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-xix
  2. Shigeo Abe
    Pages 1-19
  3. Shigeo Abe
    Pages 21-112
  4. Shigeo Abe
    Pages 113-161
  5. Shigeo Abe
    Pages 163-226
  6. Shigeo Abe
    Pages 227-303
  7. Shigeo Abe
    Pages 305-329
  8. Shigeo Abe
    Pages 331-341
  9. Shigeo Abe
    Pages 343-352
  10. Shigeo Abe
    Pages 367-394
  11. Shigeo Abe
    Pages 395-442
  12. Back Matter
    Pages 443-471

About this book

Introduction

Originally formulated for two-class classification problems, support vector machines (SVMs) are now accepted as powerful tools for developing pattern classification and function approximation systems. Recent developments in kernel-based methods include kernel classifiers and regressors and their variants, advancements in generalization theory, and various feature selection and extraction methods.

Providing a unique perspective on the state of the art in SVMs, with a particular focus on classification, this thoroughly updated new edition includes a more rigorous performance comparison of classifiers and regressors. In addition to presenting various useful architectures for multiclass classification and function approximation problems, the book now also investigates evaluation criteria for classifiers and regressors.

Topics and Features:

  • Clarifies the characteristics of two-class SVMs through extensive analysis
  • Discusses kernel methods for improving the generalization ability of conventional neural networks and fuzzy systems
  • Contains ample illustrations, examples and computer experiments to help readers understand the concepts and their usefulness
  • Includes performance evaluation using publicly available two-class data sets, microarray sets, multiclass data sets, and regression data sets (NEW)
  • Examines Mahalanobis kernels, empirical feature space, and the effect of model selection by cross-validation (NEW)
  • Covers sparse SVMs, an approach to learning using privileged information, semi-supervised learning, multiple classifier systems, and multiple kernel learning (NEW)
  • Explores incremental training based batch training and active-set training methods, together with decomposition techniques for linear programming SVMs (NEW)
  • Provides a discussion on variable selection for support vector regressors (NEW)

An essential guide on the use of SVMs in pattern classification, this comprehensive resource will be of interest to researchers and postgraduate students, as well as professional developers.

Dr. Shigeo Abe is a Professor at Kobe University, Graduate School of Engineering. He is the author of the Springer titles Neural Networks and Fuzzy Systems and Pattern Classification: Neuro-fuzzy Methods and Their Comparison.

Keywords

Fuzzy Systems Kernel Methods Neural Networks Pattern Classification Support Vector Machine Support Vector Machines classification

Authors and affiliations

  • Shigeo¬†Abe
    • 1
  1. 1.Dept. Electrical & Electronics, EngineeringKobe UniversityKobeJapan

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-84996-098-4
  • Copyright Information Springer-Verlag London 2010
  • Publisher Name Springer, London
  • eBook Packages Computer Science
  • Print ISBN 978-1-84996-097-7
  • Online ISBN 978-1-84996-098-4
  • Series Print ISSN 2191-6586
  • About this book