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

This is a comprehensive introduction to Support Vector Machines, a generation learning system based on advances in statistical learning theory.

Nello Cristianini (Author), John Shawe-Taylor (Author)

9780521780193, Cambridge University Press

Hardback, published 23 March 2000

204 pages, 12 b/w illus. 5 colour illus. 25 exercises
24.9 x 17.5 x 1.5 cm, 0.5 kg

' … an excellent book, complete and readable without big requirements in mathematical functional analysis.' Zentralblatt für Mathematik und ihre Grenzgebiete Mathematics Abstracts

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.

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.

Subject Areas: Pattern recognition [UYQP], Computer science [UY], Data capture & analysis [UNC]