@InProceedings{RivaCampPont:2016:ApInCl,
               author = "Riva, Mateus and Campos, Te{\'o}filo de and Ponti, Moacir",
          affiliation = "{ICMC/Universidade de S{\~a}o Paulo} and CVSSP/University of 
                         Surrey, FGA/Universidade de Bras{\'{\i}}lia and 
                         {ICMC/Universidade de S{\~a}o Paulo}",
                title = "Aprendizado incremental e classe-incremental por meio da 
                         atualiza{\c{c}}{\~a}o de {\'a}rvores geradoras em florestas de 
                         caminhos {\'o}timos",
            booktitle = "Proceedings...",
                 year = "2016",
               editor = "Aliaga, Daniel G. and Davis, Larry S. and Farias, Ricardo C. and 
                         Fernandes, Leandro A. F. and Gibson, Stuart J. and Giraldi, Gilson 
                         A. and Gois, Jo{\~a}o Paulo and Maciel, Anderson and Menotti, 
                         David and Miranda, Paulo A. V. and Musse, Soraia and Namikawa, 
                         Laercio and Pamplona, Mauricio and Papa, Jo{\~a}o Paulo and 
                         Santos, Jefersson dos and Schwartz, William Robson and Thomaz, 
                         Carlos E.",
         organization = "Conference on Graphics, Patterns and Images, 29. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "aprendizado incremental, minimum spanning trees, optimum-path 
                         forests.",
             abstract = "Class-incremental algorithms, where there is the need to update 
                         classification models with data that emerges over time, are 
                         important in many applications. We report an algorithm for 
                         updating optimum-path forests capable of including a new instance, 
                         maintaining the properties of the optimum-path trees. In addition 
                         to a proof demonstrating that the algorithm maintains the 
                         structure of the trees in linear time, the experimental evidence 
                         shows the applicability of the method, which starting from a 
                         limited model, and after the inclusion of multiple instances, is 
                         able to achieve the same accuracy of a classifier trained with the 
                         full training set.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
             language = "pt",
                  ibi = "8JMKD3MGPAW/3MC927E",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3MC927E",
           targetfile = "OPF_CI___SIBGRAPI___WUW__Portugues_(1).pdf",
        urlaccessdate = "2024, May 03"
}