Statistical Learning Theory

Vladimir N. Vapnik

ISBN 10: 0471030031 / ISBN 13: 9780471030034

Published by John Wiley and Sons Inc, US, 1998

About this Item

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.

Seller Inventory # LU-9780471030034

Bibliographic Details

• Title: Statistical Learning Theory
• Publisher: John Wiley and Sons Inc, US
• Publication Date: 1998
• Language: English
• ISBN 10: 0471030031
• ISBN 13: 9780471030034
• Binding: Hardback
• Condition: New
• Weight: 1168 grams

About this title

Synopsis

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.

About the Author

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.