangjun

    2006-11-01

    As a node in Internet/Intranet, web sensor has been promoted in recent years and wildly applied in remote manufactory, workshop measurement and control field. However, the conventional scheme can only support HTTP protocol, and the remote users supervise and control the collected data published by web in the standard browser because of the limited resource of the microprocessor in the sensor; moreover, only one node of data acquirement can be supervised and controlled in one instant therefore the requirement of centralized remote supervision, control and data process can not be satisfied in some fields. In this paper, the centralized remote supervision, control and data process by the web sensor are proposed and implemented by the principle of device driver program. The useless information of the every collected web page embedded in the sensor is filtered and the useful data is transmitted to the real-time database in the workstation, and different filter algorithms are designed for different sensors possessing independent web pages. Every sensor node has its own filter program of web, called "web data collection driver program", the collecting details are shielded, and the supervision, control and configuration software can be implemented by the call of web data collection driver program just like the use of the I/O driver program. The proposed technology can be applied in the data acquirement where relative low real-time is required.

  8. Exploiting unsupervised and supervised classification for segmentation of the pathological lung in CT

    International Nuclear Information System (INIS)

    Korfiatis, P; Costaridou, L; Kalogeropoulou, C; Petsas, T; Daoussis, D; Adonopoulos, A

    2009-01-01

    Delineation of lung fields in presence of diffuse lung diseases (DLPDs), such as interstitial pneumonias (IP), challenges segmentation algorithms. To deal with IP patterns affecting the lung border an automated image texture classification scheme is proposed. The proposed segmentation scheme is based on supervised texture classification between lung tissue (normal and abnormal) and surrounding tissue (pleura and thoracic wall) in the lung border region. This region is coarsely defined around an initial estimate of lung border, provided by means of Markov Radom Field modeling and morphological operations. Subsequently, a support vector machine classifier was trained to distinguish between the above two classes of tissue, using textural feature of gray scale and wavelet domains. 17 patients diagnosed with IP, secondary to connective tissue diseases were examined. Segmentation performance in terms of overlap was 0.924±0.021, and for shape differentiation mean, rms and maximum distance were 1.663±0.816, 2.334±1.574 and 8.0515±6.549 mm, respectively. An accurate, automated scheme is proposed for segmenting abnormal lung fields in HRC affected by IP

  9. Exploiting unsupervised and supervised classification for segmentation of the pathological lung in CT

    Science.gov (United States)

    Korfiatis, P.; Kalogeropoulou, C.; Daoussis, D.; Petsas, T.; Adonopoulos, A.; Costaridou, L.

    2009-07-01

    Delineation of lung fields in presence of diffuse lung diseases (DLPDs), such as interstitial pneumonias (IP), challenges segmentation algorithms. To deal with IP patterns affecting the lung border an automated image texture classification scheme is proposed. The proposed segmentation scheme is based on supervised texture classification between lung tissue (normal and abnormal) and surrounding tissue (pleura and thoracic wall) in the lung border region. This region is coarsely defined around an initial estimate of lung border, provided by means of Markov Radom Field modeling and morphological operations. Subsequently, a support vector machine classifier was trained to distinguish between the above two classes of tissue, using textural feature of gray scale and wavelet domains. 17 patients diagnosed with IP, secondary to connective tissue diseases were examined. Segmentation performance in terms of overlap was 0.924±0.021, and for shape differentiation mean, rms and maximum distance were 1.663±0.816, 2.334±1.574 and 8.0515±6.549 mm, respectively. An accurate, automated scheme is proposed for segmenting abnormal lung fields in HRC affected by IP

  10. hERG classification model based on a combination of support vector machine method and GRIND descriptors

    DEFF Research Database (Denmark)

    Li, Qiyuan; Jorgensen, Flemming Steen; Oprea, Tudor

    2008-01-01

    and diverse library of 495 compounds. The models combine pharmacophore-based GRIND descriptors with a support vector machine (SVM) classifier in order to discriminate between hERG blockers and nonblockers. Our models were applied at different thresholds from 1 to 40 mu m and achieved an overall accuracy up...

  11. Development and validation of the ExPRESS instrument for primary health care providers' evaluation of external supervision.

    Science.gov (United States)

    Schriver, Michael; Cubaka, Vincent Kalumire; Vedsted, Peter; Besigye, Innocent; Kallestrup, Per

    2018-01-01

    External supervision of primary health care facilities to monitor and improve services is common in low-income countries. Currently there are no tools to measure the quality of support in external supervision in these countries. To develop a provider-reported instrument to assess the support delivered through external supervision in Rwanda and other countries. "External supervision: Provider Evaluation of Supervisor Support" (ExPRESS) was developed in 18 steps, primarily in Rwanda. Content validity was optimised using systematic search for related instruments, interviews, translations, and relevance assessments by international supervision experts as well as local experts in Nigeria, Kenya, Uganda and Rwanda. Construct validity and reliability were examined in two separate field tests, the first using exploratory factor analysis and a test-retest design, the second for confirmatory factor analysis. We included 16 items in section A ('The most recent experience with an external supervisor'), and 13 items in section B ('The overall experience with external supervisors'). Item-content validity index was acceptable. In field test I, test-retest had acceptable kappa values and exploratory factor analysis suggested relevant factors in sections A and B used for model hypotheses. In field test II, models were tested by confirmatory factor analysis fitting a 4-factor model for section A, and a 3-factor model for section B. ExPRESS is a promising tool for evaluation of the quality of support of primary health care providers in external supervision of primary health care facilities in resource-constrained settings. ExPRESS may be used as specific feedback to external supervisors to help identify and address gaps in the supervision they provide. Further studies should determine optimal interpretation of scores and the number of respondents needed per supervisor to obtain precise results, as well as test the functionality of section B.

  12. Abusive supervision, psychosomatic symptoms, and deviance: Can job autonomy make a difference?

    Science.gov (United States)

    Velez, Maria João; Neves, Pedro

    2016-07-01

    Recently, interest in abusive supervision has grown (Tepper, 2000). However, little is still known about organizational factors that can reduce its adverse effects on employee behavior. Based on the Job Demands-Resources Model (Demerouti, Bakker, Nachreiner, & Schaufeli, 2001), we predict that job autonomy acts as a buffer of the positive relationship between abusive supervision, psychosomatic symptoms and deviance. Therefore, when job autonomy is low, a higher level of abusive supervision should be accompanied by increased psychosomatic symptoms and thus lead to higher production deviance. When job autonomy is high, abusive supervision should fail to produce increased psychosomatic symptoms and thus should not lead to higher production deviance. Our model was explored among a sample of 170 supervisor-subordinate dyads from 4 organizations. The results of the moderated mediation analysis supported our hypotheses. That is, abusive supervision was significantly related to production deviance via psychosomatic symptoms when job autonomy was low, but not when job autonomy was high. These findings suggest that job autonomy buffers the impact of abusive supervision perceptions on psychosomatic symptoms, with consequences for production deviance. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  13. 20 CFR 656.21 - Supervised recruitment.

    Science.gov (United States)

    2010-04-01

    ... 20 Employees' Benefits 3 2010-04-01 2010-04-01 false Supervised recruitment. 656.21 Section 656.21... Supervised recruitment. (a) Supervised recruitment. Where the Certifying Officer determines it appropriate, post-filing supervised recruitment may be required of the employer for the pending application or...

  14. BEHAVIOR BASED CREDIT CARD FRAUD DETECTION USING SUPPORT VECTOR MACHINES

    Directory of Open Access Journals (Sweden)

    V. Dheepa

    2012-07-01

    Full Text Available Along with the great increase of internet and e-commerce, the use of credit card is an unavoidable one. Due to the increase of credit card usage, the frauds associated with this have also increased. There are a lot of approaches used to detect the frauds. In this paper, behavior based classification approach using Support Vector Machines are employed and efficient feature extraction method also adopted. If any discrepancies occur in the behaviors transaction pattern then it is predicted as suspicious and taken for further consideration to find the frauds. Generally credit card fraud detection problem suffers from a large amount of data, which is rectified by the proposed method. Achieving finest accuracy, high fraud catching rate and low false alarms are the main tasks of this approach.

  15. Supervision Duty of School Principals

    Directory of Open Access Journals (Sweden)

    Kürşat YILMAZ

    2009-04-01

    Full Text Available Supervision by school administrators is becoming more and more important. The change in the roles ofschool administrators has a great effect on that increase. At present, school administrators are consideredmore than as technical directors, but as instructional leaders. This increased the importance of schooladministrators’ expected supervision acts. In this respect, the aim of this study is to make a conceptualanalysis about school administrators’ supervision duties. For this reason, a literature review related withsupervision and contemporary supervision approaches was done, and the official documents concerningsupervision were examined. As a result, it can be said that school administrators’ supervision duties havebecome very important. And these duties must certainly be carried out by school administrators.

  16. A SURVEY OF SEMI-SUPERVISED LEARNING

    OpenAIRE

    Amrita Sadarangani *, Dr. Anjali Jivani

    2016-01-01

    Semi Supervised Learning involves using both labeled and unlabeled data to train a classifier or for clustering. Semi supervised learning finds usage in many applications, since labeled data can be hard to find in many cases. Currently, a lot of research is being conducted in this area. This paper discusses the different algorithms of semi supervised learning and then their advantages and limitations are compared. The differences between supervised classification and semi-supervised classific...

  17. Modeling a ground-coupled heat pump system by a support vector machine

    Energy Technology Data Exchange (ETDEWEB)

    Esen, Hikmet; Esen, Mehmet [Department of Mechanical Education, Faculty of Technical Education, Firat University, 23119 Elazig (Turkey); Inalli, Mustafa [Department of Mechanical Engineering, Faculty of Engineering, Firat University, 23279 Elazig (Turkey); Sengur, Abdulkadir [Department of Electronic and Computer Science, Faculty of Technical Education, Firat University, 23119 Elazig (Turkey)

    2008-08-15

    This paper reports on a modeling study of ground coupled heat pump (GCHP) system performance (COP) by using a support vector machine (SVM) method. A GCHP system is a multi-variable system that is hard to model by conventional methods. As regards the SVM, it has a superior capability for generalization, and this capability is independent of the dimensionality of the input data. In this study, a SVM based method was intended to adopt GCHP system for efficient modeling. The Lin-kernel SVM method was quite efficient in modeling purposes and did not require a pre-knowledge about the system. The performance of the proposed methodology was evaluated by using several statistical validation parameters. It is found that the root-mean squared (RMS) value is 0.002722, the coefficient of multiple determinations (R{sup 2}) value is 0.999999, coefficient of variation (cov) value is 0.077295, and mean error function (MEF) value is 0.507437 for the proposed Lin-kernel SVM method. The optimum parameters of the SVM method were determined by using a greedy search algorithm. This search algorithm was effective for obtaining the optimum parameters. The simulation results show that the SVM is a good method for prediction of the COP of the GCHP system. The computation of SVM model is faster compared with other machine learning techniques (artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS)); because there are fewer free parameters and only support vectors (only a fraction of all data) are used in the generalization process. (author)

  18. Moment constrained semi-supervised LDA

    DEFF Research Database (Denmark)

    Loog, Marco

    2012-01-01

    This BNAIC compressed contribution provides a summary of the work originally presented at the First IAPR Workshop on Partially Supervised Learning and published in [5]. It outlines the idea behind supervised and semi-supervised learning and highlights the major shortcoming of many current methods...

  19. Intellectual Production Supervision Perform based on RFID Smart Electricity Meter

    Science.gov (United States)

    Chen, Xiangqun; Huang, Rui; Shen, Liman; chen, Hao; Xiong, Dezhi; Xiao, Xiangqi; Liu, Mouhai; Xu, Renheng

    2018-03-01

    This topic develops the RFID intelligent electricity meter production supervision project management system. The system is designed for energy meter production supervision in the management of the project schedule, quality and cost information management requirements in RFID intelligent power, and provide quantitative information more comprehensive, timely and accurate for supervision engineer and project manager management decisions, and to provide technical information for the product manufacturing stage file. From the angle of scheme analysis, design, implementation and test, the system development of production supervision project management system for RFID smart meter project is discussed. Focus on the development of the system, combined with the main business application and management mode at this stage, focuses on the energy meter to monitor progress information, quality information and cost based information on RFID intelligent power management function. The paper introduces the design scheme of the system, the overall client / server architecture, client oriented graphical user interface universal, complete the supervision of project management and interactive transaction information display, the server system of realizing the main program. The system is programmed with C# language and.NET operating environment, and the client and server platforms use Windows operating system, and the database server software uses Oracle. The overall platform supports mainstream information and standards and has good scalability.

