An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Cambridge University Press, 3/23/2000
EAN 9780521780193, ISBN10: 0521780195
Hardcover, 204 pages, 24.7 x 17.4 x 1.5 cm
Language: English
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.
'... the most accessible introduction to the area I have yet seen'. D. J. Hand, Publication of the International Statistical Institute