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Statistical Learning Theory
Vladimir N. Vapnik
€ 252.34
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Description for Statistical Learning Theory
Hardcover. This book is devoted to the statistical theory of learning and generalization, that is, the problem of choosing the desired function on the basis of empirical data. The author will present the whole picture of learning and generalization theory. Learning theory has applications in many fields, such as psychology, education and computer science. Series: Adaptive and Learning Systems for Signal Processing, Communications and Control Series. Num Pages: 768 pages, Illustrations. BIC Classification: PBKF; UYQM. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 163 x 235 x 41. Weight in Grams: 1214.
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.
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.
Product Details
Publisher
John Wiley and Sons Ltd United States
Number of pages
768
Format
Hardback
Publication date
1998
Series
Adaptive and Learning Systems for Signal Processing, Communications and Control Series
Condition
New
Number of Pages
768
Place of Publication
, United States
ISBN
9780471030034
SKU
V9780471030034
Shipping Time
Usually ships in 7 to 11 working days
Ref
99-50
About Vladimir N. Vapnik
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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