Support Vector Machines for Pattern Classification

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Former Library book. Shows some signs of wear, and may have some markings on the inside. 100% Money Back Guarantee. Shipped to over one million happy customers. Your purchase ... benefits world literacy! Read more Show Less

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Overview

"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. Support Vector Machines for Pattern Classification 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 of Support Vector Machines." "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." Support Vector Machines for Pattern Classification supplies a comprehensive resource for the use of SVMs in pattern classification and will be invaluable reading for researchers, developers and students in academia and industry.
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Editorial Reviews

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"This broad and deep … book is organized around the highly significant concept of pattern recognition by support vector machines (SVMs). … The book is praxis and application oriented but with strong theoretical backing and support. Many … details are presented and discussed, thereby making the SVM both an easy-to-understand learning machine and a more likable data modeling (mining) tool. Shigeo Abe has produced the book that will become the standard … . I like it and therefore highly recommend this book … ." (Vojislav Kecman, SIAM Review, Vol. 48 (2), 2006)

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Product Details

Table of Contents

1 Introduction 3
2 Two-class support vector machines 15
3 Multiclass support vector machines 83
4 Variants of support vector machines 129
5 Training methods 155
6 Feature selection and extraction 189
7 Clustering 201
8 Kernel-based methods 209
9 Maximum-margin multilayer neural networks 223
10 Maximum-margin fuzzy classifiers 237
11 Function approximation 265
A Conventional classifiers 297
B Matrices 301
C Quadratic programming 309
D Positive semidefinite kernels and reproducing kernel Hilbert space 313
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