Download or read book Pattern Classification written by Shigeo Abe and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a unified approach for developing a fuzzy classifier and explains the advantages and disadvantages of different classifiers through extensive performance evaluation of real data sets. It thus offers new learning paradigms for analyzing neural networks and fuzzy systems, while training fuzzy classifiers. Function approximation is also treated and function approximators are compared.

Download or read book Support Vector Machines for Pattern Classification written by Shigeo Abe and published by Springer Science & Business Media. This book was released on 2005-07-29 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: Support vector machines (SVMs), were originally formulated for two-class classification problems, and have been accepted as a powerful tool for developing pattern classification and function approximations systems. This book provides a unique perspective of the state of the art in SVMs by taking the only approach that focuses on classification rather than covering the theoretical aspects. The book clarifies the characteristics of two-class SVMs through their extensive analysis, presents various useful architectures for multiclass classification and function approximation problems, and discusses kernel methods for improving generalization ability of conventional neural networks and fuzzy systems. Ample illustrations, examples and computer experiments are included to help readers understand the new ideas and their usefulness. This book supplies a comprehensive resource for the use of SVMs in pattern classification and will be invaluable reading for researchers, developers & students in academia and industry.

Download or read book Pattern Recognition with Support Vector Machines written by Seong-Whan Lee and published by Springer. This book was released on 2003-08-02 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines, SVM 2002, held in Niagara Falls, Canada in August 2002.The 16 revised full papers and 14 poster papers presented together with two invited contributions were carefully reviewed and selected from 57 full paper submissions. The papers presented span the whole range of topics in pattern recognition with support vector machines from computational theories to implementations and applications.

Download or read book Twin Support Vector Machines written by Jayadeva and published by Springer. This book was released on 2016-10-12 with total page 221 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Learning to Classify Text Using Support Vector Machines written by Thorsten Joachims and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: Based on ideas from Support Vector Machines (SVMs), Learning To Classify Text Using Support Vector Machines presents a new approach to generating text classifiers from examples. The approach combines high performance and efficiency with theoretical understanding and improved robustness. In particular, it is highly effective without greedy heuristic components. The SVM approach is computationally efficient in training and classification, and it comes with a learning theory that can guide real-world applications. Learning To Classify Text Using Support Vector Machines gives a complete and detailed description of the SVM approach to learning text classifiers, including training algorithms, transductive text classification, efficient performance estimation, and a statistical learning model of text classification. In addition, it includes an overview of the field of text classification, making it self-contained even for newcomers to the field. This book gives a concise introduction to SVMs for pattern recognition, and it includes a detailed description of how to formulate text-classification tasks for machine learning.

Download or read book Mathematical Programming and Game Theory for Decision Making written by S. K. Neogy and published by World Scientific. This book was released on 2008 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited book presents recent developments and state-of-the-art review in various areas of mathematical programming and game theory. It is a peer-reviewed research monograph under the ISI Platinum Jubilee Series on Statistical Science and Interdisciplinary Research. This volume provides a panoramic view of theory and the applications of the methods of mathematical programming to problems in statistics, finance, games and electrical networks. It also provides an important as well as timely overview of research trends and focuses on the exciting areas like support vector machines, bilevel programming, interior point method for convex quadratic programming, cooperative games, non-cooperative games and stochastic games. Researchers, professionals and advanced graduates will find the book an essential resource for current work in mathematical programming, game theory and their applications. Sample Chapter(s). Foreword (45 KB). Chapter 1: Mathematical Programming and its Applications in Finance (177 KB). Contents: Mathematical Programming and Its Applications in Finance (L C Thomas); Anti-Stalling Pivot Rule for Linear Programs with Totally Unimodular Coefficient Matrix (S N Kabadi & A P Punnen); A New Practically Efficient Interior Point Method for Convex Quadratic Programming (K G Murty); A General Framework for the Analysis of Sets of Constraints (R Caron & T Traynor), Tolerance-Based Algorithms for the Traveling Salesman Problem (D Ghosh et al.); On the Membership Problem of the Pedigree Polytope (T S Arthanari); Exact Algorithms for a One-Defective Vertex Colouring Problem (N Achuthan et al.); Complementarity Problem Involving a Vertical Block Matrix and Its Solution Using Neural Network Model (S K Neogy et al.); Fuzzy Twin Support Vector Machines for Pattern Classification (R Khemchandani et al.); An Overview of the Minimum Sum of Absolute Errors Regression (S C Narula & J F Wellington); Hedging Against the Market with No Short Selling (S A Clark & C Srinivasan); Mathematical Programming and Electrical Network Analysis II: Computational Linear Algebra Through Network Analysis (H Narayanan); Dynamic Optimal Control Policy in Price and Quality for High Technology Product (A K Bardhan & U Chanda); Forecasting for Supply Chain and Portfolio Management (K G Murty); Variational Analysis in Bilevel Programming (S Dempe et al.); Game Engineering (R J Aumann); Games of Connectivity (P Dubey & R Garg); A Robust Feedback Nash Equilibrium in a Climate Change Policy Game (M Hennlock); De Facto Delegation and Proposer Rules (H Imai & K Yonezaki); The Bargaining Set in Effectivity Function (D Razafimahatolotra); Dynamic Oligopoly as a Mixed Large Game OCo Toy Market (A Wiszniewska-Matyszkiel); On Some Classes of Balanced Games (R B Bapat); Market Equilibrium for Combinatorial Auctions and the Matching Core of Nonnegative TU Games (S Lahiri); Continuity, Manifolds, and Arrow''s Social Choice Problem (K Saukkonen); On a Mixture Class of Stochastic Games with Ordered Field Property (S K Neogy). Readership: Researchers, professionals and advanced students in mathematical programming, game theory, management sciences and computational mathematics.

