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arXiv · 1301.3896

An Uncertainty Framework for Classification

Loo-Nin Teow·Kia-Fock Loe
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Abstract

We define a generalized likelihood function based on uncertainty measures and show that maximizing such a likelihood function for different measures induces different types of classifiers. In the probabilistic framework, we obtain classifiers that optimize the cross-entropy function. In the possibilistic framework, we obtain classifiers that maximize the interclass margin. Furthermore, we show that the support vector machine is a sub-class of these maximum-margin classifiers.

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Loo-Nin Teow, Kia-Fock Loe. 2013-01-16. An Uncertainty Framework for Classification. https://arxiv.org/abs/1301.3896

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