  20. Automatic Task Classification via Support Vector Machine and Crowdsourcing

    Directory of Open Access Journals (Sweden)

    Hyungsik Shin

    2018-01-01

    Full Text Available Automatic task classification is a core part of personal assistant systems that are widely used in mobile devices such as smartphones and tablets. Even though many industry leaders are providing their own personal assistant services, their proprietary internals and implementations are not well known to the public. In this work, we show through real implementation and evaluation that automatic task classification can be implemented for mobile devices by using the support vector machine algorithm and crowdsourcing. To train our task classifier, we collected our training data set via crowdsourcing using the Amazon Mechanical Turk platform. Our classifier can classify a short English sentence into one of the thirty-two predefined tasks that are frequently requested while using personal mobile devices. Evaluation results show high prediction accuracy of our classifier ranging from 82% to 99%. By using large amount of crowdsourced data, we also illustrate the relationship between training data size and the prediction accuracy of our task classifier.

  1. Automatic inspection of textured surfaces by support vector machines

    Science.gov (United States)

    Jahanbin, Sina; Bovik, Alan C.; Pérez, Eduardo; Nair, Dinesh

    2009-08-01

    Automatic inspection of manufactured products with natural looking textures is a challenging task. Products such as tiles, textile, leather, and lumber project image textures that cannot be modeled as periodic or otherwise regular; therefore, a stochastic modeling of local intensity distribution is required. An inspection system to replace human inspectors should be flexible in detecting flaws such as scratches, cracks, and stains occurring in various shapes and sizes that have never been seen before. A computer vision algorithm is proposed in this paper that extracts local statistical features from grey-level texture images decomposed with wavelet frames into subbands of various orientations and scales. The local features extracted are second order statistics derived from grey-level co-occurrence matrices. Subsequently, a support vector machine (SVM) classifier is trained to learn a general description of normal texture from defect-free samples. This algorithm is implemented in LabVIEW and is capable of processing natural texture images in real-time.

  2. Probability Distribution and Deviation Information Fusion Driven Support Vector Regression Model and Its Application

    Directory of Open Access Journals (Sweden)

    Changhao Fan

    2017-01-01

    Full Text Available In modeling, only information from the deviation between the output of the support vector regression (SVR model and the training sample is considered, whereas the other prior information of the training sample, such as probability distribution information, is ignored. Probabilistic distribution information describes the overall distribution of sample data in a training sample that contains different degrees of noise and potential outliers, as well as helping develop a high-accuracy model. To mine and use the probability distribution information of a training sample, a new support vector regression model that incorporates probability distribution information weight SVR (PDISVR is proposed. In the PDISVR model, the probability distribution of each sample is considered as the weight and is then introduced into the error coefficient and slack variables of SVR. Thus, the deviation and probability distribution information of the training sample are both used in the PDISVR model to eliminate the influence of noise and outliers in the training sample and to improve predictive performance. Furthermore, examples with different degrees of noise were employed to demonstrate the performance of PDISVR, which was then compared with those of three SVR-based methods. The results showed that PDISVR performs better than the three other methods.

  3. A SUPPORT VECTOR MACHINE APPROACH FOR DEVELOPING TELEMEDICINE SOLUTIONS: MEDICAL DIAGNOSIS

    Directory of Open Access Journals (Sweden)

    Mihaela GHEORGHE

    2015-06-01

    Full Text Available Support vector machine represents an important tool for artificial neural networks techniques including classification and prediction. It offers a solution for a wide range of different issues in which cases the traditional optimization algorithms and methods cannot be applied directly due to different constraints, including memory restrictions, hidden relationships between variables, very high volume of computations that needs to be handled. One of these issues relates to medical diagnosis, a subset of the medical field. In this paper, the SVM learning algorithm is tested on a diabetes dataset and the results obtained for training with different kernel functions are presented and analyzed in order to determine a good approach from a telemedicine perspective.

  4. Mapping landslide source and transport areas in VHR images with Object-Based Analysis and Support Vector Machines

    Science.gov (United States)

    Heleno, Sandra; Matias, Magda; Pina, Pedro

    2015-04-01

    Visual interpretation of satellite imagery remains extremely demanding in terms of resources and time, especially when dealing with numerous multi-scale landslides affecting wide areas, such as is the case of rainfall-induced shallow landslides. Applying automated methods can contribute to more efficient landslide mapping and updating of existing inventories, and in recent years the number and variety of approaches is rapidly increasing. Very High Resolution (VHR) images, acquired by space-borne sensors with sub-metric precision, such as Ikonos, Quickbird, Geoeye and Worldview, are increasingly being considered as the best option for landslide mapping, but these new levels of spatial detail also present new challenges to state of the art image analysis tools, asking for automated methods specifically suited to map landslide events on VHR optical images. In this work we develop and test a methodology for semi-automatic landslide recognition and mapping of landslide source and transport areas. The method combines object-based image analysis and a Support Vector Machine supervised learning algorithm, and was tested using a GeoEye-1 multispectral image, sensed 3 days after a damaging landslide event in Madeira Island, together with a pre-event LiDAR DEM. Our approach has proved successful in the recognition of landslides on a 15 Km2-wide study area, with 81 out of 85 landslides detected in its validation regions. The classifier also showed reasonable performance (false positive rate 60% and false positive rate below 36% in both validation regions) in the internal mapping of landslide source and transport areas, in particular in the sunnier east-facing slopes. In the less illuminated areas the classifier is still able to accurately map the source areas, but performs poorly in the mapping of landslide transport areas.

  5. Asco 2044 nuclear power plant: supervision; Central nuclear Asco 2044: supervision

    Energy Technology Data Exchange (ETDEWEB)

    Sabartes, J.

    2010-07-01

    Good supervision constitutes an efficient barrier to avoid the errors caused by inadequate work practices. In this sense, it is necessary to strengthen supervision to make sure that the work is carried out with adequate human performance, tending to avoid error ande provinding safety quality and efficiency at work. (Author).

  6. Whither Supervision?

    OpenAIRE

    Duncan Waite

    2006-01-01

    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...

  7. Advanced Music Therapy Supervision Training

    DEFF Research Database (Denmark)

    Pedersen, Inge Nygaard

    2009-01-01

    supervision training excerpts live in the workshop will be offered. The workshop will include demonstrating a variety of supervision methods and techniques used in A) post graduate music therapy training programs b) a variety of work contexts such as psychiatry and somatic music psychotherapy. The workshop......The presentation will illustrate training models in supervision for experienced music therapists where transference/counter transference issues are in focus. Musical, verbal and body related tools will be illustrated from supervision practice by the presenters. A possibility to experience small...

  8. Human semi-supervised learning.

    Science.gov (United States)

    Gibson, Bryan R; Rogers, Timothy T; Zhu, Xiaojin

    2013-01-01

    Most empirical work in human categorization has studied learning in either fully supervised or fully unsupervised scenarios. Most real-world learning scenarios, however, are semi-supervised: Learners receive a great deal of unlabeled information from the world, coupled with occasional experiences in which items are directly labeled by a knowledgeable source. A large body of work in machine learning has investigated how learning can exploit both labeled and unlabeled data provided to a learner. Using equivalences between models found in human categorization and machine learning research, we explain how these semi-supervised techniques can be applied to human learning. A series of experiments are described which show that semi-supervised learning models prove useful for explaining human behavior when exposed to both labeled and unlabeled data. We then discuss some machine learning models that do not have familiar human categorization counterparts. Finally, we discuss some challenges yet to be addressed in the use of semi-supervised models for modeling human categorization. Copyright © 2013 Cognitive Science Society, Inc.

  9. Individualized prediction of illness course at the first psychotic episode: a support vector machine MRI study.

    LENUS (Irish Health Repository)

    Mourao-Miranda, J

    2012-05-01

    To date, magnetic resonance imaging (MRI) has made little impact on the diagnosis and monitoring of psychoses in individual patients. In this study, we used a support vector machine (SVM) whole-brain classification approach to predict future illness course at the individual level from MRI data obtained at the first psychotic episode.

  10. Clinical supervision of nursing students: challenges and alternatives ...

    African Journals Online (AJOL)

    Clinical supervision is described as a formal process of professional learning support in the clinical practice. The goal of clinical practice is to prepare nursing students develop and apply the necessary theoretical and empirical knowledge and skills in order for them to practice as safely and effectively as professional nurses.

  11. Using a Mixed Model to Explore Evaluation Criteria for Bank Supervision: A Banking Supervision Law Perspective.

    Directory of Open Access Journals (Sweden)

    Sang-Bing Tsai

    Full Text Available Financial supervision means that monetary authorities have the power to supervise and manage financial institutions according to laws. Monetary authorities have this power because of the requirements of improving financial services, protecting the rights of depositors, adapting to industrial development, ensuring financial fair trade, and maintaining stable financial order. To establish evaluation criteria for bank supervision in China, this study integrated fuzzy theory and the decision making trial and evaluation laboratory (DEMATEL and proposes a fuzzy-DEMATEL model. First, fuzzy theory was applied to examine bank supervision criteria and analyze fuzzy semantics. Second, the fuzzy-DEMATEL model was used to calculate the degree to which financial supervision criteria mutually influenced one another and their causal relationship. Finally, an evaluation criteria model for evaluating bank and financial supervision was established.

  12. Using a Mixed Model to Explore Evaluation Criteria for Bank Supervision: A Banking Supervision Law Perspective

    Science.gov (United States)

    Tsai, Sang-Bing; Chen, Kuan-Yu; Zhao, Hongrui; Wei, Yu-Min; Wang, Cheng-Kuang; Zheng, Yuxiang; Chang, Li-Chung; Wang, Jiangtao

    2016-01-01

    Financial supervision means that monetary authorities have the power to supervise and manage financial institutions according to laws. Monetary authorities have this power because of the requirements of improving financial services, protecting the rights of depositors, adapting to industrial development, ensuring financial fair trade, and maintaining stable financial order. To establish evaluation criteria for bank supervision in China, this study integrated fuzzy theory and the decision making trial and evaluation laboratory (DEMATEL) and proposes a fuzzy-DEMATEL model. First, fuzzy theory was applied to examine bank supervision criteria and analyze fuzzy semantics. Second, the fuzzy-DEMATEL model was used to calculate the degree to which financial supervision criteria mutually influenced one another and their causal relationship. Finally, an evaluation criteria model for evaluating bank and financial supervision was established. PMID:27992449

  13. Successive overrelaxation for laplacian support vector machine.

    Science.gov (United States)

    Qi, Zhiquan; Tian, Yingjie; Shi, Yong

    2015-04-01

    Semisupervised learning (SSL) problem, which makes use of both a large amount of cheap unlabeled data and a few unlabeled data for training, in the last few years, has attracted amounts of attention in machine learning and data mining. Exploiting the manifold regularization (MR), Belkin et al. proposed a new semisupervised classification algorithm: Laplacian support vector machines (LapSVMs), and have shown the state-of-the-art performance in SSL field. To further improve the LapSVMs, we proposed a fast Laplacian SVM (FLapSVM) solver for classification. Compared with the standard LapSVM, our method has several improved advantages as follows: 1) FLapSVM does not need to deal with the extra matrix and burden the computations related to the variable switching, which make it more suitable for large scale problems; 2) FLapSVM’s dual problem has the same elegant formulation as that of standard SVMs. This means that the kernel trick can be applied directly into the optimization model; and 3) FLapSVM can be effectively solved by successive overrelaxation technology, which converges linearly to a solution and can process very large data sets that need not reside in memory. In practice, combining the strategies of random scheduling of subproblem and two stopping conditions, the computing speed of FLapSVM is rigidly quicker to that of LapSVM and it is a valid alternative to PLapSVM.

  14. Supervision in banking industry

    OpenAIRE

    Šmída, David

    2012-01-01

    The aim of submitted thesis Supervision in banking is to define the nature and the importance of banking supervision, to justify its existence and to analyze the applicable mechanisms while the system of banking regulation and supervision in this thesis is primarily examined in the European context, with a focus on the Czech Republic. The thesis is divided into five main chapters. The first chapter is devoted to the financial system and the importance of banks in this system, it defines the c...

  15. Supervised learning methods for pathological arterial pulse wave differentiation: A SVM and neural networks approach.

