Advances in Kernel Methods: Support Vector Learning / Edition 1

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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.

A young boy hears the story of his great-great-great-great- grandfather and his brother who came to the United States to make a better life for themselves helping to build the transcontinental railroad.

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Product Details

  • ISBN-13: 9780262194167
  • Publisher: MIT Press
  • Publication date: 12/18/1998
  • Edition description: New Edition
  • Edition number: 1
  • Pages: 386
  • Product dimensions: 8.00 (w) x 10.00 (h) x 1.30 (d)

Meet the Author

Bernhard Schölkopf is Professor and Director at the Max Planck Institute for Biological Cybernetics 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.

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Table of Contents

Preface
1 Introduction to Support Vector Learning 1
2 Roadmap 17
I Theory 23
3 Three Remarks on the Support Vector Method of Function Estimation 25
4 Generalization Performance of Support Vector Machines and Other Pattern Classifiers 43
5 Bayesian Voting Schemes and Large Margin Classifiers 55
6 Support Vector Machines, Reproducing Kernel Hilbert Spaces, and Randomized GACV 69
7 Geometry and Invariance in Kernel Based Methods 89
8 On the Annealed VC Entropy for Margin Classifiers: A Statistical Mechanics Study 117
9 Entropy Numbers, Operators and Support Vector Kernels 127
II Implementations 145
10 Solving the Quadratic Programming Problem Arising in Support Vector Classification 147
11 Making Large-Scale Support Vector Machine Learning Practical 169
12 Fast Training of Support Vector Machines Using Sequential Minimal Optimization 185
III Applications 209
13 Support Vector Machines for Dynamic Reconstruction of a Chaotic System 211
14 Using Support Vector Machines for Time Series Prediction 243
15 Pairwise Classification and Support Vector Machines 255
IV Extensions of the Algorithm 269
16 Reducing the Run-time Complexity in Support Vector Machines 271
17 Support Vector Regression with ANOVA Decomposition Kernels 285
18 Support Vector Density Estimation 293
19 Combining Support Vector and Mathematical Programming Methods for Classification 307
20 Kernel Principal Component Analysis 327
References 353
Index 373
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