Statistical learning theory
By: Vladimir N. Vapnik.
Series: Adaptive and learning systems for signal processing, communications, and control
Publisher: Wiley
Published: 1998
Place of publication: New York
Language: eng
Number of pages: 736
Subjects: Computational learning theory.
Description: A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis of empirical data. Highly applicable to a variety of computer science and robotics fields, this book offers lucid coverage of the theory as a whole. Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
Notes: Includes bibliographical references (p. 723-732) and index.
"A Wiley-Interscience publication."