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		<citationkey>RivaCampPont:2016:ApInCl</citationkey>
		<title>Aprendizado incremental e classe-incremental por meio da atualização de árvores geradoras em florestas de caminhos ótimos</title>
		<format>On-line</format>
		<year>2016</year>
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		<author>Riva, Mateus,</author>
		<author>Campos, Teófilo de,</author>
		<author>Ponti, Moacir,</author>
		<affiliation>ICMC/Universidade de São Paulo</affiliation>
		<affiliation>CVSSP/University of Surrey, FGA/Universidade de Brasília</affiliation>
		<affiliation>ICMC/Universidade de São Paulo</affiliation>
		<editor>Aliaga, Daniel G.,</editor>
		<editor>Davis, Larry S.,</editor>
		<editor>Farias, Ricardo C.,</editor>
		<editor>Fernandes, Leandro A. F.,</editor>
		<editor>Gibson, Stuart J.,</editor>
		<editor>Giraldi, Gilson A.,</editor>
		<editor>Gois, João Paulo,</editor>
		<editor>Maciel, Anderson,</editor>
		<editor>Menotti, David,</editor>
		<editor>Miranda, Paulo A. V.,</editor>
		<editor>Musse, Soraia,</editor>
		<editor>Namikawa, Laercio,</editor>
		<editor>Pamplona, Mauricio,</editor>
		<editor>Papa, João Paulo,</editor>
		<editor>Santos, Jefersson dos,</editor>
		<editor>Schwartz, William Robson,</editor>
		<editor>Thomaz, Carlos E.,</editor>
		<e-mailaddress>mar.ee.riva@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 29 (SIBGRAPI)</conferencename>
		<conferencelocation>São José dos Campos, SP, Brazil</conferencelocation>
		<date>4-7 Oct. 2016</date>
		<publisher>Sociedade Brasileira de Computação</publisher>
		<publisheraddress>Porto Alegre</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Undergraduate Work</tertiarytype>
		<transferableflag>1</transferableflag>
		<keywords>aprendizado incremental, minimum spanning trees, optimum-path forests.</keywords>
		<abstract>Class-incremental algorithms, where there is the need to update classification models with data that emerges over time, are important in many applications. We report an algorithm for updating optimum-path forests capable of including a new instance, maintaining the properties of the optimum-path trees. In addition to a proof demonstrating that the algorithm maintains the structure of the trees in linear time,  the experimental evidence shows the applicability of the method, which starting from a limited model, and after the inclusion of multiple instances, is able to achieve the same accuracy of a classifier trained with the full training set.</abstract>
		<language>pt</language>
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		<usergroup>mar.ee.riva@gmail.com</usergroup>
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