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This book discusses machine learning algorithms, such as artificial neural networks of different architectures, statistical learning theory, and Support Vector Machines used for the classification and mapping of spatially distributed data. It presents basic geostatistical algorithms as well. The authors describe new trends in machine lea
This is a digital product.
Machine Learning for Spatial Environmental Data: Theory, Applications, and Software 1st Edition is written by Mikhail Kanevski; Vadim Timonin; Alexi Pozdnukhov and published by EPFL Press. The Digital and eTextbook ISBNs for Machine Learning for Spatial Environmental Data are 9781439808085, 1439808082 and the print ISBNs are 9780849382376, 0849382378. Additional ISBNs for this eTextbook include 9780429147814.

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