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Abstracts
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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Dates
published
2015
online
06 - 07 - 2016
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YADDA identifier
bwmeta1.element.ojs-issn-2083-8476-year-2015-volume-24-article-6340