Special Issue "Entropy Measures for Data Analysis: Theory, Algorithms and Applications"
A special issue of Entropy (ISSN 1099-4300).
Deadline for manuscript submissions: 14 December 2018
Prof. Dr. Karsten Keller
Institut für Mathematik, Universität zu Lübeck, D-23562 Lübeck, Germany
Phone: +49 451 500 3165
Interests: data analysis; time series analysis; computational statistics; information theory; ergodic theory; automatic learning
Entropies and entropy-like quantities are playing an increasing role in modern non-linear data analysis. Fields of their application reach from diagnostics in physiology, for instance, electroencephalography (EEG), magnetoencephalography (MEG) and electrocardiography (ECG), to econophysics and engineering. During the last few years, classical concepts as the Approximate entropy and the Sample entropy have been supplemented by new entropy measures, like the Permutation entropy and various variants of it. Recent developments are focused on multidimensional generalizations of the concepts with a special emphasize on the quantification of coupling between time series and system components behind them. Some of the main future challenges in the field are a better understanding of the nature of the various entropy measures and their relationships, with the aim of their adequate application including good parameter choices. The utilization of entropy measures as features in automatic learning and their application to large and complex data for tasks as classification, discrimination and finding structural changes requires fast and well-founded algorithms.Prof. Dr. Karsten Keller
Manuscript Submission Information
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- data analysis
- complexity measures
- approximate entropy
- sample entropy
- permutation entropy
- automatic learning