@InProceedings{SapucciNegr:2017:PrClSe,
               author = "Sapucci, Gabriela Ribeiro and Negri, Rog{\'e}rio Galante",
                title = "Proposta de Classificadores Semissupervisionados baseados em 
                         Rotula{\c{c}}{\~a}o de Agrupamentos via Dist{\^a}ncias 
                         Estoc{\'a}sticas",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "7694--7700",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             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 Tapaj{\'o}s 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.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "60162",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSMG97",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSMG97",
           targetfile = "60162.pdf",
                 type = "Processamento de imagens",
        urlaccessdate = "27 abr. 2024"
}