Advances in large margin classifiers / edited by Alexander J. Smola [and others].

Format:

Book

Contributor:

Smola, Alexander J.

Series:

Neural information processing series.

Advances in neural information processing systems [i.e. Neural information processing series]

Language:

English

Subjects (All):

Machine learning.

Algorithms.

Kernel functions.

Physical Description:

1 PDF (vi, 412 pages) : illustrations.

Place of Publication:

Cambridge, Mass. : MIT Press, ©2000.

Language Note:

English

Summary:

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.

Notes:

Bibliographic Level Mode of Issuance: Monograph

Includes bibliographical references (pages [389]-407) and index.

OCLC-licensed vendor bibliographic record.

ISBN:

0-262-29240-8

0-262-28397-2

1-4237-2954-4

OCLC:

62075949