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@InProceedings{AmorimCarv:2012:SuLeUs,
               author = "Amorim, Willian Paraguassu and Carvalho, Marcelo Henriques de",
          affiliation = "FACOM - Institute of Computing, Federal University of Mato Grosso 
                         do Sul - UFMS and FACOM - Institute of Computing, Federal 
                         University of Mato Grosso do Sul - UFMS",
                title = "Supervised Learning Using Local Analysis in an Optimal-Path 
                         Forest",
            booktitle = "Proceedings...",
                 year = "2012",
               editor = "Freitas, Carla Maria Dal Sasso and Sarkar, Sudeep and Scopigno, 
                         Roberto and Silva, Luciano",
         organization = "Conference on Graphics, Patterns and Images, 25. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Supervised classifiers, Optimal-Path Forest.",
             abstract = "In this paper, we present an OPF-LA (Optimal Path Forest--Local 
                         Analysis), a new learning model proposal. OPF-LA is a heuristic 
                         that uses local information for selecting prototypes that, in 
                         turn, will be used to classify new data. It employs the main ideas 
                         of an OPF classifier, suggesting a new procedure in the data 
                         training phase. Experimental results show the advantages in 
                         efficiency and accuracy over classical learning algorithms in 
                         areas such as Support Vector Machines (SVM), Artificial Neural 
                         Networks using Multilayer Perceptrons (MP), and Optimal Path 
                         Forest (OPF), in several applications.",
  conference-location = "Ouro Preto",
      conference-year = "Aug. 22-25, 2012",
             language = "en",
           targetfile = "PID2448677.pdf",
        urlaccessdate = "2021, Jan. 28"
}


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