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The nature of statistical learning theory

Author: Vladimir Naumovich Vapnik
Publisher: New York : Springer, ©2000.
Series: Statistics for engineering and information science.
Edition/Format:   Print book : English : 2nd edView all editions and formats
Database:WorldCat
Summary:
"The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem 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."--The publisher.
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Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Vladimir Naumovich Vapnik
ISBN: 0387987800 9780387987804
OCLC Number: 41959173
Description: xix, 314 pages : illustrations ; 24 cm.
Contents: Setting of the learning problem --
Consistency of learning processes --
Bounds on the rate of convergence of learning processes --
Controlling the generalization ability of learning processes --
Methods of pattern recognition --
Methods of function estimation --
Direct methods in statistical learning theory --
The vicinal risk minimization principle and the SVMs --
Conclusion : what is important in learning theory?
Series Title: Statistics for engineering and information science.
Responsibility: Vladimir N. Vapnik.
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Abstract:

Discusses the fundamental ideas which lie behind the statistical theory of learning and generalization. This book considers learning as a general problem of function estimation based on empirical  Read more...

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From the reviews of the second edition: ZENTRALBLATT MATH "...written in a concise style. It must be recommended to scientists of statistics, mathematics, physics, and computer science." SHORT BOOK Read more...

 
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