Advances in Kernel Methods: Support Vector Learning

Front Cover
Bernhard Schölkopf, Christopher J. C. Burges, Alexander J. Smola
MIT Press, 1999 - 376 pages
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 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

From inside the book

Contents

vi
12
Roadmap
17
Three Remarks on the Support Vector Method of Function
25
Generalization Performance of Support Vector Machines
43
Bayesian Voting Schemes and Large Margin Classifiers 55
55
Support Vector Machines Reproducing Kernel Hilbert Spaces
69
Geometry and Invariance in Kernel Based Methods
89
Entropy Numbers Operators and Support Vector Kernels
127
Fast Training of Support Vector Machines Using Sequential
185
Support Vector Machines for Dynamic Reconstruction of
211
Using Support Vector Machines for Time Series Prediction
243
Pairwise Classification and Support Vector Machines
255
Reducing the Runtime Complexity in Support Vector Machines
271
Support Vector Regression with ANOVA Decomposition Kernels
285
Support Vector Density Estimation
293
Combining Support Vector and Mathematical Programming
307

Vector Classification
147
Making LargeScale Support Vector Machine Learning Practical
169
Kernel Principal Component Analysis
327
Copyright

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About the author (1999)

Alexander J. Smola is Senior Principal Researcher and Machine Learning Program Leader at National ICT Australia/Australian National University, Canberra.

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