    Science.gov (United States)

    Paiva, Joana S; Cardoso, João; Pereira, Tânia

    2018-01-01

    The main goal of this study was to develop an automatic method based on supervised learning methods, able to distinguish healthy from pathologic arterial pulse wave (APW), and those two from noisy waveforms (non-relevant segments of the signal), from the data acquired during a clinical examination with a novel optical system. The APW dataset analysed was composed by signals acquired in a clinical environment from a total of 213 subjects, including healthy volunteers and non-healthy patients. The signals were parameterised by means of 39pulse features: morphologic, time domain statistics, cross-correlation features, wavelet features. Multiclass Support Vector Machine Recursive Feature Elimination (SVM RFE) method was used to select the most relevant features. A comparative study was performed in order to evaluate the performance of the two classifiers: Support Vector Machine (SVM) and Artificial Neural Network (ANN). SVM achieved a statistically significant better performance for this problem with an average accuracy of 0.9917±0.0024 and a F-Measure of 0.9925±0.0019, in comparison with ANN, which reached the values of 0.9847±0.0032 and 0.9852±0.0031 for Accuracy and F-Measure, respectively. A significant difference was observed between the performances obtained with SVM classifier using a different number of features from the original set available. The comparison between SVM and NN allowed reassert the higher performance of SVM. The results obtained in this study showed the potential of the proposed method to differentiate those three important signal outcomes (healthy, pathologic and noise) and to reduce bias associated with clinical diagnosis of cardiovascular disease using APW. Copyright © 2017 Elsevier B.V. All rights reserved.

  16. SVM Classifier – a comprehensive java interface for support vector machine classification of microarray data

    Science.gov (United States)

    Pirooznia, Mehdi; Deng, Youping

    2006-01-01

    Motivation Graphical user interface (GUI) software promotes novelty by allowing users to extend the functionality. SVM Classifier is a cross-platform graphical application that handles very large datasets well. The purpose of this study is to create a GUI application that allows SVM users to perform SVM training, classification and prediction. Results The GUI provides user-friendly access to state-of-the-art SVM methods embodied in the LIBSVM implementation of Support Vector Machine. We implemented the java interface using standard swing libraries. We used a sample data from a breast cancer study for testing classification accuracy. We achieved 100% accuracy in classification among the BRCA1–BRCA2 samples with RBF kernel of SVM. Conclusion We have developed a java GUI application that allows SVM users to perform SVM training, classification and prediction. We have demonstrated that support vector machines can accurately classify genes into functional categories based upon expression data from DNA microarray hybridization experiments. Among the different kernel functions that we examined, the SVM that uses a radial basis kernel function provides the best performance. The SVM Classifier is available at . PMID:17217518

  17. SSC-EKE: Semi-Supervised Classification with Extensive Knowledge Exploitation.

    Science.gov (United States)

    Qian, Pengjiang; Xi, Chen; Xu, Min; Jiang, Yizhang; Su, Kuan-Hao; Wang, Shitong; Muzic, Raymond F

    2018-01-01

    We introduce a new, semi-supervised classification method that extensively exploits knowledge. The method has three steps. First, the manifold regularization mechanism, adapted from the Laplacian support vector machine (LapSVM), is adopted to mine the manifold structure embedded in all training data, especially in numerous label-unknown data. Meanwhile, by converting the labels into pairwise constraints, the pairwise constraint regularization formula (PCRF) is designed to compensate for the few but valuable labelled data. Second, by further combining the PCRF with the manifold regularization, the precise manifold and pairwise constraint jointly regularized formula (MPCJRF) is achieved. Third, by incorporating the MPCJRF into the framework of the conventional SVM, our approach, referred to as semi-supervised classification with extensive knowledge exploitation (SSC-EKE), is developed. The significance of our research is fourfold: 1) The MPCJRF is an underlying adjustment, with respect to the pairwise constraints, to the graph Laplacian enlisted for approximating the potential data manifold. This type of adjustment plays the correction role, as an unbiased estimation of the data manifold is difficult to obtain, whereas the pairwise constraints, converted from the given labels, have an overall high confidence level. 2) By transforming the values of the two terms in the MPCJRF such that they have the same range, with a trade-off factor varying within the invariant interval [0, 1), the appropriate impact of the pairwise constraints to the graph Laplacian can be self-adaptively determined. 3) The implication regarding extensive knowledge exploitation is embodied in SSC-EKE. That is, the labelled examples are used not only to control the empirical risk but also to constitute the MPCJRF. Moreover, all data, both labelled and unlabelled, are recruited for the model smoothness and manifold regularization. 4) The complete framework of SSC-EKE organically incorporates multiple

  18. Nonlinear structural damage detection using support vector machines

    Science.gov (United States)

    Xiao, Li; Qu, Wenzhong

    2012-04-01

    An actual structure including connections and interfaces may exist nonlinear. Because of many complicated problems about nonlinear structural health monitoring (SHM), relatively little progress have been made in this aspect. Statistical pattern recognition techniques have been demonstrated to be competitive with other methods when applied to real engineering datasets. When a structure existing 'breathing' cracks that open and close under operational loading may cause a linear structural system to respond to its operational and environmental loads in a nonlinear manner nonlinear. In this paper, a vibration-based structural health monitoring when the structure exists cracks is investigated with autoregressive support vector machine (AR-SVM). Vibration experiments are carried out with a model frame. Time-series data in different cases such as: initial linear structure; linear structure with mass changed; nonlinear structure; nonlinear structure with mass changed are acquired.AR model of acceleration time-series is established, and different kernel function types and corresponding parameters are chosen and compared, which can more accurate, more effectively locate the damage. Different cases damaged states and different damage positions have been recognized successfully. AR-SVM method for the insufficient training samples is proved to be practical and efficient on structure nonlinear damage detection.

  19. 17 CFR 166.3 - Supervision.

    Science.gov (United States)

    2010-04-01

    ... 17 Commodity and Securities Exchanges 1 2010-04-01 2010-04-01 false Supervision. 166.3 Section 166.3 Commodity and Securities Exchanges COMMODITY FUTURES TRADING COMMISSION CUSTOMER PROTECTION RULES § 166.3 Supervision. Each Commission registrant, except an associated person who has no supervisory duties, must diligently supervise the handling b...

  20. 28 CFR 810.1 - Supervision contact requirements.

    Science.gov (United States)

    2010-07-01

    ... 28 Judicial Administration 2 2010-07-01 2010-07-01 false Supervision contact requirements. 810.1 Section 810.1 Judicial Administration COURT SERVICES AND OFFENDER SUPERVISION AGENCY FOR THE DISTRICT OF COLUMBIA COMMUNITY SUPERVISION: ADMINISTRATIVE SANCTIONS § 810.1 Supervision contact requirements. If you are an offender under supervision by th...

  1. Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations

    KAUST Repository

    Smaili, Fatima Z.; Gao, Xin; Hoehndorf, Robert

    2018-01-01

    We propose the Onto2Vec method, an approach to learn feature vectors for biological entities based on their annotations to biomedical ontologies. Our method can be applied to a wide range of bioinformatics research problems such as similarity-based prediction of interactions between proteins, classification of interaction types using supervised learning, or clustering.

  2. Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations

    KAUST Repository

    Smaili, Fatima Zohra

    2018-01-31

    We propose the Onto2Vec method, an approach to learn feature vectors for biological entities based on their annotations to biomedical ontologies. Our method can be applied to a wide range of bioinformatics research problems such as similarity-based prediction of interactions between proteins, classification of interaction types using supervised learning, or clustering.

  3. Kernel method for clustering based on optimal target vector

    International Nuclear Information System (INIS)

    Angelini, Leonardo; Marinazzo, Daniele; Pellicoro, Mario; Stramaglia, Sebastiano

    2006-01-01

    We introduce Ising models, suitable for dichotomic clustering, with couplings that are (i) both ferro- and anti-ferromagnetic (ii) depending on the whole data-set and not only on pairs of samples. Couplings are determined exploiting the notion of optimal target vector, here introduced, a link between kernel supervised and unsupervised learning. The effectiveness of the method is shown in the case of the well-known iris data-set and in benchmarks of gene expression levels, where it works better than existing methods for dichotomic clustering

  4. What influences palliative care nurses in their choice to engage in or decline clinical supervision?

    Science.gov (United States)

    Puffett, Nick; Perkins, Paul

    2017-11-02

    Clinical supervision (CS) has been around since the early 1990s in the UK and has been endorsed by government and professional bodies. Levels of engagement range from 18% to 85%. To investigate what influences palliative care nurses in their choice to engage in or decline clinical supervision. A qualitative study was undertaken in an inpatient hospice in England and employed two focus groups to compare the views of participants and non-participants in CS. Data were audio recorded and transcribed verbatim by the researchers and analysed using systematic text condensation. Palliative care nurses all used informal team support for 'in the moment' support. Some engaged in formal CS to reflect 'on action' and to challenge practice. Nurses reported a lack of clarity regarding CS but, once this was overcome and engagement with CS was established, it led to changes in practice, identification of training needs and team building. The option of choice between group and individual supervision was found to be important. Group supervision led to enhanced understanding of group members which also led to team building, individual sessions were useful for individual issues. Protected time was essential for staff to be able to engage in CS. Staff who worked in larger teams reported higher levels of engagement, whereas a small team reported less need due to more informal team support. These findings are positive as they illuminate the importance of choice for support. Nurses need to be aware of their options for support and ultimately how this support affects the care they provide. The Palliative Care Nurse's Model of Support was developed, which shows the effects of each choice and how this may lead to team-building.

  5. Mentoring, coaching and supervision

    OpenAIRE

    McMahon, Samantha; Dyer, Mary; Barker, Catherine

    2016-01-01

    This chapter considers the purpose of coaching, mentoring and supervision in early childhood eduaction and care. It examines a number of different approaches and considers the key skills required for effective coaching, mentoring and supervision.

  6. Using Doctoral Experience Survey Data to Support Developments in Postgraduate Supervision and Support

    Directory of Open Access Journals (Sweden)

    Lucy Johnston

    2016-06-01

    Full Text Available Provision of both high standards of thesis supervision and high quality research environments are required for doctoral candidates to flourish. An important component of ensuring quality provision of research resources is the soliciting of feedback from research students and the provision from research supervisors and institutions of timely and constructive responses to such feedback. In this manuscript we describe the use of locally developed survey instruments to elicit student feedback. We then demonstrate how actions taken in response to this student feedback can help establish a virtuous circle that enhances doctoral students’ research experiences. We provide examples of changes to supervisory practice and resource allocation based on feedback and show the positive impact on subsequent student evaluations. While the examples included here are local, the issues considered and the methods and interventions developed are applicable to all institutions offering research degrees.

  7. Using support vector machines to improve elemental ion identification in macromolecular crystal structures

    Energy Technology Data Exchange (ETDEWEB)

    Morshed, Nader [University of California, Berkeley, CA 94720 (United States); Lawrence Berkeley National Laboratory, Berkeley, CA 94720 (United States); Echols, Nathaniel, E-mail: nechols@lbl.gov [Lawrence Berkeley National Laboratory, Berkeley, CA 94720 (United States); Adams, Paul D., E-mail: nechols@lbl.gov [Lawrence Berkeley National Laboratory, Berkeley, CA 94720 (United States); University of California, Berkeley, CA 94720 (United States)

    2015-05-01

    A method to automatically identify possible elemental ions in X-ray crystal structures has been extended to use support vector machine (SVM) classifiers trained on selected structures in the PDB, with significantly improved sensitivity over manually encoded heuristics. In the process of macromolecular model building, crystallographers must examine electron density for isolated atoms and differentiate sites containing structured solvent molecules from those containing elemental ions. This task requires specific knowledge of metal-binding chemistry and scattering properties and is prone to error. A method has previously been described to identify ions based on manually chosen criteria for a number of elements. Here, the use of support vector machines (SVMs) to automatically classify isolated atoms as either solvent or one of various ions is described. Two data sets of protein crystal structures, one containing manually curated structures deposited with anomalous diffraction data and another with automatically filtered, high-resolution structures, were constructed. On the manually curated data set, an SVM classifier was able to distinguish calcium from manganese, zinc, iron and nickel, as well as all five of these ions from water molecules, with a high degree of accuracy. Additionally, SVMs trained on the automatically curated set of high-resolution structures were able to successfully classify most common elemental ions in an independent validation test set. This method is readily extensible to other elemental ions and can also be used in conjunction with previous methods based on a priori expectations of the chemical environment and X-ray scattering.

  8. Empirical study of supervised gene screening

    Directory of Open Access Journals (Sweden)

    Ma Shuangge

    2006-12-01

    Full Text Available Abstract Background Microarray studies provide a way of linking variations of phenotypes with their genetic causations. Constructing predictive models using high dimensional microarray measurements usually consists of three steps: (1 unsupervised gene screening; (2 supervised gene screening; and (3 statistical model building. Supervised gene screening based on marginal gene ranking is commonly used to reduce the number of genes in the model building. Various simple statistics, such as t-statistic or signal to noise ratio, have been used to rank genes in the supervised screening. Despite of its extensive usage, statistical study of supervised gene screening remains scarce. Our study is partly motivated by the differences in gene discovery results caused by using different supervised gene screening methods. Results We investigate concordance and reproducibility of supervised gene screening based on eight commonly used marginal statistics. Concordance is assessed by the relative fractions of overlaps between top ranked genes screened using different marginal statistics. We propose a Bootstrap Reproducibility Index, which measures reproducibility of individual genes under the supervised screening. Empirical studies are based on four public microarray data. We consider the cases where the top 20%, 40% and 60% genes are screened. Conclusion From a gene discovery point of view, the effect of supervised gene screening based on different marginal statistics cannot be ignored. Empirical studies show that (1 genes passed different supervised screenings may be considerably different; (2 concordance may vary, depending on the underlying data structure and percentage of selected genes; (3 evaluated with the Bootstrap Reproducibility Index, genes passed supervised screenings are only moderately reproducible; and (4 concordance cannot be improved by supervised screening based on reproducibility.

