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arXiv · 1504.04740

On the consistency of Multithreshold Entropy Linear Classifier

Wojciech Marian Czarnecki
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Abstract

Multithreshold Entropy Linear Classifier (MELC) is a recent classifier idea which employs information theoretic concept in order to create a multithreshold maximum margin model. In this paper we analyze its consistency over multithreshold linear models and show that its objective function upper bounds the amount of misclassified points in a similar manner like hinge loss does in support vector machines. For further confirmation we also conduct some numerical experiments on five datasets.

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Wojciech Marian Czarnecki. 2015-04-18. On the consistency of Multithreshold Entropy Linear Classifier. https://doi.org/10.4467/20838476si.15.012.3034

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