Generalization performance of multi-class discriminant models
Identifieur interne : 006010 ( Hal/Checkpoint ); précédent : 006009; suivant : 006011Generalization performance of multi-class discriminant models
Auteurs : Hélène Paugam-Moisy ; André Elisseeff ; Yann Guermeur [France]Source :
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Abstract
Starting from a direct definition of the notion of margin in the multiclass case , we study the generalization performance of multiclass discriminant systems. In the framework of statistical learning theory, we establish on this performance a bound based on covering numbers. An application to a linear ensemble method w hich estimates the class posterior probabilities provides us with a way to compa re this bound and another one based on combinatorial dimensions, with respect to the capacity measure they incorporate. Experimental results highlight their use fulness for a real-world problem.
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In the framework of statistical learning theory, we establish on this performance a bound based on covering numbers. An application to a linear ensemble method w hich estimates the class posterior probabilities provides us with a way to compa re this bound and another one based on combinatorial dimensions, with respect to the capacity measure they incorporate. Experimental results highlight their use fulness for a real-world problem.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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