  9. Intelligent Quality Prediction Using Weighted Least Square Support Vector Regression

    Science.gov (United States)

    Yu, Yaojun

    A novel quality prediction method with mobile time window is proposed for small-batch producing process based on weighted least squares support vector regression (LS-SVR). The design steps and learning algorithm are also addressed. In the method, weighted LS-SVR is taken as the intelligent kernel, with which the small-batch learning is solved well and the nearer sample is set a larger weight, while the farther is set the smaller weight in the history data. A typical machining process of cutting bearing outer race is carried out and the real measured data are used to contrast experiment. The experimental results demonstrate that the prediction accuracy of the weighted LS-SVR based model is only 20%-30% that of the standard LS-SVR based one in the same condition. It provides a better candidate for quality prediction of small-batch producing process.

  10. A Support Vector Machine-Based Gender Identification Using Speech Signal

    Science.gov (United States)

    Lee, Kye-Hwan; Kang, Sang-Ick; Kim, Deok-Hwan; Chang, Joon-Hyuk

    We propose an effective voice-based gender identification method using a support vector machine (SVM). The SVM is a binary classification algorithm that classifies two groups by finding the voluntary nonlinear boundary in a feature space and is known to yield high classification performance. In the present work, we compare the identification performance of the SVM with that of a Gaussian mixture model (GMM)-based method using the mel frequency cepstral coefficients (MFCC). A novel approach of incorporating a features fusion scheme based on a combination of the MFCC and the fundamental frequency is proposed with the aim of improving the performance of gender identification. Experimental results demonstrate that the gender identification performance using the SVM is significantly better than that of the GMM-based scheme. Moreover, the performance is substantially improved when the proposed features fusion technique is applied.

  11. 32 CFR 727.11 - Supervision.

    Science.gov (United States)

    2010-07-01

    ... 32 National Defense 5 2010-07-01 2010-07-01 false Supervision. 727.11 Section 727.11 National Defense Department of Defense (Continued) DEPARTMENT OF THE NAVY PERSONNEL LEGAL ASSISTANCE § 727.11 Supervision. The Judge Advocate General will exercise supervision over all legal assistance activities in the Department of the Navy. Subject to the...

  12. Prototype-based Models for the Supervised Learning of Classification Schemes

    Science.gov (United States)

    Biehl, Michael; Hammer, Barbara; Villmann, Thomas

    2017-06-01

    An introduction is given to the use of prototype-based models in supervised machine learning. The main concept of the framework is to represent previously observed data in terms of so-called prototypes, which reflect typical properties of the data. Together with a suitable, discriminative distance or dissimilarity measure, prototypes can be used for the classification of complex, possibly high-dimensional data. We illustrate the framework in terms of the popular Learning Vector Quantization (LVQ). Most frequently, standard Euclidean distance is employed as a distance measure. We discuss how LVQ can be equipped with more general dissimilarites. Moreover, we introduce relevance learning as a tool for the data-driven optimization of parameterized distances.

  13. Object Recognition System-on-Chip Using the Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Houzet Dominique

    2005-01-01

    Full Text Available The first aim of this work is to propose the design of a system-on-chip (SoC platform dedicated to digital image and signal processing, which is tuned to implement efficiently multiply-and-accumulate (MAC vector/matrix operations. The second aim of this work is to implement a recent promising neural network method, namely, the support vector machine (SVM used for real-time object recognition, in order to build a vision machine. With such a reconfigurable and programmable SoC platform, it is possible to implement any SVM function dedicated to any object recognition problem. The final aim is to obtain an automatic reconfiguration of the SoC platform, based on the results of the learning phase on an objects' database, which makes it possible to recognize practically any object without manual programming. Recognition can be of any kind that is from image to signal data. Such a system is a general-purpose automatic classifier. Many applications can be considered as a classification problem, but are usually treated specifically in order to optimize the cost of the implemented solution. The cost of our approach is more important than a dedicated one, but in a near future, hundreds of millions of gates will be common and affordable compared to the design cost. What we are proposing here is a general-purpose classification neural network implemented on a reconfigurable SoC platform. The first version presented here is limited in size and thus in object recognition performances, but can be easily upgraded according to technology improvements.

  14. Online Artifact Removal for Brain-Computer Interfaces Using Support Vector Machines and Blind Source Separation

    OpenAIRE

    Halder, Sebastian; Bensch, Michael; Mellinger, Jürgen; Bogdan, Martin; Kübler, Andrea; Birbaumer, Niels; Rosenstiel, Wolfgang

    2007-01-01

    We propose a combination of blind source separation (BSS) and independent component analysis (ICA) (signal decomposition into artifacts and nonartifacts) with support vector machines (SVMs) (automatic classification) that are designed for online usage. In order to select a suitable BSS/ICA method, three ICA algorithms (JADE, Infomax, and FastICA) and one BSS algorithm (AMUSE) are evaluated to determine their ability to isolate electromyographic (EMG) and electrooculographic...

  15. Analysis of programming properties and the row-column generation method for 1-norm support vector machines.

    Science.gov (United States)

    Zhang, Li; Zhou, WeiDa

    2013-12-01

    This paper deals with fast methods for training a 1-norm support vector machine (SVM). First, we define a specific class of linear programming with many sparse constraints, i.e., row-column sparse constraint linear programming (RCSC-LP). In nature, the 1-norm SVM is a sort of RCSC-LP. In order to construct subproblems for RCSC-LP and solve them, a family of row-column generation (RCG) methods is introduced. RCG methods belong to a category of decomposition techniques, and perform row and column generations in a parallel fashion. Specially, for the 1-norm SVM, the maximum size of subproblems of RCG is identical with the number of Support Vectors (SVs). We also introduce a semi-deleting rule for RCG methods and prove the convergence of RCG methods when using the semi-deleting rule. Experimental results on toy data and real-world datasets illustrate that it is efficient to use RCG to train the 1-norm SVM, especially in the case of small SVs. Copyright © 2013 Elsevier Ltd. All rights reserved.

  16. Particle swarm optimization-based support vector machine for forecasting dissolved gases content in power transformer oil

    Energy Technology Data Exchange (ETDEWEB)

    Fei, Sheng-wei; Wang, Ming-Jun; Miao, Yu-bin; Tu, Jun; Liu, Cheng-liang [School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240 (China)

    2009-06-15

    Forecasting of dissolved gases content in power transformer oil is a complicated problem due to its nonlinearity and the small quantity of training data. Support vector machine (SVM) has been successfully employed to solve regression problem of nonlinearity and small sample. However, the practicability of SVM is effected due to the difficulty of selecting appropriate SVM parameters. Particle swarm optimization (PSO) is a new optimization method, which is motivated by social behaviour of organisms such as bird flocking and fish schooling. The method not only has strong global search capability, but also is very easy to implement. Thus, the proposed PSO-SVM model is applied to forecast dissolved gases content in power transformer oil in this paper, among which PSO is used to determine free parameters of support vector machine. The experimental data from several electric power companies in China is used to illustrate the performance of proposed PSO-SVM model. The experimental results indicate that the PSO-SVM method can achieve greater forecasting accuracy than grey model, artificial neural network under the circumstances of small sample. (author)

  17. Particle swarm optimization-based support vector machine for forecasting dissolved gases content in power transformer oil

    Energy Technology Data Exchange (ETDEWEB)

    Fei Shengwei [School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240 (China)], E-mail: feishengwei@sohu.com; Wang Mingjun; Miao Yubin; Tu Jun; Liu Chengliang [School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240 (China)

    2009-06-15

    Forecasting of dissolved gases content in power transformer oil is a complicated problem due to its nonlinearity and the small quantity of training data. Support vector machine (SVM) has been successfully employed to solve regression problem of nonlinearity and small sample. However, the practicability of SVM is effected due to the difficulty of selecting appropriate SVM parameters. Particle swarm optimization (PSO) is a new optimization method, which is motivated by social behaviour of organisms such as bird flocking and fish schooling. The method not only has strong global search capability, but also is very easy to implement. Thus, the proposed PSO-SVM model is applied to forecast dissolved gases content in power transformer oil in this paper, among which PSO is used to determine free parameters of support vector machine. The experimental data from several electric power companies in China is used to illustrate the performance of proposed PSO-SVM model. The experimental results indicate that the PSO-SVM method can achieve greater forecasting accuracy than grey model, artificial neural network under the circumstances of small sample.

  18. Anticipatory Monitoring and Control of Complex Systems using a Fuzzy based Fusion of Support Vector Regressors

    Energy Technology Data Exchange (ETDEWEB)

    Miltiadis Alamaniotis; Vivek Agarwal

    2014-10-01

    This paper places itself in the realm of anticipatory systems and envisions monitoring and control methods being capable of making predictions over system critical parameters. Anticipatory systems allow intelligent control of complex systems by predicting their future state. In the current work, an intelligent model aimed at implementing anticipatory monitoring and control in energy industry is presented and tested. More particularly, a set of support vector regressors (SVRs) are trained using both historical and observed data. The trained SVRs are used to predict the future value of the system based on current operational system parameter. The predicted values are then inputted to a fuzzy logic based module where the values are fused to obtain a single value, i.e., final system output prediction. The methodology is tested on real turbine degradation datasets. The outcome of the approach presented in this paper highlights the superiority over single support vector regressors. In addition, it is shown that appropriate selection of fuzzy sets and fuzzy rules plays an important role in improving system performance.

  19. The vision in supervision: transference-countertransference dynamics and disclosure in the supervision relationship.

    Science.gov (United States)

    Coburn, W J

    1997-01-01

    The centrality of the supervision experience in the development of the supervisee's personal and professional capacities is addressed. The supervision relationship and process are explored in light of the potential effects of transference-countertransference configurations of supervisor and supervisee. Parallels between supervision and treatment are highlighted. The importance of developing and utilizing the capacity for reflectivity is reviewed, as is the impact of supervisee nondisclosure to supervisor. The direct use of countertransference experiences in the context of supervision is explored, and the centrality of self-disclosure is highlighted. It is recommended that supervisor and supervisee remain receptive to exploring these experiences in the service of developing a shared subjective sense of the patient, of increasing the supervisee's capacity to treat his or her patient, and of providing the supervisee with a novel, growth-enhancing relationship.

  20. Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron for Large Scale Classification of Protein Structures.

    Science.gov (United States)

    Arana-Daniel, Nancy; Gallegos, Alberto A; López-Franco, Carlos; Alanís, Alma Y; Morales, Jacob; López-Franco, Adriana

    2016-01-01

    With the increasing power of computers, the amount of data that can be processed in small periods of time has grown exponentially, as has the importance of classifying large-scale data efficiently. Support vector machines have shown good results classifying large amounts of high-dimensional data, such as data generated by protein structure prediction, spam recognition, medical diagnosis, optical character recognition and text classification, etc. Most state of the art approaches for large-scale learning use traditional optimization methods, such as quadratic programming or gradient descent, which makes the use of evolutionary algorithms for training support vector machines an area to be explored. The present paper proposes an approach that is simple to implement based on evolutionary algorithms and Kernel-Adatron for solving large-scale classification problems, focusing on protein structure prediction. The functional properties of proteins depend upon their three-dimensional structures. Knowing the structures of proteins is crucial for biology and can lead to improvements in areas such as medicine, agriculture and biofuels.

  1. Distributed collaborative probabilistic design for turbine blade-tip radial running clearance using support vector machine of regression

    Science.gov (United States)

    Fei, Cheng-Wei; Bai, Guang-Chen

    2014-12-01

    To improve the computational precision and efficiency of probabilistic design for mechanical dynamic assembly like the blade-tip radial running clearance (BTRRC) of gas turbine, a distribution collaborative probabilistic design method-based support vector machine of regression (SR)(called as DCSRM) is proposed by integrating distribution collaborative response surface method and support vector machine regression model. The mathematical model of DCSRM is established and the probabilistic design idea of DCSRM is introduced. The dynamic assembly probabilistic design of aeroengine high-pressure turbine (HPT) BTRRC is accomplished to verify the proposed DCSRM. The analysis results reveal that the optimal static blade-tip clearance of HPT is gained for designing BTRRC, and improving the performance and reliability of aeroengine. The comparison of methods shows that the DCSRM has high computational accuracy and high computational efficiency in BTRRC probabilistic analysis. The present research offers an effective way for the reliability design of mechanical dynamic assembly and enriches mechanical reliability theory and method.

