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Entropy 2019, 21(2), 166; https://doi.org/10.3390/e21020166

Learning Entropy as a Learning-Based Information Concept

1
Department of Mechanics, Biomechanics, and Mechatronics, Research Centre for Low-Carbon Energy Technologies, Faculty of Mechanical Engineering, Czech Technical University, Prague, Czech Republic
2
Department of Electrical and Computer Engineering, University of Manitoba, Canada
3
Dpt. of Radiological Imaging and Informatics, Tohoku Univ. Grad. School of Medicine, Intelligent Biomed. Sys. Eng. Lab., Grad. School of Biomed. Eng., Tohoku University, Sendai 980-8575, Japan
*
Author to whom correspondence should be addressed.
Received: 30 December 2018 / Revised: 28 January 2019 / Accepted: 5 February 2019 / Published: 11 February 2019
PDF [1662 KB, uploaded 11 February 2019]

Abstract

Recently, a novel concept of a non-probabilistic novelty detection measure, based on a multi-scale quantification of unusually large learning efforts of machine learning systems, was introduced as learning entropy (LE). The key finding with LE is that the learning effort of learning systems is quantifiable as a novelty measure for each individually observed data point of otherwise complex dynamic systems, while the model accuracy is not a necessary requirement for novelty detection. This brief paper extends the explanation of LE from the point of an informatics approach towards a cognitive (learning-based) information measure emphasizing the distinction from Shannon’s concept of probabilistic information. Fundamental derivations of learning entropy and of its practical estimations are recalled and further extended. The potentials, limitations, and, thus, the current challenges of LE are discussed.
Keywords: learning; information; novelty detection; non-probabilistic entropy; learning systems learning; information; novelty detection; non-probabilistic entropy; learning systems
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Bukovsky, I.; Kinsner, W.; Homma, N. Learning Entropy as a Learning-Based Information Concept. Entropy 2019, 21, 166.

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