Overview
- 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.
Part of the book series: Information Science and Statistics (ISS)
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Keywords
Table of contents (10 chapters)
Reviews
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)
Authors and Affiliations
Bibliographic Information
Book Title: The Nature of Statistical Learning Theory
Authors: Vladimir N. Vapnik
Series Title: Information Science and Statistics
DOI: https://doi.org/10.1007/978-1-4757-3264-1
Publisher: Springer New York, NY
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eBook Packages: Springer Book Archive
Copyright Information: Springer Science+Business Media New York 2000
Hardcover ISBN: 978-0-387-98780-4Published: 19 November 1999
Softcover ISBN: 978-1-4419-3160-3Published: 01 December 2010
eBook ISBN: 978-1-4757-3264-1Published: 29 June 2013
Series ISSN: 1613-9011
Series E-ISSN: 2197-4128
Edition Number: 2
Number of Pages: XX, 314
Additional Information: Originally published as a monograph
Topics: Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Artificial Intelligence