  2. Performance and optimization of support vector machines in high-energy physics classification problems

    Energy Technology Data Exchange (ETDEWEB)

    Sahin, M.Oe.; Kruecker, D.; Melzer-Pellmann, I.A.

    2016-01-15

    In this paper we promote the use of Support Vector Machines (SVM) as a machine learning tool for searches in high-energy physics. As an example for a new-physics search we discuss the popular case of Supersymmetry at the Large Hadron Collider. We demonstrate that the SVM is a valuable tool and show that an automated discovery-significance based optimization of the SVM hyper-parameters is a highly efficient way to prepare an SVM for such applications. A new C++ LIBSVM interface called SVM-HINT is developed and available on Github.

  3. Performance and optimization of support vector machines in high-energy physics classification problems

    International Nuclear Information System (INIS)

    Sahin, M.Oe.; Kruecker, D.; Melzer-Pellmann, I.A.

    2016-01-01

    In this paper we promote the use of Support Vector Machines (SVM) as a machine learning tool for searches in high-energy physics. As an example for a new-physics search we discuss the popular case of Supersymmetry at the Large Hadron Collider. We demonstrate that the SVM is a valuable tool and show that an automated discovery-significance based optimization of the SVM hyper-parameters is a highly efficient way to prepare an SVM for such applications. A new C++ LIBSVM interface called SVM-HINT is developed and available on Github.

  4. A Subdivision-Based Representation for Vector Image Editing.

    Science.gov (United States)

    Liao, Zicheng; Hoppe, Hugues; Forsyth, David; Yu, Yizhou

    2012-11-01

    Vector graphics has been employed in a wide variety of applications due to its scalability and editability. Editability is a high priority for artists and designers who wish to produce vector-based graphical content with user interaction. In this paper, we introduce a new vector image representation based on piecewise smooth subdivision surfaces, which is a simple, unified and flexible framework that supports a variety of operations, including shape editing, color editing, image stylization, and vector image processing. These operations effectively create novel vector graphics by reusing and altering existing image vectorization results. Because image vectorization yields an abstraction of the original raster image, controlling the level of detail of this abstraction is highly desirable. To this end, we design a feature-oriented vector image pyramid that offers multiple levels of abstraction simultaneously. Our new vector image representation can be rasterized efficiently using GPU-accelerated subdivision. Experiments indicate that our vector image representation achieves high visual quality and better supports editing operations than existing representations.

  5. A qualitative investigation of the nature of "informal supervision" among therapists in training.

    Science.gov (United States)

    Coren, Sidney; Farber, Barry A

    2017-11-29

    This study investigated how, when, why, and with whom therapists in training utilize "informal supervision"-that is, engage individuals who are not their formally assigned supervisors in significant conversations about their clinical work. Participants were 16 doctoral trainees in clinical and counseling psychology programs. Semi-structured interviews were conducted and analyzed using the Consensual Qualitative Research (CQR) method. Seven domains emerged from the analysis, indicating that, in general, participants believe that informal and formal supervision offer many of the same benefits, including validation, support, and reassurance; freedom and safety to discuss doubts, anxieties, strong personal reactions to patients, clinical mistakes and challenges; and alternative approaches to clinical interventions. However, several differences also emerged between these modes of learning-for example, formal supervision is seen as more focused on didactics per se ("what to do"), whereas informal supervision is seen as providing more of a "holding environment." Overall, the findings of this study suggest that informal supervision is an important and valuable adjunctive practice by which clinical trainees augment their professional competencies. Recommendations are proposed for clinical practice and training, including the need to further specify the ethical boundaries of this unique and essentially unregulated type of supervision.

  6. Cognitive Development Optimization Algorithm Based Support Vector Machines for Determining Diabetes

    Directory of Open Access Journals (Sweden)

    Utku Kose

    2016-03-01

    Full Text Available The definition, diagnosis and classification of Diabetes Mellitus and its complications are very important. First of all, the World Health Organization (WHO and other societies, as well as scientists have done lots of studies regarding this subject. One of the most important research interests of this subject is the computer supported decision systems for diagnosing diabetes. In such systems, Artificial Intelligence techniques are often used for several disease diagnostics to streamline the diagnostic process in daily routine and avoid misdiagnosis. In this study, a diabetes diagnosis system, which is formed via both Support Vector Machines (SVM and Cognitive Development Optimization Algorithm (CoDOA has been proposed. Along the training of SVM, CoDOA was used for determining the sigma parameter of the Gauss (RBF kernel function, and eventually, a classification process was made over the diabetes data set, which is related to Pima Indians. The proposed approach offers an alternative solution to the field of Artificial Intelligence-based diabetes diagnosis, and contributes to the related literature on diagnosis processes.

  7. Financial Distress Prediction using Linear Discriminant Analysis and Support Vector Machine

    Science.gov (United States)

    Santoso, Noviyanti; Wibowo, Wahyu

    2018-03-01

    A financial difficulty is the early stages before the bankruptcy. Bankruptcies caused by the financial distress can be seen from the financial statements of the company. The ability to predict financial distress became an important research topic because it can provide early warning for the company. In addition, predicting financial distress is also beneficial for investors and creditors. This research will be made the prediction model of financial distress at industrial companies in Indonesia by comparing the performance of Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) combined with variable selection technique. The result of this research is prediction model based on hybrid Stepwise-SVM obtains better balance among fitting ability, generalization ability and model stability than the other models.

  8. Facial Expression Recognition using Multiclass Ensemble Least-Square Support Vector Machine

    Science.gov (United States)

    Lawi, Armin; Sya'Rani Machrizzandi, M.

    2018-03-01

    Facial expression is one of behavior characteristics of human-being. The use of biometrics technology system with facial expression characteristics makes it possible to recognize a person’s mood or emotion. The basic components of facial expression analysis system are face detection, face image extraction, facial classification and facial expressions recognition. This paper uses Principal Component Analysis (PCA) algorithm to extract facial features with expression parameters, i.e., happy, sad, neutral, angry, fear, and disgusted. Then Multiclass Ensemble Least-Squares Support Vector Machine (MELS-SVM) is used for the classification process of facial expression. The result of MELS-SVM model obtained from our 185 different expression images of 10 persons showed high accuracy level of 99.998% using RBF kernel.

  9. Data on Support Vector Machines (SVM model to forecast photovoltaic power

    Directory of Open Access Journals (Sweden)

    M. Malvoni

    2016-12-01

    Full Text Available The data concern the photovoltaic (PV power, forecasted by a hybrid model that considers weather variations and applies a technique to reduce the input data size, as presented in the paper entitled “Photovoltaic forecast based on hybrid pca-lssvm using dimensionality reducted data” (M. Malvoni, M.G. De Giorgi, P.M. Congedo, 2015 [1]. The quadratic Renyi entropy criteria together with the principal component analysis (PCA are applied to the Least Squares Support Vector Machines (LS-SVM to predict the PV power in the day-ahead time frame. The data here shared represent the proposed approach results. Hourly PV power predictions for 1,3,6,12, 24 ahead hours and for different data reduction sizes are provided in Supplementary material.

  10. Supervision som undervisningsform i voksenspecialundervisningen

    DEFF Research Database (Denmark)

    Kristensen, René

    2000-01-01

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

  11. Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach

    International Nuclear Information System (INIS)

    Chen, Kuilin; Yu, Jie

    2014-01-01

    Highlights: • A novel hybrid modeling method is proposed for short-term wind speed forecasting. • Support vector regression model is constructed to formulate nonlinear state-space framework. • Unscented Kalman filter is adopted to recursively update states under random uncertainty. • The new SVR–UKF approach is compared to several conventional methods for short-term wind speed prediction. • The proposed method demonstrates higher prediction accuracy and reliability. - Abstract: Accurate wind speed forecasting is becoming increasingly important to improve and optimize renewable wind power generation. Particularly, reliable short-term wind speed prediction can enable model predictive control of wind turbines and real-time optimization of wind farm operation. However, this task remains challenging due to the strong stochastic nature and dynamic uncertainty of wind speed. In this study, unscented Kalman filter (UKF) is integrated with support vector regression (SVR) based state-space model in order to precisely update the short-term estimation of wind speed sequence. In the proposed SVR–UKF approach, support vector regression is first employed to formulate a nonlinear state-space model and then unscented Kalman filter is adopted to perform dynamic state estimation recursively on wind sequence with stochastic uncertainty. The novel SVR–UKF method is compared with artificial neural networks (ANNs), SVR, autoregressive (AR) and autoregressive integrated with Kalman filter (AR-Kalman) approaches for predicting short-term wind speed sequences collected from three sites in Massachusetts, USA. The forecasting results indicate that the proposed method has much better performance in both one-step-ahead and multi-step-ahead wind speed predictions than the other approaches across all the locations

  12. Multi combined Adlerian supervision in Counseling

    OpenAIRE

    Gungor, Abdi

    2017-01-01

    For counselor professional and counselor education, supervision is an important process, in which more experienced professional helps and guides less experienced professional. To provide an effective and beneficial supervision, various therapy, development, or process based approaches and models have been developed. In addition, different eclectic models integrating more than one model have been developed. In this paper, as a supervision model, multi combined Adlerian supervision model is pro...

  13. A nonlinear support vector machine model with hard penalty function based on glowworm swarm optimization for forecasting daily global solar radiation

    International Nuclear Information System (INIS)

    Jiang, He; Dong, Yao

    2016-01-01

    Highlights: • Eclat data mining algorithm is used to determine the possible predictors. • Support vector machine is converted into a ridge regularization problem. • Hard penalty selects the number of radial basis functions to simply the structure. • Glowworm swarm optimization is utilized to determine the optimal parameters. - Abstract: For a portion of the power which is generated by grid connected photovoltaic installations, an effective solar irradiation forecasting approach must be crucial to ensure the quality and the security of power grid. This paper develops and investigates a novel model to forecast 30 daily global solar radiation at four given locations of the United States. Eclat data mining algorithm is first presented to discover association rules between solar radiation and several meteorological factors laying a theoretical foundation for these correlative factors as input vectors. An effective and innovative intelligent optimization model based on nonlinear support vector machine and hard penalty function is proposed to forecast solar radiation by converting support vector machine into a regularization problem with ridge penalty, adding a hard penalty function to select the number of radial basis functions, and using glowworm swarm optimization algorithm to determine the optimal parameters of the model. In order to illustrate our validity of the proposed method, the datasets at four sites of the United States are split to into training data and test data, separately. The experiment results reveal that the proposed model delivers the best forecasting performances comparing with other competitors.

  14. Nursing supervision for care comprehensiveness

    Directory of Open Access Journals (Sweden)

    Lucieli Dias Pedreschi Chaves

    Full Text Available ABSTRACT Objective: To reflect on nursing supervision as a management tool for care comprehensiveness by nurses, considering its potential and limits in the current scenario. Method: A reflective study based on discourse about nursing supervision, presenting theoretical and practical concepts and approaches. Results: Limits on the exercise of supervision are related to the organization of healthcare services based on the functional and clinical model of care, in addition to possible gaps in the nurse training process and work overload. Regarding the potential, researchers emphasize that supervision is a tool for coordinating care and management actions, which may favor care comprehensiveness, and stimulate positive attitudes toward cooperation and contribution within teams, co-responsibility, and educational development at work. Final considerations: Nursing supervision may help enhance care comprehensiveness by implying continuous reflection on including the dynamics of the healthcare work process and user needs in care networks.

  15. Learning Dynamics in Doctoral Supervision

    DEFF Research Database (Denmark)

    Kobayashi, Sofie

    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...... of different theoretical frameworks from the perspectives of learning as individual acquisition and a sociocultural perspective on learning contributed to a nuanced illustration of the otherwise implicit practices of supervision....

  16. Supervision and group dynamics

    DEFF Research Database (Denmark)

    Hansen, Søren; Jensen, Lars Peter

    2004-01-01

     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...... 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...

  17. Automatic Detection of Retinal Exudates using a Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Nualsawat HIRANSAKOLWONG

    2013-02-01

    Full Text Available Retinal exudates are among the preliminary signs of diabetic retinopathy, a major cause of vision loss in diabetic patients. Correct and efficient screening of exudates is very expensive in professional time and may cause human error. Nowadays, the digital retinal image is frequently used to follow-up and diagnoses eye diseases. Therefore, the retinal image is crucial and essential for experts to detect exudates. Unfortunately, it is a normal situation that retinal images in Thailand are poor quality images. In this paper, we present a series of experiments on feature selection and exudates classification using the support vector machine classifiers. The retinal images are segmented following key preprocessing steps, i.e., color normalization, contrast enhancement, noise removal and color space selection. On data sets of poor quality images, sensitivity, specificity and accuracy is 94.46%, 89.52% and 92.14%, respectively.

