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@InProceedings{FreitasAvilPapa:2007:SeSuVe,
               author = "Freitas, Greice Martins de and Avila, Ana Maria Heuminski de and 
                         Papa, Joao Paulo",
          affiliation = "CEPAGRI/UNICAMP and CEPAGRI/UNICAMP and IC/UNICAMP",
                title = "Semi-Supervised Support Vector Rainfall Estimation Using Satellite 
                         Images",
            booktitle = "Proceedings...",
                 year = "2007",
               editor = "Gon{\c{c}}alves, Luiz and Wu, Shin Ting",
         organization = "Brazilian Symposium on Computer Graphics and Image Processing, 20. 
                         (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "rainfall estimation, semi-supervised support vector machines.",
             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. .",
  conference-location = "Belo Horizonte",
      conference-year = "Oct. 7-10, 2007",
             language = "en",
           targetfile = "freitas.pdf",
        urlaccessdate = "2020, Oct. 26"
}


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