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Open AccessConcept Paper

Introduction to Extreme Seeking Entropy

by Jan Vrba 1,* and Jan Mareš 1,2,*
1
Department of Computing and Control Engineering, Faculty of Chemical Engineering, University of Chemistry and Technology, 166 28 Prague, Czech Republic
2
Department of Process Control, Faculty of Electrical Engineering and Informatics, University of Pardubice, 530 02 Pardubice, Czech Republic
*
Authors to whom correspondence should be addressed.
Entropy 2020, 22(1), 93; https://doi.org/10.3390/e22010093
Received: 9 December 2019 / Revised: 31 December 2019 / Accepted: 8 January 2020 / Published: 12 January 2020
Recently, the concept of evaluating an unusually large learning effort of an adaptive system to detect novelties in the observed data was introduced. The present paper introduces a new measure of the learning effort of an adaptive system. The proposed method also uses adaptable parameters. Instead of a multi-scale enhanced approach, the generalized Pareto distribution is employed to estimate the probability of unusual updates, as well as for detecting novelties. This measure was successfully tested in various scenarios with (i) synthetic data, (ii) real time series datasets, and multiple adaptive filters and learning algorithms. The results of these experiments are presented.
Keywords: novelty detection; learning system; learning; time series; learning entropy; extreme seeking entropy novelty detection; learning system; learning; time series; learning entropy; extreme seeking entropy
MDPI and ACS Style

Vrba, J.; Mareš, J. Introduction to Extreme Seeking Entropy. Entropy 2020, 22, 93.

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