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Advances in Kernel methods: support vector learning

Author: Schölkopf, Bernhard ; Burges, Christopher J. C. ; Smola, Alexander J.Meeting: Neural Information Processing Systems (NIPS) Conference, Breckenridge, CO, December 1997Publisher: MIT Press, 1998.Language: EnglishDescription: 376 p. : Graphs/Ill. ; 26 cm.ISBN: 0262194163Type of document: BookBibliography/Index: Includes bibliographical references and index
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Item type Current location Collection Call number Status Date due Barcode Item holds
Book Europe Campus
Main Collection
Print Q325 .A38 1998
(Browse shelf)
Available 001207582
Total holds: 0

Includes bibliographical references and index


Advances in Kernel Methods Support Vector Learning Contents Preface 1 Introduction to Support Vector Learning 2 Roadmap I Theory ix 1 17 23 3 Three Remarks on the Support Vector Method of Function Estimation Vladimir Vapnik 25 4 Generalization Performance of Support Vector Machines and Other Pattern Classifiers Peter Bartlett 81 John Shawe-Taylor 43 55 5 Bayesian Voting Schemes and Large Margin Classifiers Nello Cristianini and John Shawe-Taylor 6 Support Vector Machines, Reproducing Kernel Hilbert Spaces, and Randomized GACV Grace Wahba 69 89 7 Geometry and Invariance in Kernel Based Methods Christopher J. C. Burges 8 On the Annealed VC Entropy for Margin Classifiers: A Statistical Mechanics Study Manfred Opper 117 127 9 Entropy Numbers, Operators and Support Vector Kernels Robert C. Williamson, Alex J. Smola 64 Bernhard Schölkopf II Implementations 145 147 169 10 Solving the Quadratic Programming Problem Arising in Support Vector Classification Linda Kaufman 11 Making Large-Scale Support Vector Machine Learning Practical Thorsten Joachims 12 Fast Training of Support Vector Machines Using Sequential Minimal Optimization John C. Platt 185 III Applications 209 211 243 13 Support Vector Machines for Dynamic Reconstruction of a Chaotic System Davide Mattera and Simon Haykin 14 Using Support Vector Machines for Time Series Prediction Klaus-Robert Müller, Alex J. Smola, Gunnar Rätsch, Bernhard Schölkopf, Jens Kohlmorgen and Vladimir Vapnik 15 Pairwise Classification and Support Vector Machines Ulrich Krefiel 255 IV Extensions of the Algorithm 16 Reducing the Run-time Complexity in Support Vector Machines Edgar E. Osuna and Federico Girosi 269 271 285 17 Support Vector Regression with ANOVA Decomposition Kernels Mark 0. Stitson, Alex Gammerman, Vladimir Vapnik, Volodya Vovk, Chris Watkins and Jason Weston 18 Support Vector Density Estimation Jason Weston, Alex Gammerman, Mark 0. Stitson, Vladimir Vapnik, Volodya Vovk and Chris Watkins 293 19 Combining Support Vector and Mathematical Programming Methods for Classification Kristin P. Bennett 307 327 20 Kernel Principal Component Analysis Bernhard Scholkopf, Alex J. Smola and Klaus-Robert Müller References Index 353 373

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