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On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory
Hardcover
Series: Springer Theses
Technology & EngineeringGeneral ComputersGeneral Science
ISBN13: 9783030952303
Publisher: Springer
Published: Mar 13 2022
Pages: 158
Weight: 0.94
Height: 0.44 Width: 6.14 Depth: 9.21
Language: English
Introduction.- Study Area and Data Sets.- Advanced Exploratory Data Analysis.- Fisher-Shannon Analysis.- Spatio-Temporal Prediction with Machine Learning.- Uncertainty Quantification with Extreme Learning Machine.- Spatio-Temporal Modelling using Extreme Learning Machine.- Conclusions, Perspectives and Recommendations.
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On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory
Guignard, Fabian
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Guignard, Fabian
On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory
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