Combining Supervised and Unsupervised Learning for GIS Classification

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dc.creatorTorres-Moreno, Juan-Manuel
dc.creatorBougrain, Laurent
dc.creatorAlexandre, Frdéric
dc.date2009-05-14
dc.date.accessioned2026-07-07T13:15:02Z
dc.date.available2026-07-07T13:15:02Z
dc.descriptionThis paper presents a new hybrid learning algorithm for unsupervised classification tasks. We combined Fuzzy c-means learning algorithm and a supervised version of Minimerror to develop a hybrid incremental strategy allowing unsupervised classifications. We applied this new approach to a real-world database in order to know if the information contained in unlabeled features of a Geographic Information System (GIS), allows to well classify it. Finally, we compared our results to a classical supervised classification obtained by a multilayer perceptron.
dc.description8 pages, 3 figures
dc.identifierhttps://arxiv.org/abs/0905.2347
dc.identifierhttp://arxiv.org/abs/0905.2347
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/230365
dc.subjectMachine Learning
dc.titleCombining Supervised and Unsupervised Learning for GIS Classification
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