Advances in Kernel Methods: Support Vect
β Schoelkopf B., Burges C.J.C., Smola A.J. (eds.)
π Library
π
1999
π MIT
π English
β¦ LIBER β¦
π
Advances in Kernel Methods: Support Vector Learning
β Scribed by Bernhard SchΓΆlkopf (editor), Christopher J. C. Burges (editor), Alexander J. Smola (editor)
- Publisher
- Mit Pr
- Year
- 1998
- Tongue
- English
- Leaves
- 306
- Category
- Library
β¬ Acquire This Volume
No coin nor oath required. For personal study only.
β¦ Synopsis
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 Muller, Manfred Opper, Edgar E. Osuna, John C. Platt, Gunnar Ratsch, Bernhard Scholkopf, John Shawe-Taylor, Alexander J. Smola, Mark O. Stitson, Vladimir Vapnik, Volodya Vovk, Grace Wahba, Chris Watkins, Jason Weston, Robert C. Williamson.
β¦ Table of Contents
Contents
Preface
1 Introduction to Support Vector Learning
2 Roadmap
I. Theory
3 Three Remarks on the Support Vector Method of Function Estimation
4 Generalization Performance of Support Vector Machines and Other Pattern Classifiers
5 Bayesian Voting Schemes as Large Margin Classifiers
6 Support Vector Machines, Reproducing Kernel Hilbert Spaces and the Randomized GACV
7 Geometry and Invariance in Kernel Based Methods
8 On the Annealed VC Entropy for Margin Classifiers: A Statistical Mechanics Study
9 Entropy Numbers Operators and Support Vector Kernels
II. Implementations
10 Solving the Quadratic Programming problem arising in support vector classification
11 Making Large-Scale SVM Learning Practical
12 Fast Training of Support Vector Machines using Sequential Minimal Optimization
Ill. Applications
14 Using Support Vector Machines for Time Series Prediction
IV. Extensions of the Algorithm
18 Support Vector Density Estimation
19 Combining Support Vector and Mathematical Programming Methods for Classification
20 Kernel Principal Component Analysis
References
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