Advances in Kernel Methods: Support Vector Learning

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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.

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.

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Three Remarks on the Support Vector Method of Function
Generalization Performance of Support Vector Machines
Bayesian Voting Schemes and Large Margin Classifiers
Support Vector Machines Reproducing Kernel Hilbert Spaces
Geometry and Invariance in Kernel Based Methods
Entropy Numbers Operators and Support Vector Kernels
Vector Classification
Support Vector Machines for Dynamic Reconstruction of
Using Support Vector Machines for Time Series Prediction
Pairwise Classification and Support Vector Machines
Reducing the Runtime Complexity in Support Vector Machines
Support Vector Regression with ANOVA Decomposition Kernels
Support Vector Density Estimation
Combining Support Vector and Mathematical Programming
Kernel Principal Component Analysis

Making LargeScale Support Vector Machine Learning Practical
Fast Training of Support Vector Machines Using Sequential

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Page 368 - Golowich, and A. Smola. Support Vector Method for Function Approximation, Regression Estimation and Signal Processing.
Page 360 - Application of the Karhunen-Loeve procedure for the characterization of human faces," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.
Page 354 - Proc. of the 4th Midwest Artificial Intelligence and Cognitive Science Society Conference, pages 97-101, 1992.
Page 355 - PS Bradley and OL Mangasarian. Feature selection via concave minimization and support vector machines. In J. Shavlik, editor, Machine Learning Proceedings of the Fifteenth International Conference(ICML '98), pages 82-90.

About the author (1999)

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

Bernhard Schölkopf is Director at the Max Planck Institute for Intelligent Systems 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.

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