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Title Advances in kernel methods : support vector learning / edited by Bernhard Schölkopf, Christopher J.C. Burges, Alexander J. Smola.
Imprint Cambridge, Mass. : MIT Press, ©1999.
©1999

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Subject Machine learning.
Algorithms.
Kernel functions.
Alt Name Schölkopf, Bernhard.
Burges, Christopher J. C.
Smola, Alexander J.
Description 1 online resource (vii, 376 pages) : illustrations
Bibliography Note Includes bibliographical references (pages 353-371) and index.
Contents Preface -- Introduction to support vector learning -- Roadmap -- Three remarks on the support vector method of function estimation / Valdimir Vapnik -- Generalization performance of support vector machines and other pattern classifiers / Peter Bartlett and John Shawe-Taylor -- Bayesian voting schemes and large margin classifiers / Nello Cristianini and John Shawe-Taylor -- Support vector machines, reproducing kernal Hilbert spaces, and randomized GACV / Grace Wahba -- Geometry and invariance in kernel based methods / Christopher J.C. Burgess -- On the annealed VC entropy for margin classifiers : a statistical mechanics study / Manfred Opper -- Entropy numbers, operators and support vector kernels / Robert C. Williamson, Alex J. Smola and Berhard Schölkopf -- Solving the quadratic programming problem arising in support vector classification / Linda Kaufman -- Making large-scale support vector machine learning practical / Thorsten Joachims -- Fast training of support vector machines using sequential minimal optimization / John C. Platt -- Support vector machines for dynamic reconstruction of a chaotic system / David Mattera and Simon Haykin -- Using support vector machines for time series prediction / Klaus-Robert Müller . [and others] -- Pairwise classification and support vector machines / Ulrich Kressel -- Reducing the run-time complexity in support vector machines / Edgar E. Osuna and Federico Girosi -- Support vector regression with ANOVA decomposition kernels / Mark O. Stitson . [and others] -- Support vector density estimation / Jason Weston . [and others] -- Combining support vector and mathematical programming methods for classification / Kristin P. Bennett -- Kernel principal component analysis / Berhard Schölkopf, Alex J. Smola and Klaus-Robert Müller.
Note Print version record.
English.
ISBN 0585128294 (electronic bk.)
9780585128290 (electronic bk.)
9780262194167 (alk. paper)
0262194163 (alk. paper)
9780262283199 (electronic book)
0262283190 (electronic book)
0262194163 (alk. paper)
OCLC # 44957981
Additional Format Print version: Advances in kernel methods. Cambridge, Mass. : MIT Press, ©1999 0262194163 (DLC) 98040302 (OCoLC)39706952
Table of Contents
 Preface 
 1Introduction to Support Vector Learning1
 2Roadmap17
ITheory23
 3Three Remarks on the Support Vector Method of Function Estimation / Vladimir Vapnik25
 4Generalization Performance of Support Vector Machines and Other Pattern Classifiers / Peter Bartlett, John Shawe-Taylor43
 5Bayesian Voting Schemes and Large Margin Classifiers / Nello Cristianini, John Shawe-Taylor55
 6Support Vector Machines, Reproducing Kernel Hilbert Spaces, and Randomized GACV / Grace Wahba69
 7Geometry and Invariance in Kernel Based Methods / Christopher J. C. Burges89
 8On the Annealed VC Entropy for Margin Classifiers: A Statistical Mechanics Study / Manfred Opper117
 9Entropy Numbers, Operators and Support Vector Kernels / Robert C. Williamson, Alex J. Smola, Bernhard Scholkopf127
IIImplementations145
 10Solving the Quadratic Programming Problem Arising in Support Vector Classification / Linda Kaufman147
 11Making Large-Scale Support Vector Machine Learning Practical / Thorsten Joachims169
 12Fast Training of Support Vector Machines Using Sequential Minimal Optimization / John C. Platt185
IIIApplications209
 13Support Vector Machines for Dynamic Reconstruction of a Chaotic System / Davide Mattera, Simon Haykin211
 14Using Support Vector Machines for Time Series Prediction / Klaus-Robert Muller, Alex J. Smolo, Gunnar Ratsch [et al.]243
 15Pairwise Classification and Support Vector Machines / Ulrich Kressel255
IVExtensions of the Algorithm269
 16Reducing the Run-time Complexity in Support Vector Machines / Edgar E. Osuna, Federico Girosi271
 17Support Vector Regression with ANOVA Decomposition Kernels / Mark O. Stitson, Alex Gammerman, Vladimir Vapnik [et al.]285
 18Support Vector Density Estimation / Jason Weston, Alex Gammerman, Mark O. Stitson [et al.]293
 19Combining Support Vector and Mathematical Programming Methods for Classification / Kristin P. Bennett307
 20Kernel Principal Component Analysis / Bernhard Scholkopf, Alex J. Smola, Klaus-Robert Muller327
 References353
 Index373


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