The Nature of Statistical Learning Theory

Vladimir N. Vapnik - Collection Statistics for Engineering and Information Science

314 pages, parution le 30/09/2001 (2^eme édition)

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Résumé

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. These include:

the setting of learning problems based on the model of minimizing the risk functional from empirical data
a comprehensive analysis of the empirical risk minimization principle including necessary and sufficient conditions for its 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 based on these bounds
the Support Vector methods that control the generalization ability when estimating function using small sample size.

The second edition of the book contains three new chapters devoted to further development of the learning theory and SVM techniques. These include:

the theory of direct method of learning based on solving multidimensional integral equations for density, conditional probability, and conditional density estimation
a new inductive principle of learning.

Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.

L'auteur - Vladimir N. Vapnik

Vladimir N. Vapnik is Technology Leader AT&T Labs-Research and Professor of London University. He is one of the founders of statistical learning theory, and the author of seven books published in English, Russian, German, and Chinese.

Sommaire

Informal Reasoning and Comments
Consistency of Learning Processes
Bounds on the Rate of Convergence of Learing 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

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Caractéristiques techniques

	PAPIER
Éditeur(s)	Springer
Auteur(s)	Vladimir N. Vapnik
Collection	Statistics for Engineering and Information Science
Parution	30/09/2001
Édition	2^eme édition
Nb. de pages	314
Format	16 x 24
Couverture	Relié
Poids	615g
Intérieur	Noir et Blanc
EAN13	9780387987804
ISBN13	978-0-387-98780-4