Combining Supervised and Unsupervised Learning for GIS Classification

Autor:	Torres-Moreno, Juan-Manuel, Bougrain, Laurent, Alexandre, Frdéric
Rok vydání:	2009
Předmět:	Computer Science - Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 8 pages, 3 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/0905.2347 Zobrazit plný text záznamu View this record from Arxiv