CiNii Books - Advances in kernel methods : support vector learning

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 Muller, Manfred Opper, Edgar E. Osuna, John C. Platt, Gunnar Ratsch, Bernhard Schoelkopf, 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.

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Advances in kernel methods : support vector learning

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