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Advances in large margin classifiers

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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.OCLC-licensed vendor bibliographic record.

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Series Title

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Call Number

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Publisher

Cambridge, Mass. : : MIT Press,., 2000.

Collation

1 online resource (vi, 412 pages) :illustrations.

Language

English

ISBN/ISSN

9780262283977

Classification

NONE

Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

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Subject(s)

Machine learning.
Algorithms.
Kernel functions.

Specific Detail Info

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Statement of Responsibility

edited by Alexander J. Smola [and others].

Other Information

Cataloger

dianna Puji

Source

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Validator

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Digital Object Identifier (DOI)

https://direct.mit.edu/books/edited-volume/2787/Advances-in-Large-Margin-Classifiers

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