1.

Record Nr.

UNINA9910367736303321

Autore

Keller Karsten

Titolo

Entropy Measures for Data Analysis: Theory, Algorithms and Applications

Pubbl/distr/stampa

MDPI - Multidisciplinary Digital Publishing Institute, 2019

ISBN

3-03928-033-3

Descrizione fisica

1 online resource (260 p.)

Soggetti

History of engineering and technology

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Sommario/riassunto

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.