An Introduction to Information Theoretic Learning, Part I: Foundations

Authors

  • Daniel Silva University of Brasília
  • Denis Fantinato University of Campinas
  • Janio Canuto Federal University of Sergipe
  • Leonardo Duarte University of Campinas
  • Aline Neves Federal University of ABC
  • Ricardo Suyama Federal University of ABC
  • Jugurta Montalvão Federal University of Sergipe
  • Romis Attux University of Campinas

DOI:

https://doi.org/10.14209/jcis.2016.6

Keywords:

ITL, information theory, entropy, Rényi

Abstract

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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Published

2016-04-08

How to Cite

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). https://doi.org/10.14209/jcis.2016.6

Issue

Vol. 31 No. 1 (2016)

Section

Tutorial Papers

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Received 2015-02-20
Accepted 2016-03-30
Published 2016-04-08

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