Download or read book Support Vector Machines Applications written by Yunqian Ma and published by Springer Science & Business Media. This book was released on 2014-02-12 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: Support vector machines (SVM) have both a solid mathematical background and practical applications. This book focuses on the recent advances and applications of the SVM, such as image processing, medical practice, computer vision, and pattern recognition, machine learning, applied statistics, and artificial intelligence. The aim of this book is to create a comprehensive source on support vector machine applications.

Download or read book Support Vector Machines and Perceptrons written by M.N. Murty and published by Springer. This book was released on 2016-08-16 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work reviews the state of the art in SVM and perceptron classifiers. A Support Vector Machine (SVM) is easily the most popular tool for dealing with a variety of machine-learning tasks, including classification. SVMs are associated with maximizing the margin between two classes. The concerned optimization problem is a convex optimization guaranteeing a globally optimal solution. The weight vector associated with SVM is obtained by a linear combination of some of the boundary and noisy vectors. Further, when the data are not linearly separable, tuning the coefficient of the regularization term becomes crucial. Even though SVMs have popularized the kernel trick, in most of the practical applications that are high-dimensional, linear SVMs are popularly used. The text examines applications to social and information networks. The work also discusses another popular linear classifier, the perceptron, and compares its performance with that of the SVM in different application areas.>

Download or read book An Introduction to Support Vector Machines and Other Kernel based Learning Methods written by Nello Cristianini and published by Cambridge University Press. This book was released on 2000-03-23 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a comprehensive introduction to Support Vector Machines, a generation learning system based on advances in statistical learning theory.

Download or read book Support Vector Machines for Pattern Classification written by Shigeo Abe and published by Springer Science & Business Media. This book was released on 2010-07-23 with total page 486 pages. Available in PDF, EPUB and Kindle. Book excerpt: A guide on the use of SVMs in pattern classification, including a rigorous performance comparison of classifiers and regressors. The book presents architectures for multiclass classification and function approximation problems, as well as evaluation criteria for classifiers and regressors. Features: Clarifies the characteristics of two-class SVMs; Discusses kernel methods for improving the generalization ability of neural networks and fuzzy systems; Contains ample illustrations and examples; Includes performance evaluation using publicly available data sets; Examines Mahalanobis kernels, empirical feature space, and the effect of model selection by cross-validation; Covers sparse SVMs, learning using privileged information, semi-supervised learning, multiple classifier systems, and multiple kernel learning; Explores incremental training based batch training and active-set training methods, and decomposition techniques for linear programming SVMs; Discusses variable selection for support vector regressors.

Download or read book Object Based Image Analysis written by Thomas Blaschke and published by Springer Science & Business Media. This book was released on 2008-08-09 with total page 804 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together a collection of invited interdisciplinary persp- tives on the recent topic of Object-based Image Analysis (OBIA). Its c- st tent is based on select papers from the 1 OBIA International Conference held in Salzburg in July 2006, and is enriched by several invited chapters. All submissions have passed through a blind peer-review process resulting in what we believe is a timely volume of the highest scientific, theoretical and technical standards. The concept of OBIA first gained widespread interest within the GIScience (Geographic Information Science) community circa 2000, with the advent of the first commercial software for what was then termed ‘obje- oriented image analysis’. However, it is widely agreed that OBIA builds on older segmentation, edge-detection and classification concepts that have been used in remote sensing image analysis for several decades. Nevert- less, its emergence has provided a new critical bridge to spatial concepts applied in multiscale landscape analysis, Geographic Information Systems (GIS) and the synergy between image-objects and their radiometric char- teristics and analyses in Earth Observation data (EO).

