Advances in Kernel Methods: Support Vector Learning / Edition 1

Advances in Kernel Methods: Support Vector Learning / Edition 1

by Bernhard Scholkopf
     
 

ISBN-10: 0262194163

ISBN-13: 9780262194167

Pub. Date: 12/18/1998

Publisher: 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…  See more details below

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.

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Product Details

ISBN-13:
9780262194167
Publisher:
MIT Press
Publication date:
12/18/1998
Edition description:
New Edition
Pages:
386
Product dimensions:
8.00(w) x 10.00(h) x 1.30(d)

Table of Contents

Preface
1Introduction to Support Vector Learning1
2Roadmap17
ITheory23
3Three Remarks on the Support Vector Method of Function Estimation25
4Generalization Performance of Support Vector Machines and Other Pattern Classifiers43
5Bayesian Voting Schemes and Large Margin Classifiers55
6Support Vector Machines, Reproducing Kernel Hilbert Spaces, and Randomized GACV69
7Geometry and Invariance in Kernel Based Methods89
8On the Annealed VC Entropy for Margin Classifiers: A Statistical Mechanics Study117
9Entropy Numbers, Operators and Support Vector Kernels127
IIImplementations145
10Solving the Quadratic Programming Problem Arising in Support Vector Classification147
11Making Large-Scale Support Vector Machine Learning Practical169
12Fast Training of Support Vector Machines Using Sequential Minimal Optimization185
IIIApplications209
13Support Vector Machines for Dynamic Reconstruction of a Chaotic System211
14Using Support Vector Machines for Time Series Prediction243
15Pairwise Classification and Support Vector Machines255
IVExtensions of the Algorithm269
16Reducing the Run-time Complexity in Support Vector Machines271
17Support Vector Regression with ANOVA Decomposition Kernels285
18Support Vector Density Estimation293
19Combining Support Vector and Mathematical Programming Methods for Classification307
20Kernel Principal Component Analysis327
References353
Index373

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