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Entropy 2017, 19(11), 612; https://doi.org/10.3390/e19110612

An Analysis of Information Dynamic Behavior Using Autoregressive Models

1
Center of Exact and Natural Sciences, Federal Rural University of the Semi-Arid Region, Mossoro 59625-900, RN, Brazil
2
Department of Automation and Computer Engineering, Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil
3
Department of Electrical Engineering, Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil
*
Author to whom correspondence should be addressed.
Received: 20 September 2017 / Revised: 8 November 2017 / Accepted: 10 November 2017 / Published: 18 November 2017
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Abstract

Information Theory is a branch of mathematics, more specifically probability theory, that studies information quantification. Recently, several researches have been successful with the use of Information Theoretic Learning (ITL) as a new technique of unsupervised learning. In these works, information measures are used as criterion of optimality in learning. In this article, we will analyze a still unexplored aspect of these information measures, their dynamic behavior. Autoregressive models (linear and non-linear) will be used to represent the dynamics in information measures. As a source of dynamic information, videos with different characteristics like fading, monotonous sequences, etc., will be used. View Full-Text
Keywords: information theory; dynamics process; information potential information theory; dynamics process; information potential
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Oliveira, A.; Dória Neto, A.D.; Martins, A. An Analysis of Information Dynamic Behavior Using Autoregressive Models. Entropy 2017, 19, 612.

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