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		<citationkey>FreitasAvilPapa:2007:SeSuVe</citationkey>
		<author>Freitas, Greice Martins de,</author>
		<author>Avila, Ana Maria Heuminski de,</author>
		<author>Papa, Joao Paulo,</author>
		<affiliation>CEPAGRI/UNICAMP</affiliation>
		<affiliation>CEPAGRI/UNICAMP</affiliation>
		<affiliation>IC/UNICAMP</affiliation>
		<title>Semi-Supervised Support Vector Rainfall Estimation Using Satellite Images</title>
		<conferencename>Brazilian Symposium on Computer Graphics and Image Processing, 20 (SIBGRAPI)</conferencename>
		<year>2007</year>
		<editor>Gonçalves, Luiz,</editor>
		<editor>Wu, Shin Ting,</editor>
		<booktitle>Proceedings</booktitle>
		<date>Oct. 7-10, 2007</date>
		<publisheraddress>Porto Alegre</publisheraddress>
		<publisher>Sociedade Brasileira de Computação</publisher>
		<conferencelocation>Belo Horizonte</conferencelocation>
		<keywords>rainfall estimation, semi-supervised support vector machines.</keywords>
		<abstract>In this paper we introduce the use of semi-supervised support vector machines for rainfall estimation using images obtained from visible and infrared NOAA satellite channels. Two experiments were performed, one involving traditional SVM and other using semi-supervised SVM (S3VM). The S3VM approach outperforms SVM in our experiments, with can be seen as a good methodology for rainfall satellite estimation, due to the large amount of unlabeled data. .</abstract>
		<language>en</language>
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