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		<citationkey>AmorimCarv:2012:SuLeUs</citationkey>
		<title>Supervised Learning Using Local Analysis in an Optimal-Path Forest</title>
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		<year>2012</year>
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		<author>Amorim, Willian Paraguassu,</author>
		<author>Carvalho, Marcelo Henriques de,</author>
		<affiliation>FACOM - Institute of Computing, Federal University of Mato Grosso do Sul - UFMS</affiliation>
		<affiliation>FACOM - Institute of Computing, Federal University of Mato Grosso do Sul - UFMS</affiliation>
		<editor>Freitas, Carla Maria Dal Sasso,</editor>
		<editor>Sarkar, Sudeep,</editor>
		<editor>Scopigno, Roberto,</editor>
		<editor>Silva, Luciano,</editor>
		<e-mailaddress>paraguassuec@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 25 (SIBGRAPI)</conferencename>
		<conferencelocation>Ouro Preto</conferencelocation>
		<date>Aug. 22-25, 2012</date>
		<booktitle>Proceedings</booktitle>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<contenttype>External Contribution</contenttype>
		<keywords>Supervised classifiers, Optimal-Path Forest.</keywords>
		<abstract>In this paper, we present an OPF-LA (Optimal Path Forest--Local Analysis), a new learning model proposal. OPF-LA is a heuristic that uses local information for selecting prototypes that, in turn, will be used to classify new data. It employs the main ideas of an OPF classifier, suggesting a new procedure in the data training phase. Experimental results show the advantages in efficiency and accuracy over classical learning algorithms in areas such as Support Vector Machines (SVM), Artificial Neural Networks using Multilayer Perceptrons (MP), and Optimal Path Forest (OPF), in several applications.</abstract>
		<language>en</language>
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