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  Advances in Large Margin Classifiers

Smola, A., Bartlett, P., Schölkopf, B., & Schuurmans, D. (2000). Advances in Large Margin Classifiers. Cambridge, MA, USA: MIT Press.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-E42E-E Version Permalink: http://hdl.handle.net/21.11116/0000-0005-B6C5-F
Genre: Book

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https://ieeexplore.ieee.org/book/6267437 (Publisher version)
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 Creators:
Smola, AJ, Author              
Bartlett, PJ, Author
Schölkopf, B1, Author              
Schuurmans, D, Author
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1External Organizations, ou_persistent22              

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 Abstract: 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.

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 Dates: 2000-10
 Publication Status: Published in print
 Pages: 412
 Publishing info: Cambridge, MA, USA : MIT Press
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 974
ISBN: 0-262-19448-1
 Degree: -

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Title: Advances in neural information processing systems
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