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

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


By (author) Nello Christianini, By (author) John Shawe-Taylor, By (author) Nello Cristianini

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  • Format: Hardback | 204 pages
  • Dimensions: 174mm x 248mm x 16mm | 558g
  • Publication date: 28 March 2000
  • Publication City/Country: Cambridge
  • ISBN 10: 0521780195
  • ISBN 13: 9780521780193
  • Illustrations note: 12 b/w illus. 5 colour illus. 25 exercises
  • Sales rank: 454,431

Product description

This is the first comprehensive introduction to Support Vector Machines (SVMs), a generation learning system based on recent advances in statistical learning theory. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc., and are now established as one of the standard tools for machine learning and data mining. 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 ensure that it forms an ideal starting point for further study. Equally, the book and its associated web site will guide practitioners to updated literature, new applications, and on-line software.

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Review quote

'... the most accessible introduction to the area I have yet seen'. D. J. Hand, Publication of the International Statistical Institute 'The book is an admirable presentation of this powerful new approach to pattern classification.' Alex M. Andrew, Robotica ' ... an excellent book, complete and readable without big requirements in mathematical functional analysis.' Zentralblatt fur Mathematik und ihre Grenzgebiete Mathematics Abstracts

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.