%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi@80/2008/07.23.17.43
%2 sid.inpe.br/sibgrapi@80/2008/07.23.17.43.49
%@doi 10.1109/SIBGRAPI.2008.35
%T A New Training Algorithm for Pattern Recognition Technique Based on Straight Line Segments
%D 2008
%A Ribeiro, Joao Henrique Burckas,
%A Hashimoto, Ronaldo Fumio,
%@affiliation Institute of Mathematics and Statistics - University of Sao Paulo
%@affiliation Institute of Mathematics and Statistics - University of Sao Paulo
%E Jung, Cláudio Rosito,
%E Walter, Marcelo,
%B Brazilian Symposium on Computer Graphics and Image Processing, 21 (SIBGRAPI)
%C Campo Grande, MS, Brazil
%8 12-15 Oct. 2008
%I IEEE Computer Society
%J Los Alamitos
%S Proceedings
%K Straight Line Segments, Machine Learning, Pattern Recognition, Classification, Support Vector Machine.
%X 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..
%@language en
%3 sibgrapi2008_sls.pdf