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Support Vector Machines for Pattern Classification

  • Book
  • © 2005

Overview

Authors:
  1. Shigeo Abe

    1. Kobe University, Kobe, Japan

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Part of the book series: Advances in Computer Vision and Pattern Recognition (ACVPR)

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Table of contents (11 chapters)

  1. Front Matter

    Pages I-1
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  2. Introduction

    Pages 3-13

  3. Two-Class Support Vector Machines

    Pages 15-82

  4. Multiclass Support Vector Machines

    Pages 83-128

  5. Variants of Support Vector Machines

    Pages 129-154

  6. Training Methods

    Pages 155-188

  7. Feature Selection and Extraction

    Pages 189-199

  8. Clustering

    Pages 201-208

  9. Kernel-Based Methods

    Pages 209-222

  10. Maximum-Margin Multilayer Neural Networks

    Pages 223-235

  11. Maximum-Margin Fuzzy Classifiers

    Pages 237-263

  12. Function Approximation

    Pages 265-296

  13. Back Matter

    Pages 297-343
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Keywords

About this book

I was shocked to see a student’s report on performance comparisons between support vector machines (SVMs) and fuzzy classi?ers that we had developed withourbestendeavors.Classi?cationperformanceofourfuzzyclassi?erswas comparable, but in most cases inferior, to that of support vector machines. This tendency was especially evident when the numbers of class data were small. I shifted my research e?orts from developing fuzzy classi?ers with high generalization ability to developing support vector machine–based classi?ers. This book focuses on the application of support vector machines to p- tern classi?cation. Speci?cally, we discuss the properties of support vector machines that are useful for pattern classi?cation applications, several m- ticlass models, and variants of support vector machines. To clarify their - plicability to real-world problems, we compare performance of most models discussed in the book using real-world benchmark data. Readers interested in the theoretical aspect of support vector machines should refer to books such as [109, 215, 256, 257].

Reviews

From the reviews:

"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)

Authors and Affiliations

  • Kobe University, Kobe, Japan

    Shigeo Abe

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