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		<citationkey>BreveJúniMasc:2007:MuPeCl</citationkey>
		<author>Breve, Fabricio Aparecido,</author>
		<author>Júnior, Moacir Pereira Ponti,</author>
		<author>Mascarenhas, Nelson Delfino D'Ávila,</author>
		<affiliation>Universidade Federal de São Carlos</affiliation>
		<affiliation>Universidade Federal de São Carlos</affiliation>
		<affiliation>Universidade Federal de São Carlos</affiliation>
		<title>Multilayer Perceptron Classifier Combination for Identification of Materials on Noisy Soil Science Multispectral Images</title>
		<conferencename>Brazilian Symposium on Computer Graphics and Image Processing, 20 (SIBGRAPI)</conferencename>
		<year>2007</year>
		<editor>Falcão, Alexandre Xavier,</editor>
		<editor>Lopes, Hélio Côrtes Vieira,</editor>
		<booktitle>Proceedings</booktitle>
		<date>Oct. 7-10, 2007</date>
		<publisheraddress>Los Alamitos</publisheraddress>
		<publisher>IEEE Computer Society</publisher>
		<conferencelocation>Belo Horizonte</conferencelocation>
		<keywords>classifier combination multilayer perceptron dempster-shafer decision templates bagging soil tomograph images.</keywords>
		<abstract>Classifier combination experiments using the Multilayer Perceptron (MLP) were carried out using noisy soil science multispectral images, which were obtained using a Tomograph scanner. Using few units in the MLP hidden layer, images were classified using a single classifier. Later we used classifier combining techniques as Bagging, Decision Templates (DT) and Dempster-Shafer (DS), in order to improve the performance of the single classifiers and also stabilize the performance of the Multilayer Perceptron. The classification results were evaluated using Cross-Validation. The results showed stabilization of Multilayer Perceptron and improved results were achieved with fewer units in the MLP hidden layer.</abstract>
		<language>en</language>
		<tertiarytype>Full Paper</tertiarytype>
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