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Machine Learning Methods in the Environmental Sciences
Taschenbuch von William. W Hsieh
Sprache: Englisch

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Beschreibung
A graduate textbook that provides a unified treatment of machine learning methods and their applications in the environmental sciences.
A graduate textbook that provides a unified treatment of machine learning methods and their applications in the environmental sciences.
Über den Autor
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.
Inhaltsverzeichnis
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.
Details
Erscheinungsjahr: 2017
Fachbereich: Populäre Darstellungen
Genre: Chemie
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
ISBN-13: 9781108456906
ISBN-10: 1108456901
Sprache: Englisch
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Hsieh, William. W
Hersteller: Cambridge University Press
Maße: 244 x 170 x 20 mm
Von/Mit: William. W Hsieh
Erscheinungsdatum: 14.12.2017
Gewicht: 0,628 kg
Artikel-ID: 110816558
Über den Autor
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.
Inhaltsverzeichnis
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.
Details
Erscheinungsjahr: 2017
Fachbereich: Populäre Darstellungen
Genre: Chemie
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
ISBN-13: 9781108456906
ISBN-10: 1108456901
Sprache: Englisch
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Hsieh, William. W
Hersteller: Cambridge University Press
Maße: 244 x 170 x 20 mm
Von/Mit: William. W Hsieh
Erscheinungsdatum: 14.12.2017
Gewicht: 0,628 kg
Artikel-ID: 110816558
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