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Statistical Learning Theory

Yuhai Wu, Vladimir N. Vapnik

Technometrics · 1999 · 26,773 citations

doi.org/10.2307/1271368

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.

Cite this paper

Wu, Y., & Vapnik, V. N. (1999). Statistical learning theory. Technometrics, 41(4), 377. https://doi.org/10.2307/1271368

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