The Nature of Statistical Learning Theory

av Vladimir Vapnik. Inbunden, 1999

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 (inbunden)
  • Inbunden (Hardback)
  • Språk: Engelska
  • Antal sidor: 314
  • Utg.datum: 1999-11-01
  • Upplaga: 2nd ed. 2000
  • Förlag: Springer-Verlag New York Inc.
  • Illustratör/Fotograf: 50 Abb
  • Illustrationer: biography
  • Dimensioner: 242 x 162 x 25 mm
  • Vikt: 600 g
  • Antal komponenter: 1
  • Komponenter: 52:B&W 6.14 x 9.21in or 234 x 156mm (Royal 8vo) Case Laminate on White w/Gloss Lam
  • ISBN: 9780387987804

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Recensioner i media

From the reviews of the second edition: ZENTRALBLATT MATH "...written in a concise style. It must be recommended to scientists of statistics, mathematics, physics, and computer science." SHORT BOOK REVIEWS "This interesting book helps a reader to understand the interconnections between various streams in the empirical modeling realm and may be recommended to any reader who feels lost in modern terminology, such as artificial intelligence, neural networks, machine learning etcetera." "The book by Vapnik focuses on how to estimate a function of parameters from empirical data ... . The book is concisely written and is intended to be useful to statisticians, computer scientists, mathematicians, and physicists. ... This book is very well written at a very high level of abstract thinking and comprehension. The references are up-to-date." (Ramalingam Shanmugam, Journal of Statistical Computation and Simulation, Vol. 75 (2), February, 2005) "The aim of the book is to introduce a wide range of readers to the fundamental ideas of statistical learning theory. ... Each chapter is supplemented by 'Reasoning and Comments' which describe the relations between classical research in mathematical statistics and research in learning theory. ... The book is well suited to promote the ideas of statistical learning theory and can be warmly recommended to all who are interested in computer learning problems." (S. Vogel, Metrika, June, 2002)

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Innehållsförteckning

Informal Reasoning and Comments * Consistency of Learning Processes * Bounds on the Rate of Convergence of Learing Processes * Controlling the Generalization Ability of Learning Processes * Methods of Pattern Recognition * Methods of Function Estimation * Direct Methods in Statistical Learning Theory * The Vicinal Risk Minimization Principle and the SVMs