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

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

$90.00

  • Date Published: March 2000
  • availability: In stock
  • format: Hardback
  • isbn: 9780521780193

$90.00
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About the Authors
  • 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.

    • Devoted to an organic treatment of Support Vector Machines
    • Self-contained course-book for advanced students or introduction for practitioners, with recipes, pseudo-code and practical advice
    • Contains examples, exercises, case studies and pointers to relevant literature and web-sites, where updated software is available
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    Reviews & endorsements

    "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

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    Product details

    • Date Published: March 2000
    • format: Hardback
    • isbn: 9780521780193
    • length: 198 pages
    • dimensions: 256 x 180 x 23 mm
    • weight: 0.574kg
    • contains: 12 b/w illus. 5 colour illus. 25 exercises
    • availability: In stock
  • 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.

  • Authors

    Nello Cristianini, University of Bristol

    John Shawe-Taylor, Royal Holloway, University of London

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