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  A comparative study of multi-class support vector machines in the unifying framework of large margin classifiers

Guermeur, J., Elisseeff, A., & Zelus, D. (2005). A comparative study of multi-class support vector machines in the unifying framework of large margin classifiers. Applied Stochastic Models in Business and Industry, 21(2), 199-214. doi:10.1002/asmb.534.

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Guermeur, J, Author
Elisseeff, A1, 2, Author              
Zelus, D, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Vapnik's statistical learning theory has mainly been developed for two types of problems: pattern recognition (computation of dichotomies) and regression (estimation of real‐valued functions). Only in recent years has multi‐class discriminant analysis been studied independently. Extending several standard results, among which a famous theorem by Bartlett, we have derived distribution‐free uniform strong laws of large numbers devoted to multi‐class large margin discriminant models. The capacity measure appearing in the confidence interval, a covering number, has been bounded from above in terms of a new generalized VC dimension. In this paper, the aforementioned theorems are applied to the architecture shared by all the multi‐class SVMs proposed so far, which provides us with a simple theoretical framework to study them, compare their performance and design new machines.

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 Dates: 2005-04
 Publication Status: Published in print
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 Rev. Type: -
 Identifiers: DOI: 10.1002/asmb.534
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Title: Applied Stochastic Models in Business and Industry
Source Genre: Journal
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Publ. Info: New York, NY : John Wiley & Sons
Pages: - Volume / Issue: 21 (2) Sequence Number: - Start / End Page: 199 - 214 Identifier: ISSN: 1524-1904
CoNE: https://pure.mpg.de/cone/journals/resource/954928624317