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The Nature of Statistical Learning Theory

Authors: Vapnik, Vladimir N.

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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.

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

Table of contents (7 chapters)

Table of contents (7 chapters)

Buy this book

eBook $74.99
price for USA in USD
  • ISBN 978-1-4757-2440-0
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
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Bibliographic Information

Bibliographic Information
Book Title
The Nature of Statistical Learning Theory
Authors
Copyright
1995
Publisher
Springer-Verlag New York
Copyright Holder
Springer Science+Business Media New York
eBook ISBN
978-1-4757-2440-0
DOI
10.1007/978-1-4757-2440-0
Edition Number
1
Number of Pages
XV, 188
Number of Illustrations
18 b/w illustrations
Topics

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