Entropy Measures for Data Analysis: Theory, Algorithms and Applications
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 an...
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| Format: | Book Chapter |
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MDPI - Multidisciplinary Digital Publishing Institute
2019
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| Online Access: | Get Fullteks DOAB: description of the publication |
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| LEADER | 04490naaaa2201141uu 4500 | ||
|---|---|---|---|
| 001 | doab_20_500_12854_46559 | ||
| 005 | 20210211 | ||
| 020 | |a books978-3-03928-033-9 | ||
| 020 | |a 9783039280322 | ||
| 020 | |a 9783039280339 | ||
| 024 | 7 | |a 10.3390/books978-3-03928-033-9 |c doi | |
| 041 | 0 | |a English | |
| 042 | |a dc | ||
| 100 | 1 | |a Keller, Karsten |4 auth | |
| 245 | 1 | 0 | |a Entropy Measures for Data Analysis: Theory, Algorithms and Applications |
| 260 | |b MDPI - Multidisciplinary Digital Publishing Institute |c 2019 | ||
| 300 | |a 1 electronic resource (260 p.) | ||
| 506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
| 520 | |a 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. | ||
| 540 | |a Creative Commons |f https://creativecommons.org/licenses/by-nc-nd/4.0/ |2 cc |4 https://creativecommons.org/licenses/by-nc-nd/4.0/ | ||
| 546 | |a English | ||
| 653 | |a fault diagnosis | ||
| 653 | |a empirical mode decomposition | ||
| 653 | |a auditory attention | ||
| 653 | |a Dempster-Shafer evidence theory | ||
| 653 | |a simulation | ||
| 653 | |a uncertainty of basic probability assignment | ||
| 653 | |a center of pressure displacement | ||
| 653 | |a particle size distribution | ||
| 653 | |a multivariate analysis | ||
| 653 | |a symbolic analysis | ||
| 653 | |a permutation entropy | ||
| 653 | |a short time records | ||
| 653 | |a co-evolution | ||
| 653 | |a plausibility transformation | ||
| 653 | |a experiment of design | ||
| 653 | |a cross-entropy method | ||
| 653 | |a weighted Hartley entropy | ||
| 653 | |a firefly algorithm | ||
| 653 | |a embedded dimension | ||
| 653 | |a entropy measure | ||
| 653 | |a effective transfer entropy | ||
| 653 | |a treadmill walking | ||
| 653 | |a ordinal patterns | ||
| 653 | |a complex fuzzy set | ||
| 653 | |a entropy visualization | ||
| 653 | |a belief entropy | ||
| 653 | |a signal classification | ||
| 653 | |a machine learning evaluation | ||
| 653 | |a novelty detection | ||
| 653 | |a selfsimilar measure | ||
| 653 | |a Permutation entropy | ||
| 653 | |a automatic learning | ||
| 653 | |a cross wavelet transform | ||
| 653 | |a cross-visibility graphs | ||
| 653 | |a Kolmogorov-Sinai entropy | ||
| 653 | |a distance | ||
| 653 | |a Shannon-type relations | ||
| 653 | |a Tsallis entropy | ||
| 653 | |a market crash | ||
| 653 | |a support vector machine (SVM) | ||
| 653 | |a conditional entropy of ordinal patterns | ||
| 653 | |a sample entropy | ||
| 653 | |a learning | ||
| 653 | |a electroencephalography (EEG) | ||
| 653 | |a meta-heuristic | ||
| 653 | |a entropy | ||
| 653 | |a data transformation | ||
| 653 | |a information entropy | ||
| 653 | |a signal analysis | ||
| 653 | |a synchronization analysis | ||
| 653 | |a similarity indices | ||
| 653 | |a data analysis | ||
| 653 | |a geodesic distance | ||
| 653 | |a auditory attention classifier | ||
| 653 | |a entropy measures | ||
| 653 | |a distance induced vague entropy | ||
| 653 | |a analog circuit | ||
| 653 | |a vague entropy | ||
| 653 | |a complex vague soft set | ||
| 653 | |a entropy balance equation | ||
| 653 | |a parametric t-distributed stochastic neighbor embedding | ||
| 653 | |a global optimization | ||
| 653 | |a learning systems | ||
| 653 | |a image entropy | ||
| 653 | |a algorithmic complexity | ||
| 653 | |a support vector machine | ||
| 653 | |a system coupling | ||
| 653 | |a relevance analysis | ||
| 653 | |a Chinese stock sectors | ||
| 653 | |a Shannon entropy | ||
| 653 | |a linear discriminant analysis (LDA) | ||
| 653 | |a information | ||
| 653 | |a information transfer | ||
| 653 | |a dual-tasking | ||
| 653 | |a non-probabilistic entropy | ||
| 856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/1906 |7 0 |z Get Fullteks |
| 856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/46559 |7 0 |z DOAB: description of the publication |