Books: Advances in Kernel Methods

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	Dec 1998
	ISBN 0262194163
	392 pp.

Advances in Kernel Methods

Bernhard Schölkopf , Christopher J. C. Burges and Sebastian Mika

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.

Table of Contents

		Preface
1		Introduction to Support Vector Learning
2		Roadmap
I		Theory
3		Three Remarks on the Support Vector Method of Function Estimation by Vladimir Vapnik
4		Generalization Performance of Support Vector Machines and Other Patter n Classifiers by Peter Bartlett and John Shawe-Taylor
5		Bayesian Voting Schemes and Large Margin Classifiers by Nello Cristianini and John Shawe-Taylor
6		Support Vector Machines, Reproducing Kernel Hilbert Spaces, and Randomized GACV by Grace Wahba
7		Geometry and Invariance in Kernel Based Methods by Christopher J. C. Burges
8		On the Annealed VC Entropy for Margin Classifiers: A Statistical Mechanics Study by Manfred Opper
9		Entropy Numbers, Operators and Support Vector Kernels by Robert C. Williamson, Alex J. Smola and Bernhard Schökopf
II		Implementations
10		Solving the Quadratic Programming Problem Arising in Support Vector Classification by Linda Kaufman
11		Making Large-Scale Support Vector Machine Learning Practical by Thorsten Joachims
12		Fast Training of Support Vector Machines Using Sequential Minimal Opti mization by John C. Platt
III		Applications
13		Support Vector Machines for Dynamic Reconstruction of a Chaotic System by Davide Mattera and Simon Haykin
14		Using Support Vector Machines for Time Series Prediction by Klaus-Robert Müller, Alex J. Smola, Gunnar Rätsch, Bernhard Schökpf, Jens Kohlmorgen and Vladimir Vapnik
15		Pairwise Classification and Support Vector Machines by Ulrich Keßel
IV		Extensions of the Algorithm
16		Reducing the Run-time Complexity in Support Vector Machines by Edgar E. Osuna and Federico Girosi
17		Support Vector Regression with ANOVA Decomposition Kernels by Mark O. Stitson, Alex Gammerman, Vladimir Vapnik, Volodya Vovk, Chris Watkins and Jason Weston
18		Support Vector Density Estimation by Jason Weston, Alex Gammerman, Mark O. Stitson, Vladimir Vapnik and Chris Watkins
19		Combining Support Vector and Mathematical Programming Methods for Classification by Kristin P. Bennett
20		Kernel Principal Component Analysis by Bernhard Schökopf, Alex J. Smola and Klaus-Robert Müller
		References
		Index

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