An Uncertainty Framework for Classification
Machine Learning
2013-01-18 v1 Machine Learning
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.
Cite
@article{arxiv.1301.3896,
title = {An Uncertainty Framework for Classification},
author = {Loo-Nin Teow and Kia-Fock Loe},
journal= {arXiv preprint arXiv:1301.3896},
year = {2013}
}
Comments
Appears in Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence (UAI2000)