Advances in Kernel Methods: Support Vector Learning(The MIT Press)

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.ContributorsPeter 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:
Introduction to support vector learning; roadmap. Part 1 Theory: three remarks on the support vector method of function estimation, Vladimir Vapnik; generalization performance of support vector machines and other pattern classifiers, Peter Bartlett and John Shawe-Taylor; Bayesian voting schemes and large margin classifiers, Nello Cristianini and John Shawe-Taylor; support vector machines, reproducing kernel Hilbert spaces, and randomized GACV, Grace Wahba; geometry and invariance in kernel based methods, Christopher J.C. Burges; on the annealed VC entropy for margin classifiers - a statistical mechanics study, Manfred Opper; entropy numbers, operators and support vector kernels, Robert C. Williamson et al. Part 2 Implementations: solving the quadratic programming problem arising in support vector classification, Linda Kaufman; making large-scale support vector machine learning practical, Thorsten Joachims; fast training of support vector machines using sequential minimal optimization, John C. Platt. Part 3 Applications: support vector machines for dynamic reconstruction of a chaotic system, Davide Mattera and Simon Haykin; using support vector machines for time series prediction, Klaus-Robert Muller et al; pairwise classification and support vector machines, Ulrich Kressel. Part 4 Extensions of the algorithm: reducing the run-time complexity in support vector machines, Edgar E. Osuna and Federico Girosi; support vector regression with ANOVA decomposition kernels, Mark O. Stitson et al; support vector density estimation, Jason Weston et al; combining support vector and mathematical programming methods for classification, Bernhard Scholkopf et al.

About the Author :
Bernhard Schölkopf is Director at the Max Planck Institute for Intelligent Systems in Tübingen, Germany. He is coauthor of Learning with Kernels (2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large-Margin Classifiers (2000), and Kernel Methods in Computational Biology (2004), all published by the MIT Press. Alexander J. Smola is Senior Principal Researcher and Machine Learning Program Leader at National ICT Australia/Australian National University, Canberra.