Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond

Couverture

A comprehensive introduction to Support Vector Machines and related kernel methods.

In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs---kernels--for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics.

Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.

 

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Table des matières

A Tutorial Introduction
1
CONCEPTS AND TOOLS
23
Risk and Loss Functions
61
Regularization
87
Elements of Statistical Learning Theory
125
Optimization
149
SUPPORT VECTOR MACHINES
187
Quantile Estimation and Novelty Detection
227
KERNEL METHODS
405
Kernel Feature Extraction
427
Kernel Fisher Discriminant
457
Bayesian Kernel Methods
469
Regularized Principal Manifolds
517
PreImages and Reduced Set Methods
543
A Addenda
569
B Mathematical Prerequisites
575

Regression Estimation
251
Implementation
279
Incorporating Invariances
333
Learning Theory Revisited
359
References
591
Index
617
Notation and Symbols
625
Droits d'auteur

Expressions et termes fréquents

Fréquemment cités

Page 603 - Application of the Karhunen-Loeve procedure for the characterization of human faces," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.
Page 593 - 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, San Francisco, California, 1998.
Page 610 - A tutorial on support vector machines for pattern recognition," Data Mining and Knowledge Discovery 2, 1-47(1998). 5. B. Scholkopf, CJC Burges, and AJ Smola, eds., Advances in Kernel Methods, Support Vector Learning, MIT Press, Cambridge MA (1999). 6. Y. Lecun et al., "Comparison of learning algorithms for handwritten digit recognition,
Page 600 - Buhmann. Pairwise data clustering by deterministic annealing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(1):1-14, 1997.

À propos de l'auteur (2002)

Bernhard Sch lkopf is Professor and Director at the Max Planck Institute for Biological Cybernetics in T bingen, 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.

Informations bibliographiques