@article{Silva_Fantinato_Canuto_Duarte_Neves_Suyama_Montalvão_Attux_2016, title={An Introduction to Information Theoretic Learning, Part I: Foundations}, volume={31}, url={https://jcis.sbrt.org.br/jcis/article/view/94}, DOI={10.14209/jcis.2016.6}, abstractNote={<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>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. </span></p></div></div></div><p> </p></div></div></div>}, number={1}, journal={Journal of Communication and Information Systems}, author={Silva, Daniel and Fantinato, Denis and Canuto, Janio and Duarte, Leonardo and Neves, Aline and Suyama, Ricardo and Montalvão, Jugurta and Attux, Romis}, year={2016}, month={Apr.} }