@InProceedings{RibeiroHash:2008:NeTrAl,
               author = "Ribeiro, Joao Henrique Burckas and Hashimoto, Ronaldo Fumio",
          affiliation = "{Institute of Mathematics and Statistics - University of Sao 
                         Paulo} and {Institute of Mathematics and Statistics - University 
                         of Sao Paulo}",
                title = "A New Training Algorithm for Pattern Recognition Technique Based 
                         on Straight Line Segments",
            booktitle = "Proceedings...",
                 year = "2008",
               editor = "Jung, Cl{\'a}udio Rosito and Walter, Marcelo",
         organization = "Brazilian Symposium on Computer Graphics and Image Processing, 21. 
                         (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Straight Line Segments, Machine Learning, Pattern Recognition, 
                         Classification, Support Vector Machine.",
             abstract = "Recently, a new Pattern Recognition technique based on straight 
                         line segments (SLSs) was presented. The key issue in this new 
                         technique is to find a function based on distances between points 
                         and two sets of SLSs that minimizes a certain error or risk 
                         criterion. An algorithm for solving this optimization problem is 
                         called training algorithm. Although this technique seems to be 
                         very promising, the first presented training algorithm is based on 
                         a heuristic. In fact, the search for this best function is a hard 
                         nonlinear optimization problem. In this paper, we present a new 
                         and improved training algorithm for the SLS technique based on 
                         gradient descent optimization method. We have applied this new 
                         training algorithm to artificial and public data sets and their 
                         results confirm the improvement of this methodology..",
  conference-location = "Campo Grande, MS, Brazil",
      conference-year = "12-15 Oct. 2008",
                  doi = "10.1109/SIBGRAPI.2008.35",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2008.35",
             language = "en",
                  ibi = "6qtX3pFwXQZG2LgkFdY/URfSG",
                  url = "http://urlib.net/ibi/6qtX3pFwXQZG2LgkFdY/URfSG",
           targetfile = "sibgrapi2008_sls.pdf",
        urlaccessdate = "2024, May 02"
}