6 October 2006 Issues in training SVM classifications
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Abstract
SVM classification has great potential in remote sensing. The nature of SVM classification also provides opportunities for accurate classification from relatively small training sets, especially if interest is focused on a single class. Five approaches to reducing training set size from that suggested by conventional heuristics are discussed: intelligent selection of the most informative training samples, selective class exclusion, acceptance of imprecise descriptions for spectrally distinct classes, the adoption of a one-class classifier and a focus on boundary regions. All five approaches were able to reduce the training set size required considerably below that suggested by conventional widely used heuristics without significant impact on the accuracy with which the class of interest was classified. For example, reductions in training set size of ~90% from that suggested by a conventional heuristic are reported with the accuracy of the class of interest remaining nearly constant at ~95% and ~97% from the user's and producer's perspectives respectively.
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Giles M. Foody, Giles M. Foody, Ajay Mathur, Ajay Mathur, Carolina Sanchez-Hernandez, Carolina Sanchez-Hernandez, Doreen S. Boyd, Doreen S. Boyd, } "Issues in training SVM classifications", Proc. SPIE 6365, Image and Signal Processing for Remote Sensing XII, 63650Q (6 October 2006); doi: 10.1117/12.695460; https://doi.org/10.1117/12.695460
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