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Statistical Learning Theory

Yuhai Wu; Vladimir Vapnik · Technometrics · 1999

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Author / responsible party
Yuhai Wu; Vladimir Vapnik
Publisher
Technometrics
Year
1999
Language
English

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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.

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APA 7

Wu, Y. & Vapnik, V. (1999). Statistical Learning Theory. https://doi.org/10.2307/1271368

MLA

Wu, Yuhai, and Vladimir Vapnik. "Statistical Learning Theory." 1999. https://doi.org/10.2307/1271368.

Chicago

Wu, Yuhai and Vladimir Vapnik. 1999. "Statistical Learning Theory.". https://doi.org/10.2307/1271368.

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Wu, Y. and Vapnik, V. 1999, Statistical Learning Theory, Technometrics, available at: https://doi.org/10.2307/1271368 [Accessed 6 Aug. 2026].

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Resource details

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Title
Statistical Learning Theory
Author / contributors
Yuhai Wu; Vladimir Vapnik
Publisher
Technometrics
Publication year
1999
Language
English

Subjects

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Generalization; Statistical learning theory; Artificial intelligence; Consistency (knowledge bases); Computer science; Machine learning; Statistical theory; Variety (cybernetics); Process (computing); Algorithmic learning theory; Learning theory; Function (biology)
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