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Machine Learning Methods in the Environmental Sciences: Neural Networks and Kernels

Machine Learning Methods in the Environmental Sciences: Neural Networks and Kernels

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William W. Hsieh
Cambridge University Press, 7/30/2009
EAN 9780521791922, ISBN10: 0521791928

Hardcover, 364 pages, 24.7 x 17.4 x 2 cm
Language: English

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 websites for downloading computer code and data sources. A resources website contains 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.

Preface
1. Basic notions in classical data analysis
2. Linear multivariate statistical analysis
3. Basic time series analysis
4. Feed-forward neural network models
5. Nonlinear optimization
6. Learning and generalization
7. Kernel methods
8. Nonlinear classification
9. Nonlinear regression
10. Nonlinear principal component analysis
11. Nonlinear canonical correlation analysis
12. Applications in environmental sciences
Appendix A. Sources for data and codes
Appendix B. Lagrange multipliers
Bibliography
Index.