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On the consistency of Multithreshold Entropy Linear Classifier
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.Comment: Presented at Theoretical Foundations of Machine Learning 2015
(http://tfml.gmum.net), final version published in Schedae Informaticae
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