Download or read book Pattern Recognition and Image Analysis written by Francisco José Perales and published by Springer Science & Business Media. This book was released on 2003-05-22 with total page 1194 pages. Available in PDF, EPUB and Kindle. Book excerpt: The refereed proceedings of the First Iberial Conference on Pattern Recognition and Image Analysis, IbPria 2003, held in Puerto de Andratx, Mallorca, Spain in June 2003. The 130 revised papers presented were carefully reviewed and selected from 185 full papers submitted. All current aspects of ongoing research in computer vision, image processing, pattern recognition, and speech recognition are addressed.

Download or read book Knowledge Discovery with Support Vector Machines written by Lutz H. Hamel and published by John Wiley & Sons. This book was released on 2011-09-20 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: An easy-to-follow introduction to support vector machines This book provides an in-depth, easy-to-follow introduction to support vector machines drawing only from minimal, carefully motivated technical and mathematical background material. It begins with a cohesive discussion of machine learning and goes on to cover: Knowledge discovery environments Describing data mathematically Linear decision surfaces and functions Perceptron learning Maximum margin classifiers Support vector machines Elements of statistical learning theory Multi-class classification Regression with support vector machines Novelty detection Complemented with hands-on exercises, algorithm descriptions, and data sets, Knowledge Discovery with Support Vector Machines is an invaluable textbook for advanced undergraduate and graduate courses. It is also an excellent tutorial on support vector machines for professionals who are pursuing research in machine learning and related areas.

Download or read book Imbalanced Learning written by Haibo He and published by John Wiley & Sons. This book was released on 2013-06-07 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first book of its kind to review the current status and future direction of the exciting new branch of machine learning/data mining called imbalanced learning Imbalanced learning focuses on how an intelligent system can learn when it is provided with imbalanced data. Solving imbalanced learning problems is critical in numerous data-intensive networked systems, including surveillance, security, Internet, finance, biomedical, defense, and more. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently into information and knowledge representation. The first comprehensive look at this new branch of machine learning, this book offers a critical review of the problem of imbalanced learning, covering the state of the art in techniques, principles, and real-world applications. Featuring contributions from experts in both academia and industry, Imbalanced Learning: Foundations, Algorithms, and Applications provides chapter coverage on: Foundations of Imbalanced Learning Imbalanced Datasets: From Sampling to Classifiers Ensemble Methods for Class Imbalance Learning Class Imbalance Learning Methods for Support Vector Machines Class Imbalance and Active Learning Nonstationary Stream Data Learning with Imbalanced Class Distribution Assessment Metrics for Imbalanced Learning Imbalanced Learning: Foundations, Algorithms, and Applications will help scientists and engineers learn how to tackle the problem of learning from imbalanced datasets, and gain insight into current developments in the field as well as future research directions.

Download or read book Support Vector Machines Theory and Applications written by Lipo Wang and published by Springer Science & Business Media. This book was released on 2005-06-21 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: The support vector machine (SVM) has become one of the standard tools for machine learning and data mining. This carefully edited volume presents the state of the art of the mathematical foundation of SVM in statistical learning theory, as well as novel algorithms and applications. Support Vector Machines provides a selection of numerous real-world applications, such as bioinformatics, text categorization, pattern recognition, and object detection, written by leading experts in their respective fields.

Download or read book Support Vector Machines written by Ingo Steinwart and published by Springer Science & Business Media. This book was released on 2008-09-15 with total page 611 pages. Available in PDF, EPUB and Kindle. Book excerpt: Every mathematical discipline goes through three periods of development: the naive, the formal, and the critical. David Hilbert The goal of this book is to explain the principles that made support vector machines (SVMs) a successful modeling and prediction tool for a variety of applications. We try to achieve this by presenting the basic ideas of SVMs together with the latest developments and current research questions in a uni?ed style. In a nutshell, we identify at least three reasons for the success of SVMs: their ability to learn well with only a very small number of free parameters, their robustness against several types of model violations and outliers, and last but not least their computational e?ciency compared with several other methods. Although there are several roots and precursors of SVMs, these methods gained particular momentum during the last 15 years since Vapnik (1995, 1998) published his well-known textbooks on statistical learning theory with aspecialemphasisonsupportvectormachines. Sincethen,the?eldofmachine learninghaswitnessedintenseactivityinthestudyofSVMs,whichhasspread moreandmoretootherdisciplinessuchasstatisticsandmathematics. Thusit seems fair to say that several communities are currently working on support vector machines and on related kernel-based methods. Although there are many interactions between these communities, we think that there is still roomforadditionalfruitfulinteractionandwouldbegladifthistextbookwere found helpful in stimulating further research. Many of the results presented in this book have previously been scattered in the journal literature or are still under review. As a consequence, these results have been accessible only to a relativelysmallnumberofspecialists,sometimesprobablyonlytopeoplefrom one community but not the others.

Download or read book Support Vector Machines written by Naiyang Deng and published by CRC Press. This book was released on 2012-12-17 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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