Statistical Learning Theory

Vladimir N. Vapnik (Consultant)

$418.95

Hardback

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English

Wiley-Interscience
16 September 1998

Functional analysis & transforms; Machine learning

Series: Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control

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.

By: Vladimir N. Vapnik (Consultant)
Imprint: Wiley-Interscience
Country of Publication: United States
Dimensions: Height: 241mm, Width: 163mm, Spine: 36mm
Weight: 1.211kg
ISBN: 9780471030034
ISBN 10: 0471030031
Series: Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control
Pages: 768
Publication Date: 16 September 1998
Audience: College/higher education , Professional and scholarly , Professional & Vocational , A / AS level , Further / Higher Education
Format: Hardback
Publisher's Status: Active

Vladimir Naumovich Vapnik is one of the main developers of the Vapnik-Chervonenkis theory of statistical learning, and the co-inventor of the support vector machine method, and support vector clustering algorithm.

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Statistical Learning Theory

Vladimir N. Vapnik (Consultant)

$418.95

Hardback

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