Introduction to Support Vector Machines and Other Kernel-based Learning Methods
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...
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A comprehensive introduction to this recent method for machine learning and data mining.
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.
\ From the Publisher"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\ \
