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@InProceedings{Hirata:2005:BiImOp,
               author = "Hirata, Nina Sumiko Tomita",
          affiliation = "Department of Computer Science, Institute of Mathematics and 
                         Statistics, University of Sao Paulo",
                title = "Binary image operator design based on stacked generalization",
            booktitle = "Proceedings...",
                 year = "2005",
               editor = "Rodrigues, Maria Andr?ia Formico and Frery, Alejandro C?sar",
         organization = "Brazilian Symposium on Computer Graphics and Image Processing, 18. 
                         (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "stacked generalization, image operator design, multi-stage 
                         training.",
             abstract = "Stacked generalization refers to any learning schema that consists 
                         of multiple levels of training. Level zero classifiers are those 
                         that depend solely on input data while classifiers at other levels 
                         may use the output of lower levels as the input. Stacked 
                         generalization can be used to address the difficulties related to 
                         the design of image operators defined on large windows. This paper 
                         describes a simple stacked generalization schema for the design of 
                         binary image operators and presents several application examples 
                         that show its effectiveness as a training schema.",
  conference-location = "Natal",
      conference-year = "9-12 Oct. 2005",
             language = "en",
           targetfile = "hiratan.pdf",
        urlaccessdate = "2020, Nov. 26"
}


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