A graduate textbook that provides a unified treatment of machine learning methods and their applications in the environmental sciences.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
William W. Hsieh is a Professor in the Department of Earth and Ocean Sciences and in the Department of Physics and Astronomy, as well as Chair of the Atmospheric Science Programme, at the University of British Columbia. He is internationally known for his pioneering work in developing and applying machine learning methods in environmental sciences. He has published over eighty peer-reviewed journal publications covering areas of climate variability, machine learning, oceanography, atmospheric science and hydrology.
Inhaltsangabe
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.
