An Introduction to Support Vector Machines and Other Kernel-based Learning Methods

by Nello Cristianini, John Shawe-Taylor

Overview

This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software make it an ideal starting point for further study.

Product Details

ISBN-13:	9780521780193
Publisher:	Cambridge University Press
Publication date:	03/23/2000
Edition description:	New Edition
Pages:	204
Product dimensions:	6.89(w) x 9.80(h) x 0.59(d)

Table of Contents

Preface; 1. The learning methodology; 2. Linear learning machines; 3. Kernel-induced feature spaces; 4. Generalisation theory; 5. Optimisation theory; 6. Support vector machines; 7. Implementation techniques; 8. Applications of support vector machines; Appendix A: pseudocode for the SMO algorithm; Appendix B: background mathematics; Appendix C: glossary; Appendix D: notation; Bibliography; Index.

Editorial Reviews

"This book is an excellent introduction to this area... it is nicely organized, self-contained, and well written. The book is most suitable for the beginning graduate student in computer science." Richard A Chechile, Journal of Mathematical Psychology

From the Publisher