Ensemble Methods of Appropriate Capacity for Multi-Class Support Vector Machines
Identifieur interne : 001F81 ( Hal/Corpus ); précédent : 001F80; suivant : 001F82Ensemble Methods of Appropriate Capacity for Multi-Class Support Vector Machines
Auteurs : Yann GuermeurSource :
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Abstract
Roughly speaking, there is one single model of pattern recognition support vector machine (SVM), with variants of lower popularity. On the contrary, among the different multi-class SVMs (M-SVMs) published, none is clearly favoured. Although several comparative studies between M-SVMs and decomposition methods have been reported, no attention had been paid so far to the combination of those models. We investigate the combination of M-SVMs with low capacity linear ensemble methods that estimate the class posterior probabilities.
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<front><div type="abstract" xml:lang="en">Roughly speaking, there is one single model of pattern recognition support vector machine (SVM), with variants of lower popularity. On the contrary, among the different multi-class SVMs (M-SVMs) published, none is clearly favoured. Although several comparative studies between M-SVMs and decomposition methods have been reported, no attention had been paid so far to the combination of those models. We investigate the combination of M-SVMs with low capacity linear ensemble methods that estimate the class posterior probabilities.</div>
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