The nature of statistical learning theory
Author(s)
Bibliographic Information
The nature of statistical learning theory
(Statistics for engineering and information science)
Springer, c2010
2nd ed
- : [pbk]
Available at 1 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
  Kanagawa
  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
  Mie
  Shiga
  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
  China
  Thailand
  United Kingdom
  Germany
  Switzerland
  France
  Belgium
  Netherlands
  Sweden
  Norway
  United States of America
Note
Softcover version of original hardcover edition 2000
Includes bibliographical references (p. [301]-309) and index
"With 50 illustrations"
Description and Table of Contents
Description
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.
Table of Contents
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
by "Nielsen BookData"