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Statistical Learning Theory

Technometrics · 1999 · Vol. 41(4) · pp. 377–377

Yuhai Wu✉(Purdue University West Lafayette)Vladimir Vapnik

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.

Evolutionary Algorithms and Applications Metaheuristic Optimization Algorithms Research Neural Networks and ApplicationsGeneralizationStatistical learning theoryArtificial intelligenceConsistency (knowledge bases)Computer scienceMachine learningStatistical theoryVariety (cybernetics)Process (computing)Algorithmic learning theory

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