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arXiv · 0905.2347

Combining Supervised and Unsupervised Learning for GIS Classification

Juan-Manuel Torres-Moreno·Laurent Bougrain·Frdéric Alexandre
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Abstract

This 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.

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Juan-Manuel Torres-Moreno, Laurent Bougrain, Frdéric Alexandre. 2009-05-14. Combining Supervised and Unsupervised Learning for GIS Classification. https://arxiv.org/abs/0905.2347

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