  18. Prediction of Hydrocarbon Reservoirs Permeability Using Support Vector Machine

    Directory of Open Access Journals (Sweden)

    R. Gholami

    2012-01-01

    Full Text Available Permeability is a key parameter associated with the characterization of any hydrocarbon reservoir. In fact, it is not possible to have accurate solutions to many petroleum engineering problems without having accurate permeability value. The conventional methods for permeability determination are core analysis and well test techniques. These methods are very expensive and time consuming. Therefore, attempts have usually been carried out to use artificial neural network for identification of the relationship between the well log data and core permeability. In this way, recent works on artificial intelligence techniques have led to introduce a robust machine learning methodology called support vector machine. This paper aims to utilize the SVM for predicting the permeability of three gas wells in the Southern Pars field. Obtained results of SVM showed that the correlation coefficient between core and predicted permeability is 0.97 for testing dataset. Comparing the result of SVM with that of a general regression neural network (GRNN revealed that the SVM approach is faster and more accurate than the GRNN in prediction of hydrocarbon reservoirs permeability.

  19. Clinical Supervision in Denmark

    DEFF Research Database (Denmark)

    Jacobsen, Claus Haugaard

    2011-01-01

    Core Questionnaire (DPCCQ) has only few questions on supervision. To rectify this limitation, a recent Danish version of the DPCCQ included two new sections on supervision, one focusing on supervisees and another on supervisors and their supervisory training. This paper presents our initial findings...

  20. Networks of Professional Supervision

    Science.gov (United States)

    Annan, Jean; Ryba, Ken

    2013-01-01

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

  1. A framework for multiple kernel support vector regression and its applications to siRNA efficacy prediction.

    Science.gov (United States)

    Qiu, Shibin; Lane, Terran

    2009-01-01

    The cell defense mechanism of RNA interference has applications in gene function analysis and promising potentials in human disease therapy. To effectively silence a target gene, it is desirable to select appropriate initiator siRNA molecules having satisfactory silencing capabilities. Computational prediction for silencing efficacy of siRNAs can assist this screening process before using them in biological experiments. String kernel functions, which operate directly on the string objects representing siRNAs and target mRNAs, have been applied to support vector regression for the prediction and improved accuracy over numerical kernels in multidimensional vector spaces constructed from descriptors of siRNA design rules. To fully utilize information provided by string and numerical data, we propose to unify the two in a kernel feature space by devising a multiple kernel regression framework where a linear combination of the kernels is used. We formulate the multiple kernel learning into a quadratically constrained quadratic programming (QCQP) problem, which although yields global optimal solution, is computationally demanding and requires a commercial solver package. We further propose three heuristics based on the principle of kernel-target alignment and predictive accuracy. Empirical results demonstrate that multiple kernel regression can improve accuracy, decrease model complexity by reducing the number of support vectors, and speed up computational performance dramatically. In addition, multiple kernel regression evaluates the importance of constituent kernels, which for the siRNA efficacy prediction problem, compares the relative significance of the design rules. Finally, we give insights into the multiple kernel regression mechanism and point out possible extensions.

  2. Supervised Convolutional Sparse Coding

    KAUST Repository

    Affara, Lama Ahmed

    2018-04-08

    Convolutional Sparse Coding (CSC) is a well-established image representation model especially suited for image restoration tasks. In this work, we extend the applicability of this model by proposing a supervised approach to convolutional sparse coding, which aims at learning discriminative dictionaries instead of purely reconstructive ones. We incorporate a supervised regularization term into the traditional unsupervised CSC objective to encourage the final dictionary elements to be discriminative. Experimental results show that using supervised convolutional learning results in two key advantages. First, we learn more semantically relevant filters in the dictionary and second, we achieve improved image reconstruction on unseen data.

  3. A Conjunction Method of Wavelet Transform-Particle Swarm Optimization-Support Vector Machine for Streamflow Forecasting

    Directory of Open Access Journals (Sweden)

    Fanping Zhang

    2014-01-01

    Full Text Available Streamflow forecasting has an important role in water resource management and reservoir operation. Support vector machine (SVM is an appropriate and suitable method for streamflow prediction due to its best versatility, robustness, and effectiveness. In this study, a wavelet transform particle swarm optimization support vector machine (WT-PSO-SVM model is proposed and applied for streamflow time series prediction. Firstly, the streamflow time series were decomposed into various details (Ds and an approximation (A3 at three resolution levels (21-22-23 using Daubechies (db3 discrete wavelet. Correlation coefficients between each D subtime series and original monthly streamflow time series are calculated. Ds components with high correlation coefficients (D3 are added to the approximation (A3 as the input values of the SVM model. Secondly, the PSO is employed to select the optimal parameters, C, ε, and σ, of the SVM model. Finally, the WT-PSO-SVM models are trained and tested by the monthly streamflow time series of Tangnaihai Station located in Yellow River upper stream from January 1956 to December 2008. The test results indicate that the WT-PSO-SVM approach provide a superior alternative to the single SVM model for forecasting monthly streamflow in situations without formulating models for internal structure of the watershed.

  4. SemiBoost: boosting for semi-supervised learning.

    Science.gov (United States)

    Mallapragada, Pavan Kumar; Jin, Rong; Jain, Anil K; Liu, Yi

    2009-11-01

    Semi-supervised learning has attracted a significant amount of attention in pattern recognition and machine learning. Most previous studies have focused on designing special algorithms to effectively exploit the unlabeled data in conjunction with labeled data. Our goal is to improve the classification accuracy of any given supervised learning algorithm by using the available unlabeled examples. We call this as the Semi-supervised improvement problem, to distinguish the proposed approach from the existing approaches. We design a metasemi-supervised learning algorithm that wraps around the underlying supervised algorithm and improves its performance using unlabeled data. This problem is particularly important when we need to train a supervised learning algorithm with a limited number of labeled examples and a multitude of unlabeled examples. We present a boosting framework for semi-supervised learning, termed as SemiBoost. The key advantages of the proposed semi-supervised learning approach are: 1) performance improvement of any supervised learning algorithm with a multitude of unlabeled data, 2) efficient computation by the iterative boosting algorithm, and 3) exploiting both manifold and cluster assumption in training classification models. An empirical study on 16 different data sets and text categorization demonstrates that the proposed framework improves the performance of several commonly used supervised learning algorithms, given a large number of unlabeled examples. We also show that the performance of the proposed algorithm, SemiBoost, is comparable to the state-of-the-art semi-supervised learning algorithms.

  5. Experimental Investigation for Fault Diagnosis Based on a Hybrid Approach Using Wavelet Packet and Support Vector Classification

    Directory of Open Access Journals (Sweden)

    Pengfei Li

    2014-01-01

    Full Text Available To deal with the difficulty to obtain a large number of fault samples under the practical condition for mechanical fault diagnosis, a hybrid method that combined wavelet packet decomposition and support vector classification (SVC is proposed. The wavelet packet is employed to decompose the vibration signal to obtain the energy ratio in each frequency band. Taking energy ratios as feature vectors, the pattern recognition results are obtained by the SVC. The rolling bearing and gear fault diagnostic results of the typical experimental platform show that the present approach is robust to noise and has higher classification accuracy and, thus, provides a better way to diagnose mechanical faults under the condition of small fault samples.

  6. Coupled dimensionality reduction and classification for supervised and semi-supervised multilabel learning.

    Science.gov (United States)

    Gönen, Mehmet

    2014-03-01

    Coupled training of dimensionality reduction and classification is proposed previously to improve the prediction performance for single-label problems. Following this line of research, in this paper, we first introduce a novel Bayesian method that combines linear dimensionality reduction with linear binary classification for supervised multilabel learning and present a deterministic variational approximation algorithm to learn the proposed probabilistic model. We then extend the proposed method to find intrinsic dimensionality of the projected subspace using automatic relevance determination and to handle semi-supervised learning using a low-density assumption. We perform supervised learning experiments on four benchmark multilabel learning data sets by comparing our method with baseline linear dimensionality reduction algorithms. These experiments show that the proposed approach achieves good performance values in terms of hamming loss, average AUC, macro F 1 , and micro F 1 on held-out test data. The low-dimensional embeddings obtained by our method are also very useful for exploratory data analysis. We also show the effectiveness of our approach in finding intrinsic subspace dimensionality and semi-supervised learning tasks.

  7. Support vector machine based diagnostic system for breast cancer using swarm intelligence.

    Science.gov (United States)

    Chen, Hui-Ling; Yang, Bo; Wang, Gang; Wang, Su-Jing; Liu, Jie; Liu, Da-You

    2012-08-01

    Breast cancer is becoming a leading cause of death among women in the whole world, meanwhile, it is confirmed that the early detection and accurate diagnosis of this disease can ensure a long survival of the patients. In this paper, a swarm intelligence technique based support vector machine classifier (PSO_SVM) is proposed for breast cancer diagnosis. In the proposed PSO-SVM, the issue of model selection and feature selection in SVM is simultaneously solved under particle swarm (PSO optimization) framework. A weighted function is adopted to design the objective function of PSO, which takes into account the average accuracy rates of SVM (ACC), the number of support vectors (SVs) and the selected features simultaneously. Furthermore, time varying acceleration coefficients (TVAC) and inertia weight (TVIW) are employed to efficiently control the local and global search in PSO algorithm. The effectiveness of PSO-SVM has been rigorously evaluated against the Wisconsin Breast Cancer Dataset (WBCD), which is commonly used among researchers who use machine learning methods for breast cancer diagnosis. The proposed system is compared with the grid search method with feature selection by F-score. The experimental results demonstrate that the proposed approach not only obtains much more appropriate model parameters and discriminative feature subset, but also needs smaller set of SVs for training, giving high predictive accuracy. In addition, Compared to the existing methods in previous studies, the proposed system can also be regarded as a promising success with the excellent classification accuracy of 99.3% via 10-fold cross validation (CV) analysis. Moreover, a combination of five informative features is identified, which might provide important insights to the nature of the breast cancer disease and give an important clue for the physicians to take a closer attention. We believe the promising result can ensure that the physicians make very accurate diagnostic decision in

  8. Clinical supervision of nurses working with patients with borderline personality disorder.

    Science.gov (United States)

    Bland, Ann R; Rossen, Eileen K

    2005-06-01

    Some nurses describe individuals diagnosed with borderline personality disorder (BPD) as among the most challenging and difficult patients encountered in their practice. As a result, the argument has been made for nursing staff to receive clinical supervision to enhance therapeutic effectiveness and treatment outcomes for individuals with BPD. Formal clinical supervision can focus on the stresses of working in a demanding environment within the work place and enable nurses to accept accountability for their own practice and development (Pesut & Herman, 1999). A psychiatric-mental health clinical nurse specialist can provide individual and/or group supervision for the nursing staff, including education about patient dynamics, staff responses, and treatment team decisions. A clinical nurse specialist also can provide emotional support to nursing staff, which enhances job satisfaction, as they struggle to maintain professional therapeutic behavior with these individuals.

  9. Support vector machines and evolutionary algorithms for classification single or together?

    CERN Document Server

    Stoean, Catalin

    2014-01-01

    When discussing classification, support vector machines are known to be a capable and efficient technique to learn and predict with high accuracy within a quick time frame. Yet, their black box means to do so make the practical users quite circumspect about relying on it, without much understanding of the how and why of its predictions. The question raised in this book is how can this ‘masked hero’ be made more comprehensible and friendly to the public: provide a surrogate model for its hidden optimization engine, replace the method completely or appoint a more friendly approach to tag along and offer the much desired explanations? Evolutionary algorithms can do all these and this book presents such possibilities of achieving high accuracy, comprehensibility, reasonable runtime as well as unconstrained performance.

  10. Educational Supervision Appropriate for Psychiatry Trainee's Needs

    Science.gov (United States)

    Rele, Kiran; Tarrant, C. Jane

    2010-01-01

    Objective: The authors studied the regularity and content of supervision sessions in one of the U.K. postgraduate psychiatric training schemes (Mid-Trent). Methods: A questionnaire sent to psychiatry trainees assessed the timing and duration of supervision, content and protection of supervision time, and overall quality of supervision. The authors…

  11. Emotions, Social Work Practice and Supervision: An Uneasy Alliance?

    Science.gov (United States)

    Ingram, Richard

    2013-03-01

    This paper examines the place of emotions within social work practice. The perceived tensions between emotions and rational decision making are explored and it is argued that their relationship is compatible and necessary. A model for the co-creation of emotionally intelligent supervision is developed to support this vision of practice.

  12. Tværfaglig supervision

    DEFF Research Database (Denmark)

    Tværfaglig supervision dækker over supervision af forskellige faggrupper. Det er en kompleks disciplin der stiller store krav tl supervisor. Bogens første del præsenterer fire faglige supervisionsmodeller: En almen, en psykodynamisk, en kognitiv adfærdsterapeutisk og en narrativ. Anden del...

