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About this book
The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning from the general point of view of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. These include: - the general setting of learning problems and the general model of minimizing the risk functional from empirical data - a comprehensive analysis of the empirical risk minimization principle and shows how this allows for the construction of necessary and sufficient conditions for consistency - non-asymptotic bounds for the risk achieved using the empirical risk minimization principle - principles for controlling the generalization ability of learning machines using small sample sizes - introducing a new type of universal learning machine that controls the generalization ability.
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Table of contents (7 chapters)
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Front Matter
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Back Matter
Reviews
"This interesting book helps a reader to understand the interconnections between various streams in the empirical modeling realm and may be recommended to any reader who feels lost in modern terminology." V.V. Fedorov, Oak Ridge National Laboratory, USA
Authors and Affiliations
Bibliographic Information
Book Title: The Nature of Statistical Learning Theory
Authors: Vladimir N. Vapnik
DOI: https://doi.org/10.1007/978-1-4757-2440-0
Publisher: Springer New York, NY
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eBook Packages: Springer Book Archive
Copyright Information: Springer Science+Business Media New York 1995
eBook ISBN: 978-1-4757-2440-0Published: 17 April 2013
Edition Number: 1
Number of Pages: XV, 188
Number of Illustrations: 18 b/w illustrations
Topics: Probability Theory and Stochastic Processes, Statistics, general, Artificial Intelligence