Support Vector Machines for Pattern Classification

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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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].

• Fuzzy Systems
• Kernel Methods
• Pattern Classification
• Support Vector Machines
• classification
• fuzzy
• fuzzy system
• networks
• neural networks
• support vector machine

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)

• Kobe University, Kobe, Japan

  Shigeo Abe

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