Entropy Measures for Data Analysis : Theory, Algorithms and Applications
Language: English
Published by MDPI AG, 2019
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
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Add to basketItem description from seller
nach der Bestellung gedruckt Neuware - Printed after ordering - 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.
Seller Inventory # 9783039280322
- Title
- Entropy Measures for Data Analysis : Theory, Algorithms and Applications
- Author
- Karsten Keller
- Publisher
- MDPI AG
- Publication year
- 2019
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 3039280325
- ISBN 13
- 9783039280322
- Item weight
- 671 grams
- Dimensions
- 244x170x17 mm
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.
"Synopsis" may belong to another edition of this title.
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