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Machine learning methods in the environmental sciences : neural networks and kernels /
Machine learning methods originated from artificial intelligence and are now used in various fields in environmental sciences today. This is the first single-authored textbook providing a unified treatment of machine learning methods and their applications in the environmental sciences. Due to their...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cambridge, UK ; New York :
Cambridge University Press,
2009.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Hsieh, William Wei, |d 1955- | |
| 245 | 1 | 0 | |a Machine learning methods in the environmental sciences : |b neural networks and kernels / |c William W. Hsieh. |
| 260 | |a Cambridge, UK ; |a New York : |b Cambridge University Press, |c 2009. | ||
| 300 | |a 1 online resource (xiii, 349 pages) : |b illustrations, maps | ||
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| 504 | |a Includes bibliographical references (pages 322-344) and index. | ||
| 520 | |a Machine learning methods originated from artificial intelligence and are now used in various fields in environmental sciences today. This is the first single-authored textbook providing a unified treatment of machine learning methods and their applications in the environmental sciences. Due to their powerful nonlinear modelling capability, machine learning methods today are used in satellite data processing, general circulation models(GCM), weather and climate prediction, air quality forecasting, analysis and modelling of environmental data, oceanographic and hydrological forecasting, ecological modelling, and monitoring of snow, ice and forests. The book includes end-of-chapter review questions and an appendix listing web sites for downloading computer code and data sources. A resources website containing datasets for exercises, and password-protected solutions are available. The book is suitable for first-year graduate students and advanced undergraduates. It is also valuable for researchers and practitioners in environmental sciences interested in applying these new methods to their own work. | ||
| 505 | 0 | |a Basic notions in classical data analysis -- Linear multivariate statistical analysis -- Basic time series analysis -- Feed-forward neural network models -- Nonlinear optimization -- Learning and generalization -- Kernel methods -- Nonlinear classification -- Nonlinear regression -- Nonlinear principal component analysis -- Nonlinear canonical correlation analysis -- Applications in environmental sciences. | |
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| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Environmental sciences. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Sciences de l'environnement. | |
| 650 | 7 | |a environmental sciences. |2 aat | |
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| 650 | 7 | |a COMPUTERS |x Intelligence (AI) & Semantics. |2 bisacsh | |
| 650 | 7 | |a Environmental sciences. |2 fast |0 (OCoLC)fst00913474 | |
| 650 | 7 | |a Machine learning. |2 fast |0 (OCoLC)fst01004795 | |
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