Fisher Consistency of Multicategory Support Vector Machines

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Yufeng Liu ;
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, PMLR 2:291-298, 2007.

Abstract

The Support Vector Machine (SVM) has become one of the most popular machine learning techniques in recent years. The success of the SVM is mostly due to its elegant margin concept and theory in binary classification. Generalization to the multicategory setting, however, is not trivial. There are a number of different multicategory extensions of the SVM in the literature. In this paper, we review several commonly used extensions and Fisher consistency of these extensions. For inconsistent extensions, we propose two approaches to make them Fisher consistent, one is to add bounded constraints and the other is to truncate unbounded hinge losses.

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