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Entropy 2013, 15(10), 4159-4187; doi:10.3390/e15104159

Learning Entropy: Multiscale Measure for Incremental Learning

Czech Technical University in Prague, Technicka 4, 166 07, Prague 6, Czech Republic
Received: 26 July 2013 / Revised: 17 September 2013 / Accepted: 22 September 2013 / Published: 27 September 2013
(This article belongs to the Special Issue Dynamical Systems)
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Abstract

First, this paper recalls a recently introduced method of adaptive monitoring of dynamical systems and presents the most recent extension with a multiscale-enhanced approach. Then, it is shown that this concept of real-time data monitoring establishes a novel non-Shannon and non-probabilistic concept of novelty quantification, i.e., Entropy of Learning, or in short the Learning Entropy. This novel cognitive measure can be used for evaluation of each newly measured sample of data, or even of whole intervals. The Learning Entropy is quantified in respect to the inconsistency of data to the temporary governing law of system behavior that is incrementally learned by adaptive models such as linear or polynomial adaptive filters or neural networks. The paper presents this novel concept on the example of gradient descent learning technique with normalized learning rate. View Full-Text
Keywords: incremental learning; adaptation plot; multiscale; learning entropy; individual sample learning entropy; approximate learning entropy; order of learning entropy; learning entropy of a model; non-Shannon entropy; novelty detection; chaos; time series; HRV; ECG incremental learning; adaptation plot; multiscale; learning entropy; individual sample learning entropy; approximate learning entropy; order of learning entropy; learning entropy of a model; non-Shannon entropy; novelty detection; chaos; time series; HRV; ECG
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Bukovsky, I. Learning Entropy: Multiscale Measure for Incremental Learning. Entropy 2013, 15, 4159-4187.

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