Support Vector Machines for Pattern Classification

  • Shigeo Abe

Part of the Advances in Pattern Recognition book series (ACVPR)

Table of contents

  1. Front Matter
    Pages I-1
  2. Pages 3-13
  3. Pages 155-188
  4. Pages 201-208
  5. Pages 209-222
  6. Pages 265-296
  7. Back Matter
    Pages 297-343

About this book

Introduction

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].

Keywords

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

Authors and affiliations

  • Shigeo Abe
    • 1
  1. 1.Kobe UniversityKobeJapan

Bibliographic information

  • DOI https://doi.org/10.1007/1-84628-219-5
  • Copyright Information Springer-Verlag London Limited 2005
  • Publisher Name Springer, London
  • eBook Packages Computer Science
  • Print ISBN 978-1-85233-929-6
  • Online ISBN 978-1-84628-219-5
  • About this book