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Learning Entropy: Multiscale Measure for Incremental Learning
Czech Technical University in Prague, Technicka 4, 166 07, Prague 6, Czech Republic
Received: 26 July 2013; in revised form: 17 September 2013 / Accepted: 22 September 2013 / Published: 27 September 2013
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.
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
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Bukovsky, I. Learning Entropy: Multiscale Measure for Incremental Learning. Entropy 2013, 15, 4159-4187.
Bukovsky I. Learning Entropy: Multiscale Measure for Incremental Learning. Entropy. 2013; 15(10):4159-4187.
Bukovsky, Ivo. 2013. "Learning Entropy: Multiscale Measure for Incremental Learning." Entropy 15, no. 10: 4159-4187.