  13. Supervision for superheroes: the case for reflective professional supervision for senior doctors.

    Science.gov (United States)

    Austin, Helen

    2016-05-06

    The practice of medicine is inherently stressful with regular exposure to trauma and the distress of others. There is a culture in medicine that doctors should not be affected by such things, although it is well recognised that doctors have higher rates of depression, anxiety, suicide, and substance abuse than the general public. Reflective professional supervision is a forum where the complexities of the interpersonal interactions that underpin the provision of healthcare can be explored in a supportive and confidential setting. It is argued that this is a process that should continue for the duration of a doctor's career, with potential benefits including enhanced job satisfaction and resilience, better workplace communication and improved interpersonal skills.

  14. Methods of Feminist Family Therapy Supervision.

    Science.gov (United States)

    Prouty, Anne M.; Thomas, Volker; Johnson, Scott; Long, Janie K.

    2001-01-01

    Presents three supervision methods which emerged from a qualitative study of the experiences of feminist family therapy supervisors and the therapists they supervised: the supervision contract, collaborative methods, and hierarchical methods. Provides a description of the participants' experiences of these methods and discusses their fit with…

  15. Reservoir Inflow Prediction under GCM Scenario Downscaled by Wavelet Transform and Support Vector Machine Hybrid Models

    Directory of Open Access Journals (Sweden)

    Gusfan Halik

    2015-01-01

    Full Text Available Climate change has significant impacts on changing precipitation patterns causing the variation of the reservoir inflow. Nowadays, Indonesian hydrologist performs reservoir inflow prediction according to the technical guideline of Pd-T-25-2004-A. This technical guideline does not consider the climate variables directly, resulting in significant deviation to the observation results. This research intends to predict the reservoir inflow using the statistical downscaling (SD of General Circulation Model (GCM outputs. The GCM outputs are obtained from the National Center for Environmental Prediction/National Center for Atmospheric Research Reanalysis (NCEP/NCAR Reanalysis. A new proposed hybrid SD model named Wavelet Support Vector Machine (WSVM was utilized. It is a combination of the Multiscale Principal Components Analysis (MSPCA and nonlinear Support Vector Machine regression. The model was validated at Sutami Reservoir, Indonesia. Training and testing were carried out using data of 1991–2008 and 2008–2012, respectively. The results showed that MSPCA produced better extracting data than PCA. The WSVM generated better reservoir inflow prediction than the one of technical guideline. Moreover, this research also applied WSVM for future reservoir inflow prediction based on GCM ECHAM5 and scenario SRES A1B.

  16. Detection of sensor degradation using K-means clustering and support vector regression in nuclear power plant

    International Nuclear Information System (INIS)

    Seo, Inyong; Ha, Bokam; Lee, Sungwoo; Shin, Changhoon; Lee, Jaeyong; Kim, Seongjun

    2011-01-01

    In a nuclear power plant (NPP), periodic sensor calibrations are required to assure sensors are operating correctly. However, only a few faulty sensors are found to be rectified. For the safe operation of an NPP and the reduction of unnecessary calibration, on-line calibration monitoring is needed. In this study, an on-line calibration monitoring called KPCSVR using k-means clustering and principal component based Auto-Associative support vector regression (PCSVR) is proposed for nuclear power plant. To reduce the training time of the model, k-means clustering method was used. Response surface methodology is employed to efficiently determine the optimal values of support vector regression hyperparameters. The proposed KPCSVR model was confirmed with actual plant data of Kori Nuclear Power Plant Unit 3 which were measured from the primary and secondary systems of the plant, and compared with the PCSVR model. By using data clustering, the average accuracy of PCSVR improved from 1.228×10 -4 to 0.472×10 -4 and the average sensitivity of PCSVR from 0.0930 to 0.0909, which results in good detection of sensor drift. Moreover, the training time is greatly reduced from 123.5 to 31.5 sec. (author)

  17. Using support vector machines to identify literacy skills: Evidence from eye movements.

    Science.gov (United States)

    Lou, Ya; Liu, Yanping; Kaakinen, Johanna K; Li, Xingshan

    2017-06-01

    Is inferring readers' literacy skills possible by analyzing their eye movements during text reading? This study used Support Vector Machines (SVM) to analyze eye movement data from 61 undergraduate students who read a multiple-paragraph, multiple-topic expository text. Forward fixation time, first-pass rereading time, second-pass fixation time, and regression path reading time on different regions of the text were provided as features. The SVM classification algorithm assisted in distinguishing high-literacy-skilled readers from low-literacy-skilled readers with 80.3 % accuracy. Results demonstrate the effectiveness of combining eye tracking and machine learning techniques to detect readers with low literacy skills, and suggest that such approaches can be potentially used in predicting other cognitive abilities.

  18. Explaining Support Vector Machines: A Color Based Nomogram.

    Directory of Open Access Journals (Sweden)

    Vanya Van Belle

    Full Text Available Support vector machines (SVMs are very popular tools for classification, regression and other problems. Due to the large choice of kernels they can be applied with, a large variety of data can be analysed using these tools. Machine learning thanks its popularity to the good performance of the resulting models. However, interpreting the models is far from obvious, especially when non-linear kernels are used. Hence, the methods are used as black boxes. As a consequence, the use of SVMs is less supported in areas where interpretability is important and where people are held responsible for the decisions made by models.In this work, we investigate whether SVMs using linear, polynomial and RBF kernels can be explained such that interpretations for model-based decisions can be provided. We further indicate when SVMs can be explained and in which situations interpretation of SVMs is (hitherto not possible. Here, explainability is defined as the ability to produce the final decision based on a sum of contributions which depend on one single or at most two input variables.Our experiments on simulated and real-life data show that explainability of an SVM depends on the chosen parameter values (degree of polynomial kernel, width of RBF kernel and regularization constant. When several combinations of parameter values yield the same cross-validation performance, combinations with a lower polynomial degree or a larger kernel width have a higher chance of being explainable.This work summarizes SVM classifiers obtained with linear, polynomial and RBF kernels in a single plot. Linear and polynomial kernels up to the second degree are represented exactly. For other kernels an indication of the reliability of the approximation is presented. The complete methodology is available as an R package and two apps and a movie are provided to illustrate the possibilities offered by the method.

  19. Hybrid genetic algorithm tuned support vector machine regression for wave transmission prediction of horizontally interlaced multilayer moored floating pipe breakwater

    Digital Repository Service at National Institute of Oceanography (India)

    Patil, S.G.; Mandal, S.; Hegde, A.V.; Muruganandam, A.

    Support Vector Machine (SVM) works on structural risk minimization principle that has greater generalization ability and is superior to the empirical risk minimization principle as adopted in conventional neural network models. However...

  20. 28 CFR 2.91 - Supervision responsibility.

    Science.gov (United States)

    2010-07-01

    ... 28 Judicial Administration 1 2010-07-01 2010-07-01 false Supervision responsibility. 2.91 Section 2.91 Judicial Administration DEPARTMENT OF JUSTICE PAROLE, RELEASE, SUPERVISION AND RECOMMITMENT OF PRISONERS, YOUTH OFFENDERS, AND JUVENILE DELINQUENTS District of Columbia Code: Prisoners and Parolees § 2.91 Supervision responsibility. (a) Pursuan...

  1. Optimistic semi-supervised least squares classification

    DEFF Research Database (Denmark)

    Krijthe, Jesse H.; Loog, Marco

    2017-01-01

    The goal of semi-supervised learning is to improve supervised classifiers by using additional unlabeled training examples. In this work we study a simple self-learning approach to semi-supervised learning applied to the least squares classifier. We show that a soft-label and a hard-label variant ...

  2. Design, development and evaluation of an online grading system for peeled pistachios equipped with machine vision technology and support vector machine

    Directory of Open Access Journals (Sweden)

    Hosein Nouri-Ahmadabadi

    2017-12-01

    Full Text Available In this study, an intelligent system based on combined machine vision (MV and Support Vector Machine (SVM was developed for sorting of peeled pistachio kernels and shells. The system was composed of conveyor belt, lighting box, camera, processing unit and sorting unit. A color CCD camera was used to capture images. The images were digitalized by a capture card and transferred to a personal computer for further analysis. Initially, images were converted from RGB color space to HSV color ones. For segmentation of the acquired images, H-component in the HSV color space and Otsu thresholding method were applied. A feature vector containing 30 color features was extracted from the captured images. A feature selection method based on sensitivity analysis was carried out to select superior features. The selected features were presented to SVM classifier. Various SVM models having a different kernel function were developed and tested. The SVM model having cubic polynomial kernel function and 38 support vectors achieved the best accuracy (99.17% and then was selected to use in online decision-making unit of the system. By launching the online system, it was found that limiting factors of the system capacity were related to the hardware parts of the system (conveyor belt and pneumatic valves used in the sorting unit. The limiting factors led to a distance of 8 mm between the samples. The overall accuracy and capacity of the sorter were obtained 94.33% and 22.74 kg/h, respectively. Keywords: Pistachio kernel, Sorting, Machine vision, Sensitivity analysis, Support vector machine

  3. The Perceived Benefits and Difficulties in Introducing and Maintaining Supervision Groups in a SEMH Special School

    Science.gov (United States)

    Willis, Jonathan; Baines, Ed

    2018-01-01

    Supervision groups are often used in professional settings and are introduced to address and provide support in relation to the challenges that arise in everyday practice. Although group supervision is common amongst a range of helping professions, its use in schools is rare. Little research exists as to the merits and challenges of providing…

  4. Support Vector Data Description Model to Map Specific Land Cover with Optimal Parameters Determined from a Window-Based Validation Set

    Directory of Open Access Journals (Sweden)

    Jinshui Zhang

    2017-04-01

    Full Text Available This paper developed an approach, the window-based validation set for support vector data description (WVS-SVDD, to determine optimal parameters for support vector data description (SVDD model to map specific land cover by integrating training and window-based validation sets. Compared to the conventional approach where the validation set included target and outlier pixels selected visually and randomly, the validation set derived from WVS-SVDD constructed a tightened hypersphere because of the compact constraint by the outlier pixels which were located neighboring to the target class in the spectral feature space. The overall accuracies for wheat and bare land achieved were as high as 89.25% and 83.65%, respectively. However, target class was underestimated because the validation set covers only a small fraction of the heterogeneous spectra of the target class. The different window sizes were then tested to acquire more wheat pixels for validation set. The results showed that classification accuracy increased with the increasing window size and the overall accuracies were higher than 88% at all window size scales. Moreover, WVS-SVDD showed much less sensitivity to the untrained classes than the multi-class support vector machine (SVM method. Therefore, the developed method showed its merits using the optimal parameters, tradeoff coefficient (C and kernel width (s, in mapping homogeneous specific land cover.

  5. Estimating Frequency by Interpolation Using Least Squares Support Vector Regression

    Directory of Open Access Journals (Sweden)

    Changwei Ma

    2015-01-01

    Full Text Available Discrete Fourier transform- (DFT- based maximum likelihood (ML algorithm is an important part of single sinusoid frequency estimation. As signal to noise ratio (SNR increases and is above the threshold value, it will lie very close to Cramer-Rao lower bound (CRLB, which is dependent on the number of DFT points. However, its mean square error (MSE performance is directly proportional to its calculation cost. As a modified version of support vector regression (SVR, least squares SVR (LS-SVR can not only still keep excellent capabilities for generalizing and fitting but also exhibit lower computational complexity. In this paper, therefore, LS-SVR is employed to interpolate on Fourier coefficients of received signals and attain high frequency estimation accuracy. Our results show that the proposed algorithm can make a good compromise between calculation cost and MSE performance under the assumption that the sample size, number of DFT points, and resampling points are already known.

  6. Application of support vector machine for classification of multispectral data

    International Nuclear Information System (INIS)

    Bahari, Nurul Iman Saiful; Ahmad, Asmala; Aboobaider, Burhanuddin Mohd

    2014-01-01

    In this paper, support vector machine (SVM) is used to classify satellite remotely sensed multispectral data. The data are recorded from a Landsat-5 TM satellite with resolution of 30x30m. SVM finds the optimal separating hyperplane between classes by focusing on the training cases. The study area of Klang Valley has more than 10 land covers and classification using SVM has been done successfully without any pixel being unclassified. The training area is determined carefully by visual interpretation and with the aid of the reference map of the study area. The result obtained is then analysed for the accuracy and visual performance. Accuracy assessment is done by determination and discussion of Kappa coefficient value, overall and producer accuracy for each class (in pixels and percentage). While, visual analysis is done by comparing the classification data with the reference map. Overall the study shows that SVM is able to classify the land covers within the study area with a high accuracy

  7. Using support vector machines in the multivariate state estimation technique

    International Nuclear Information System (INIS)

    Zavaljevski, N.; Gross, K.C.

