%0 Conference Proceedings
%4 sid.inpe.br/banon/2004/07.27.18.50
%2 sid.inpe.br/banon/2004/07.27.18.50.02
%@doi 10.1109/SIBGRA.2004.1352957
%T W-operator window design by maximization of training data information
%D 2004
%A Martins Junior, David Correa,
%A Cesar Junior, Roberto Marcondes,
%A Barrera, Junior,
%@affiliation Instituto de Matemática e Estatística - Universidade de São Paulo
%E Araújo, Arnaldo de Albuquerque,
%E Comba, João Luiz Dihl,
%E Navazo, Isabel,
%E Sousa, Antônio Augusto de,
%B Brazilian Symposium on Computer Graphics and Image Processing, 17 (SIBGRAPI) - Ibero-American Symposium on Computer Graphics, 2 (SIACG)
%C Curitiba, PR, Brazil
%8 17-20 Oct. 2004
%I IEEE Computer Society
%J Los Alamitos
%S Proceedings
%K w-operator, feature selection, mean conditional entropy.
%X This paper presents a technique that gives a minimal window W for the estimation of a W-operator from training data. The idea is to choose a subset of variables W that maximizes the information observed in a set of training data. The task is formalized as a combinatorial optimization problem, where the search space is the powerset set of the candidate variables and the measure to be minimized is the mean entropy of the estimated conditional probabilities. As a full exploration of the search space requires an enormous computational effort, some heuristics of the feature selection literature are applied. The proposed technique is mathematically sound and experimental results show that it is adequate in practice.
%@language en
%3 4412_Martins_D.pdf