Advances in Kernel Methods: Support Vector Learning

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Bernhard Schölkopf, Christopher J. C. Burges, Alexander J. Smola
MIT Press, 1999 - Computers - 376 pages
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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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Chinese immigrants that helped build the transcontinental railroad.

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Some websites in this book is very helpfull

Contents

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
Support Vector Machines Reproducing Kernel Hilbert Spaces
69
Geometry and Invariance in Kernel Based Methods
89
Entropy Numbers Operators and Support Vector Kernels
127
Vector Classification
147
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
Kernel Principal Component Analysis
327

Making LargeScale Support Vector Machine Learning Practical
169
Fast Training of Support Vector Machines Using Sequential
185

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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.

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About the author (1999)

Bernhard Scholkopf is Managing Director of the Max Planck Institute for Biological Cybernetics in Tubingen, Germany. He is coauthor of "Learning with Kernels" (MIT Press, 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

Burges is Distinguished Member of Technical Staff at Lucent Technologies, Bell Laboratories.

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

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