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The Nature of Statistical Learning Theory

Vladimir Vapnik

16 valoraciones por Goodreads
ISBN 10: 0387987800 / ISBN 13: 9780387987804
Editorial: Springer
Nuevos Condición: New Encuadernación de tapa dura
Librería: BuySomeBooks (Las Vegas, NV, Estados Unidos de America)

Librería en AbeBooks desde: 21 de mayo de 2012

Cantidad: > 20

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Descripción

Hardcover. 314 pages. Dimensions: 9.3in. x 6.4in. x 0.9in.The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. N° de ref. de la librería 9780387987804

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Detalles bibliográficos

Título: The Nature of Statistical Learning Theory

Editorial: Springer

Encuadernación: Hardcover

Condición del libro:New

Tipo de libro: Hardcover

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Sinopsis:

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.

Review:

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)

"Sobre este título" puede pertenecer a otra edición de este libro.

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Orders usually ship within 1-2 business days. Books are shipped from multiple locations so your order may arrive from Las Vegas,NV, Roseburg,OR, La Vergne,TN, Momence,IL, or Commerce,GA.

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