William W. HsiehCambridge University Press, 7/30/2009EAN 9780521791922, ISBN10: 0521791928Hardcover, 364 pages, 24.7 x 17.4 x 2 cmLanguage: EnglishMachine 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.Preface1. Basic notions in classical data analysis2. Linear multivariate statistical analysis3. Basic time series analysis4. Feed-forward neural network models5. Nonlinear optimization6. Learning and generalization7. Kernel methods8. Nonlinear classification9. Nonlinear regression10. Nonlinear principal component analysis11. Nonlinear canonical correlation analysis12. Applications in environmental sciencesAppendix A. Sources for data and codesAppendix B. Lagrange multipliersBibliographyIndex.
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