Support vector machines for urban growth modeling
Author:
Publisher
Springer Science and Business Media LLC
Subject
Geography, Planning and Development,Information Systems
Link
http://link.springer.com/content/pdf/10.1007/s10707-009-0077-4.pdf
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4. Cherkassky V, Ma Y (2004) Practical selection of SVM parameters and noise estimation for SVM regression. Neural Netw 17:113–126
5. Clarke KC, Gaydos LJ (1998) Loose-coupling a CA model and GIS: long-term urban growth prediction for San Francisco and Washington/Baltimore. Int J Geogr Inf Sci 12:699–714
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