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Item Details
Title: ADVANCES IN LARGE-MARGIN CLASSIFIERS
By: Alexander J. Smola (Editor), Peter Bartlett (Editor), Bernhard Scholkopf (Editor)
Format: Hardback

List price: £7.99


We currently do not stock this item, please contact the publisher directly for further information.

ISBN 10: 0262194481
ISBN 13: 9780262194488
Publisher: MIT PRESS LTD
Pub. date: 29 September, 2000
Series: Neural Information Processing series
Pages: 422
Description: The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research.
Synopsis: The concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural networks, and support vector machines. The fact that it is the margin, or confidence level, of a classification--that is, a scale parameter--rather than a raw training error that matters has become a key tool for dealing with classifiers. This book shows how this idea applies to both the theoretical analysis and the design of algorithms.The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. Among the contributors are Manfred Opper, Vladimir Vapnik, and Grace Wahba.
Reader Age: From 18 years
Publication: US
Imprint: Bradford Books
Returns: Returnable
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