Nature of Statistical Learning Theory

Vapnik, Vladimir Naumovich

ISBN 10: 1441931600 / ISBN 13: 9781441931603

Editore: Springer, 2010

Riguardo questo articolo

Codice articolo 14401940-n

Dati bibliografici

• Titolo: Nature of Statistical Learning Theory
• Editore: Springer
• Data di pubblicazione: 2010
• Lingua: Inglese
• ISBN 10: 1441931600
• ISBN 13: 9781441931603
• Rilegatura: Brossura
• Edizione: seconda edizione
• Condizione: New

Descrizione articolo

Riassunto

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. Written in readable and concise style and devoted to key learning problems, the book is intended for statisticians, mathematicians, physicists, and computer scientists.

Recensione

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)