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Statistical Learning Theory

TechnometricsPublished 1 November 1999
Yuhai Wu, Vladimir Vapnik
Citations26,913
SJR quartileQ1
SJR score1.41
SNIP1.93

TL;DR

Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.

Abstract

A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis of empirical data. Highly applicable to a variety of computer science and robotics fields, this book offers lucid coverage of the theory as a whole. Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.

Keywords

Computer Science