An Introduction to Information Theoretic Learning, Part I: Foundations

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Daniel Silva
Denis Fantinato
Janio Canuto
Leonardo Duarte
Aline Neves
Ricardo Suyama
Jugurta Montalvão
Romis Attux


With the increasing number of machine learning problems that are out of the linear and Gaussian paradigm, information theoretic learning (ITL) rises as a research field that proposes a modeling method with a wealthier statistical treatment of the adaptation criterion. In the first part of this tutorial, we introduce the main concepts of ITL and a key set of estimators that enable the implementation of algorithms, in the context of a wider view independent of the differentiability property.


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Silva, D., Fantinato, D., Canuto, J., Duarte, L., Neves, A., Suyama, R., … Attux, R. (2016). An Introduction to Information Theoretic Learning, Part I: Foundations. Journal of Communication and Information Systems, 31(1).
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