Reprint

Entropy Measures for Data Analysis

Theory, Algorithms and Applications

Edited by
December 2019
260 pages
  • ISBN 978-3-03928-032-2 (Paperback)
  • ISBN 978-3-03928-033-9 (PDF)

This is a Reprint of the Special Issue Entropy Measures for Data Analysis: Theory, Algorithms and Applications that was published in

Chemistry & Materials Science
Computer Science & Mathematics
Physical Sciences
Summary

Entropies and entropy-like quantities play an increasing role in modern non-linear data analysis. Fields that benefit from this application range from biosignal analysis to econophysics and engineering. This issue is a collection of papers touching on different aspects of entropy measures in data analysis, as well as theoretical and computational analyses.

The relevant topics include the difficulty to achieve adequate application of entropy measures and the acceptable parameter choices for those entropy measures, entropy-based coupling, and similarity analysis, along with the utilization of entropy measures as features in automatic learning and classification. Various real data applications are given.

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