New multicategory boosting algorithms based on multicategory Fisher-consistent losses

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Fisher-consistent loss functions play a fundamental role in the construction of successful binary margin-based classifiers. In this paper we establish the Fisher-consistency condition for multicategory classification problems. Our approach uses the margin vector concept which can be regarded as a multicategory generalization of the binary margin. We characterize a wide class of smooth convex loss functions that are Fisher-consistent for multicategory classification. We then consider using the margin-vector-based loss functions to derive multicategory boosting algorithms. In particular, we derive two new multicategory boosting algorithms by using the exponential and logistic regression losses.
Published in at http://dx.doi.org/10.1214/08-AOAS198 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

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http://salesiana.dossiersoluciones.com/handle/123456789/217467

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/0901.3988
 http://arxiv.org/abs/0901.3988
 Annals of Applied Statistics 2008, Vol. 2, No. 4, 1290-1306
 doi:10.1214/08-AOAS198

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arXiv