On the Consistency of Multithreshold Entropy Linear Classifier
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Publication date: 11.04.2016
Schedae Informaticae, 2015, Volume 24, pp. 123 - 132
https://doi.org/10.4467/20838476SI.15.012.3034Authors
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.
Information: Schedae Informaticae, 2015, pp. 123 - 132
Article type: Original article
Titles:
On the Consistency of Multithreshold Entropy Linear Classifier
On the Consistency of Multithreshold Entropy Linear Classifier
Department of Mathematics Faculty of Mathematics and Computer Science Jagiellonian University, ul. Łojasiewicza 6, 30-348 Kraków, Poland
Published at: 11.04.2016
Article status: Öffnen Sie
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EnglischView count: 2249
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