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Handbook of HydroInformatics: Volume II: Advanced Machine Learning Techniques Saeid Eslamian
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Handbook of HydroInformatics: Volume II: Advanced Machine Learning Techniques
Saeid Eslamian
Advanced Machine Learning Techniques, Volume Two in the Handbook of HydroInformatics series, presents both the art of designing good learning algorithms, as well as the science of analyzing an algorithm's computational and statistical properties and performance guarantees. Global contributors cover theoretical foundation topics such as computational and statistical convergence rates, minimax estimation and concentration of measure. Advanced machine learning methods such as nonparametric density estimation, nonparametric regression, and Bayesian estimation, as well as advanced frameworks such as privacy, causality and stochastic learning algorithms are also included.
Other methods covered include Cloud and Cluster Computing, Data Fusion Techniques, Empirical Orthogonal Functions and Teleconnection, Internet of Things, Kernel-Based Modeling, Large Eddy Simulation, Patter Recognition, Uncertainty-Based Resiliency Evaluation, and Volume-Based Inverse Mode, making this word an interdisciplinary guide that will appeal to post graduates interested in Computer Science, Artificial Intelligence, Mathematical Science, Applied Science, Earth and Geoscience, Geography, Civil Engineering, Engineering, Water Science, Atmospheric Science, Social Science, Environment Science, Natural Resources and Chemical Engineering.
450 pages, Approx. 100 illustrations (20 in full color); Illustrations, unspecified
| メディア | 書籍 Paperback Book (ソフトカバーで背表紙を接着した本) |
| リリース済み | 2022年12月9日 |
| ISBN13 | 9780128219614 |
| 出版社 | Elsevier Science Publishing Co Inc |
| ページ数 | 418 |
| 寸法 | 150 × 220 × 10 mm · 966 g |
| 言語 | 英語 |
| 編集者 | Eslamian, Faezeh, Ph.D., CEP, (CEP, Project Manager at GHD, Quebec, Canada) |
| 編集者 | Eslamian, Saeid (Distinguished Full Professor, Disaster Relief: Resilient and Sustainable Water Resources, Isfahan University of Technology, Iran) |
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