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		<isbn>978-85-17-00088-1</isbn>
		<label>60162</label>
		<citationkey>SapucciNegr:2017:PrClSe</citationkey>
		<title>Proposta de Classificadores Semissupervisionados baseados em Rotulação de Agrupamentos via Distâncias Estocásticas</title>
		<format>Internet</format>
		<year>2017</year>
		<secondarytype>PRE CN</secondarytype>
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		<size>1765 KiB</size>
		<author>Sapucci, Gabriela Ribeiro,</author>
		<author>Negri, Rogério Galante,</author>
		<electronicmailaddress>gabrielasapucci@gmail.com</electronicmailaddress>
		<editor>Gherardi, Douglas Francisco Marcolino,</editor>
		<editor>Aragão, Luiz Eduardo Oliveira e Cruz de,</editor>
		<e-mailaddress>daniela.seki@inpe.br</e-mailaddress>
		<conferencename>Simpósio Brasileiro de Sensoriamento Remoto, 18 (SBSR)</conferencename>
		<conferencelocation>Santos</conferencelocation>
		<date>28-31 maio 2017</date>
		<publisher>Instituto Nacional de Pesquisas Espaciais (INPE)</publisher>
		<publisheraddress>São José dos Campos</publisheraddress>
		<pages>7694-7700</pages>
		<booktitle>Anais</booktitle>
		<organization>Instituto Nacional de Pesquisas Espaciais (INPE)</organization>
		<transferableflag>1</transferableflag>
		<abstract>Remote sensing image classification is one of the most important applications of Pattern Recognition in environmental studies. Image classification methods generally have supervised learning or unsupervised. As supervised learning methods perform sorting by means of a function or decision rule modeled through information provided in advance, the quality of the results is directly related to the quality of the set of training standards, which doesn''t always guarantee quality results. Unsupervised learning, in turn, build your knowledge in function of analogies observed about the data, which can be a complex task. Alternatively, the semi-supervised learning aims to deal with the weaknesses of both paradigms, by combining concepts of learning with and without supervision. In this context, this research project proposes the formalization and implementation of two methods of semi-supervised classification, which combines classic tools in the area of pattern recognition: the Hierarchical Divisive Algorithms, $K$-Means and stochastic distances. From a set of groups, defined by the combination of Hierarchical Divisive Algorithm and $K$-Means and another defined only by $K$-Means,  through unsupervised learning, stochastic distances are used for labeling of each of these groups. Through case studies on the use and classification of ground cover around the Tapajós National Forest, the quality of the results obtained according to the Kappa coefficient was analyzed and the proposed methods were compared with other classification methods already known in the literature.</abstract>
		<area>SRE</area>
		<type>Processamento de imagens</type>
		<language>pt</language>
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