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E-raamat: Advances in Large–Margin Classifiers

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Edited by (Max Planck Institute for Intelligent Systems), Edited by (University of California, Berkeley), Edited by , Edited by (Univ Of Alberta)
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The concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural networks, and support vector machines. The fact that it is the margin, or confidence level, of a classification--that is, a scale parameter--rather than a raw training error that matters has become a key tool for dealing with classifiers. This book shows how this idea applies to both the theoretical analysis and the design of algorithms.The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. Among the contributors are Manfred Opper, Vladimir Vapnik, and Grace Wahba.



The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research.
Preface ix Introduction to Large Margin Classifiers 1(30) Roadmap 31(6) I Support Vector Machines 37(96) Dynamic Alignment Kernels 39(12) Chris Watkins Natural Regularization from Generative Models 51(10) Nuria Oliver Bernhard Scholkopf Alexander J. Smola Probabilities for SV Machines 61(14) John C. Platt Maximal Margin Perceptron 75(40) Adam Kowalczyk Large Margin Rank Boundaries for Ordinal Regression 115(18) Ralf Herbrich Thore Graepel Klaus Obermayer II Kernel Machines 133(72) Generalized Support Vector Machines 135(12) Olvi L. Mangasarian Linear Discriminant and Support Vector Classifiers 147(24) Isabelle Guyon David G. Stork Regularization Networks and Support Vector Machines 171(34) Theodoros Evgeniou Massimiliano Pontil Tomaso Poggio III Boosting 205(54) Robust Ensemble Learning 207(14) Gunnar Ratsch Bernhard Scholkopf Alexander J. Smola Sebastian Mika Takashi Onoda Klaus-Robert Muller Functional Gradient Techniques for Combining Hypotheses 221(26) Llew Mason Jonathan Baxter Peter L. Bartlett Marcus Frean Towards a Strategy for Boosting Regressors 247(12) Grigoris Karakoulas John Shawe-Taylor IV Leave-One-Out Methods 259(68) Bounds on Error Expectation for SVM 261(20) Vladimir Vapnik Olivier Chapelle Adaptive Margin Support Vector Machines 281(16) Jason Weston Ralf Herbrich GACV for Support Vector Machines 297(14) Grace Wahba Yi Lin Hao Zhang Gaussian Processes and SVM: Mean Field and Leave-One-Out 311(16) Manfred Opper Ole Winther V Beyond the Margin 327(62) Computing the Bayes Kernel Classifier 329(20) Pal Rujan Mario Marchand Margin Distribution and Soft Margin 349(10) John Shawe-Taylor Nello Cristianini Support Vectors and Statistical Mechanics 359(10) Rainer Dietrich Manfred Opper Haim Sompolinsky Entropy Numbers for Convex Combinations and MLPs 369(20) Alexander J. Smola Andre Elisseeff Bernhard Scholkopf Robert C. Williamson References 389(20) Index 409
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