Advances in Kernel Methods

Support Vector Learning
Overview

The Support Vector Machine is a powerful new learning algorithm for solving a variety of learning and function estimation problems, such as pattern recognition, regression estimation, and operator inversion. The impetus for this collection was a workshop on Support Vector Machines held at the 1997 NIPS conference. The contributors, both university researchers and engineers developing applications for the corporate world, form a Who's Who of this exciting new area.

Contributors: Peter Bartlett, Kristin P. Bennett, Christopher J.C. Burges, Nello Cristianini, Alex Gammerman, Federico Girosi, Simon Haykin, Thorsten Joachims, Linda Kaufman, Jens Kohlmorgen, Ulrich Kreßel, Davide Mattera, Klaus-Robert Müller, Manfred Opper, Edgar E. Osuna, John C. Platt, Gunnar Rätsch, Bernhard Schölkopf, John Shawe-Taylor, Alexander J. Smola, Mark O. Stitson, Vladimir Vapnik, Volodya Vovk, Grace Wahba, Chris Watkins, Jason Weston, Robert C. Williamson.

Table of Contents

  1. Preface
  2. 1. Introduction to Support Vector Learning
  3. 2. Roadmap
  4. 3. Three Remarks on the Support Vector Method of Function Estimation

    Vladimir Vapnik

  5. 4. Generalization Performance of Support Vector Machines and Other Patter n Classifiers

    Peter Bartlett and John Shawe-Taylor

  6. 5. Bayesian Voting Schemes and Large Margin Classifiers

    Nello Cristianini and John Shawe-Taylor

  7. 6. Support Vector Machines, Reproducing Kernel Hilbert Spaces, and Randomized GACV

    Grace Wahba

  8. 7. Geometry and Invariance in Kernel Based Methods

    Christopher J. C. Burges

  9. 8. On the Annealed VC Entropy for Margin Classifiers: A Statistical Mechanics Study

    Manfred Opper

  10. 9. Entropy Numbers, Operators and Support Vector Kernels

    Robert C. Williamson, Alex J. Smola and Bernhard Schökopf

  11. 10. Solving the Quadratic Programming Problem Arising in Support Vector Classification

    Linda Kaufman

  12. 11. Making Large-Scale Support Vector Machine Learning Practical

    Thorsten Joachims

  13. 12. Fast Training of Support Vector Machines Using Sequential Minimal Optimization

    John C. Platt

  14. 13. Support Vector Machines for Dynamic Reconstruction of a Chaotic System

    Davide Mattera and Simon Haykin

  15. 14. Using Support Vector Machines for Time Series Prediction

    Klaus-Robert Müller, Alex J. Smola, Gunnar Rätsch, Bernhard Schökpf, Jens Kohlmorgen and Vladimir Vapnik

  16. 15. Pairwise Classification and Support Vector Machines

    Ulrich Keßel

  17. 16. Reducing the Run-time Complexity in Support Vector Machines

    Edgar E. Osuna and Federico Girosi

  18. 17. Support Vector Regression with ANOVA Decomposition Kernels

    Mark O. Stitson, Alex Gammerman, Vladimir Vapnik, Volodya Vovk, Chris Watkins and Jason Weston

  19. 18. Support Vector Density Estimation

    Jason Weston, Alex Gammerman, Mark O. Stitson, Vladimir Vapnik and Chris Watkins

  20. 19. Combining Support Vector and Mathematical Programming Methods for Classification

    Kristin P. Bennett

  21. 20. Kernel Principal Component Analysis

    Bernhard Schökopf, Alex J. Smola and Klaus-Robert Müller

  22. References
  23. Index