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On the consistency of Multithreshold Entropy Linear Classifier (1504.04740v1)
Published 18 Apr 2015 in cs.LG and stat.ML
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.