    1999-01-01

    One approach to validate nuclear power plant (NPP) signals makes use of pattern recognition techniques. This approach often assumes that there is a set of signal prototypes that are continuously compared with the actual sensor signals. These signal prototypes are often computed based on empirical models with little or no knowledge about physical processes. A common problem of all data-based models is their limited ability to make predictions on the basis of available training data. Another problem is related to suboptimal training algorithms. Both of these potential shortcomings with conventional approaches to signal validation and sensor operability validation are successfully resolved by adopting a recently proposed learning paradigm called the support vector machine (SVM). The work presented here is a novel application of SVM for data-based modeling of system state variables in an NPP, integrated with a nonlinear, nonparametric technique called the multivariate state estimation technique (MSET), an algorithm developed at Argonne National Laboratory for a wide range of nuclear plant applications

  8. Support Vector Data Descriptions and k-Means Clustering: One Class?

    Science.gov (United States)

    Gornitz, Nico; Lima, Luiz Alberto; Muller, Klaus-Robert; Kloft, Marius; Nakajima, Shinichi

    2017-09-27

    We present ClusterSVDD, a methodology that unifies support vector data descriptions (SVDDs) and k-means clustering into a single formulation. This allows both methods to benefit from one another, i.e., by adding flexibility using multiple spheres for SVDDs and increasing anomaly resistance and flexibility through kernels to k-means. In particular, our approach leads to a new interpretation of k-means as a regularized mode seeking algorithm. The unifying formulation further allows for deriving new algorithms by transferring knowledge from one-class learning settings to clustering settings and vice versa. As a showcase, we derive a clustering method for structured data based on a one-class learning scenario. Additionally, our formulation can be solved via a particularly simple optimization scheme. We evaluate our approach empirically to highlight some of the proposed benefits on artificially generated data, as well as on real-world problems, and provide a Python software package comprising various implementations of primal and dual SVDD as well as our proposed ClusterSVDD.

  9. Support-vector-based emergent self-organising approach for emotional understanding

    Science.gov (United States)

    Nguwi, Yok-Yen; Cho, Siu-Yeung

    2010-12-01

    This study discusses the computational analysis of general emotion understanding from questionnaires methodology. The questionnaires method approaches the subject by investigating the real experience that accompanied the emotions, whereas the other laboratory approaches are generally associated with exaggerated elements. We adopted a connectionist model called support-vector-based emergent self-organising map (SVESOM) to analyse the emotion profiling from the questionnaires method. The SVESOM first identifies the important variables by giving discriminative features with high ranking. The classifier then performs the classification based on the selected features. Experimental results show that the top rank features are in line with the work of Scherer and Wallbott [(1994), 'Evidence for Universality and Cultural Variation of Differential Emotion Response Patterning', Journal of Personality and Social Psychology, 66, 310-328], which approached the emotions physiologically. While the performance measures show that using the full features for classifications can degrade the performance, the selected features provide superior results in terms of accuracy and generalisation.

  10. Electricity Load Forecasting Using Support Vector Regression with Memetic Algorithms

    Directory of Open Access Journals (Sweden)

    Zhongyi Hu

    2013-01-01

    Full Text Available Electricity load forecasting is an important issue that is widely explored and examined in power systems operation literature and commercial transactions in electricity markets literature as well. Among the existing forecasting models, support vector regression (SVR has gained much attention. Considering the performance of SVR highly depends on its parameters; this study proposed a firefly algorithm (FA based memetic algorithm (FA-MA to appropriately determine the parameters of SVR forecasting model. In the proposed FA-MA algorithm, the FA algorithm is applied to explore the solution space, and the pattern search is used to conduct individual learning and thus enhance the exploitation of FA. Experimental results confirm that the proposed FA-MA based SVR model can not only yield more accurate forecasting results than the other four evolutionary algorithms based SVR models and three well-known forecasting models but also outperform the hybrid algorithms in the related existing literature.

  11. 20 CFR 655.30 - Supervised recruitment.

    Science.gov (United States)

    2010-04-01

    ... 20 Employees' Benefits 3 2010-04-01 2010-04-01 false Supervised recruitment. 655.30 Section 655.30... Workers) § 655.30 Supervised recruitment. (a) Supervised recruitment. Where an employer is found to have... failed to adequately conduct recruitment activities or failed in any obligation of this part, the CO may...

  12. Emotions, Social Work Practice and Supervision: An Uneasy Alliance?

    Science.gov (United States)

    Ingram, Richard

    2012-01-01

    This paper examines the place of emotions within social work practice. The perceived tensions between emotions and rational decision making are explored and it is argued that their relationship is compatible and necessary. A model for the co-creation of emotionally intelligent supervision is developed to support this vision of practice. PMID:24764612

  13. Oceans apart, yet connected: Findings from a qualitative study on professional supervision in rural and remote allied health services.

    Science.gov (United States)

    Ducat, Wendy; Martin, Priya; Kumar, Saravana; Burge, Vanessa; Abernathy, LuJuana

    2016-02-01

    Improving the quality and safety of health care in Australia is imperative to ensure the right treatment is delivered to the right person at the right time. Achieving this requires appropriate clinical governance and support for health professionals, including professional supervision. This study investigates the usefulness and effectiveness of and barriers to supervision in rural and remote Queensland. As part of the evaluation of the Allied Health Rural and Remote Training and Support program, a qualitative descriptive study was conducted involving semi-structured interviews with 42 rural or remote allied health professionals, nine operational managers and four supervisors. The interviews explored perspectives on their supervision arrangements, including the perceived usefulness, effect on practice and barriers. Themes of reduced isolation; enhanced professional enthusiasm, growth and commitment to the organisation; enhanced clinical skills, knowledge and confidence; and enhanced patient safety were identified as perceived outcomes of professional supervision. Time, technology and organisational factors were identified as potential facilitators as well as potential barriers to effective supervision. This research provides current evidence on the impact of professional supervision in rural and remote Queensland. A multidimensional model of organisational factors associated with effective supervision in rural and remote settings is proposed identifying positive supervision culture and a good supervisor-supervisee fit as key factors associated with effective arrangements. © 2015 Commonwealth of Australia. Australian Journal of Rural Health published by Wiley Publishing Asia Pty Ltd. on behalf of National Rural Health Alliance Inc.

  14. A Model for Art Therapy-Based Supervision for End-of-Life Care Workers in Hong Kong.

    Science.gov (United States)

    Potash, Jordan S; Chan, Faye; Ho, Andy H Y; Wang, Xiao Lu; Cheng, Carol

    2015-01-01

    End-of-life care workers and volunteers are particularly prone to burnout given the intense emotional and existential nature of their work. Supervision is one important way to provide adequate support that focuses on both professional and personal competencies. The inclusion of art therapy principles and practices within supervision further creates a dynamic platform for sustained self-reflection. A 6-week art therapy-based supervision group provided opportunities for developing emotional awareness, recognizing professional strengths, securing collegial relationships, and reflecting on death-related memories. The structure, rationale, and feedback are discussed.

  15. Man-machine supervision

    International Nuclear Information System (INIS)

    Montmain, J.

    2005-01-01

    Today's complexity of systems where man is involved has led to the development of more and more sophisticated information processing systems where decision making has become more and more difficult. The operator task has moved from operation to supervision and the production tool has become indissociable from its numerical instrumentation and control system. The integration of more and more numerous and sophisticated control indicators in the control room does not necessary fulfill the expectations of the operation team. It is preferable to develop cooperative information systems which are real situation understanding aids. The stake is not the automation of operators' cognitive tasks but the supply of a reasoning help. One of the challenges of interactive information systems is the selection, organisation and dynamical display of information. The efficiency of the whole man-machine system depends on the communication interface efficiency. This article presents the principles and specificities of man-machine supervision systems: 1 - principle: operator's role in control room, operator and automation, monitoring and diagnosis, characteristics of useful models for supervision; 2 - qualitative reasoning: origin, trends, evolutions; 3 - causal reasoning: causality, causal graph representation, causal and diagnostic graph; 4 - multi-points of view reasoning: multi flow modeling method, Sagace method; 5 - approximate reasoning: the symbolic numerical interface, the multi-criteria decision; 6 - example of application: supervision in a spent-fuel reprocessing facility. (J.S.)

  16. Daily Peak Load Forecasting Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Shuyu Dai

    2018-01-01

    Full Text Available Daily peak load forecasting is an important part of power load forecasting. The accuracy of its prediction has great influence on the formulation of power generation plan, power grid dispatching, power grid operation and power supply reliability of power system. Therefore, it is of great significance to construct a suitable model to realize the accurate prediction of the daily peak load. A novel daily peak load forecasting model, CEEMDAN-MGWO-SVM (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm, is proposed in this paper. Firstly, the model uses the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN algorithm to decompose the daily peak load sequence into multiple sub sequences. Then, the model of modified grey wolf optimization and support vector machine (MGWO-SVM is adopted to forecast the sub sequences. Finally, the forecasting sequence is reconstructed and the forecasting result is obtained. Using CEEMDAN can realize noise reduction for non-stationary daily peak load sequence, which makes the daily peak load sequence more regular. The model adopts the grey wolf optimization algorithm improved by introducing the population dynamic evolution operator and the nonlinear convergence factor to enhance the global search ability and avoid falling into the local optimum, which can better optimize the parameters of the SVM algorithm for improving the forecasting accuracy of daily peak load. In this paper, three cases are used to test the forecasting accuracy of the CEEMDAN-MGWO-SVM model. We choose the models EEMD-MGWO-SVM (Ensemble Empirical Mode Decomposition and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm, MGWO-SVM (Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm, GWO-SVM (Support Vector Machine Optimized by Grey Wolf Optimization Algorithm, SVM (Support Vector

  17. Evolution in banking supervision

    OpenAIRE

    Edward J. Stevens

    2000-01-01

    Banking supervision must keep pace with technical innovations in the banking industry. The international Basel Committee on Banking Supervision currently is reviewing public comments on its proposed new method for judging whether a bank maintains enough capital to absorb unexpected losses. This Economic Commentary explains how existing standards became obsolete and describes the new plan.

  18. A Shellcode Detection Method Based on Full Native API Sequence and Support Vector Machine

    Science.gov (United States)

    Cheng, Yixuan; Fan, Wenqing; Huang, Wei; An, Jing

    2017-09-01

    Dynamic monitoring the behavior of a program is widely used to discriminate between benign program and malware. It is usually based on the dynamic characteristics of a program, such as API call sequence or API call frequency to judge. The key innovation of this paper is to consider the full Native API sequence and use the support vector machine to detect the shellcode. We also use the Markov chain to extract and digitize Native API sequence features. Our experimental results show that the method proposed in this paper has high accuracy and low detection rate.

  19. Projected estimators for robust semi-supervised classification

    DEFF Research Database (Denmark)

    Krijthe, Jesse H.; Loog, Marco

    2017-01-01

    For semi-supervised techniques to be applied safely in practice we at least want methods to outperform their supervised counterparts. We study this question for classification using the well-known quadratic surrogate loss function. Unlike other approaches to semi-supervised learning, the procedure...... specifically, we prove that, measured on the labeled and unlabeled training data, this semi-supervised procedure never gives a lower quadratic loss than the supervised alternative. To our knowledge this is the first approach that offers such strong, albeit conservative, guarantees for improvement over...... the supervised solution. The characteristics of our approach are explicated using benchmark datasets to further understand the similarities and differences between the quadratic loss criterion used in the theoretical results and the classification accuracy typically considered in practice....

  20. Differentiation of Glioblastoma and Lymphoma Using Feature Extraction and Support Vector Machine.

    Science.gov (United States)

    Yang, Zhangjing; Feng, Piaopiao; Wen, Tian; Wan, Minghua; Hong, Xunning

    2017-01-01

    Differentiation of glioblastoma multiformes (GBMs) and lymphomas using multi-sequence magnetic resonance imaging (MRI) is an important task that is valuable for treatment planning. However, this task is a challenge because GBMs and lymphomas may have a similar appearance in MRI images. This similarity may lead to misclassification and could affect the treatment results. In this paper, we propose a semi-automatic method based on multi-sequence MRI to differentiate these two types of brain tumors. Our method consists of three steps: 1) the key slice is selected from 3D MRIs and region of interests (ROIs) are drawn around the tumor region; 2) different features are extracted based on prior clinical knowledge and validated using a t-test; and 3) features that are helpful for classification are used to build an original feature vector and a support vector machine is applied to perform classification. In total, 58 GBM cases and 37 lymphoma cases are used to validate our method. A leave-one-out crossvalidation strategy is adopted in our experiments. The global accuracy of our method was determined as 96.84%, which indicates that our method is effective for the differentiation of GBM and lymphoma and can be applied in clinical diagnosis. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.