%0 Conference Proceedings
%4 sid.inpe.br/banon/2002/11.28.12.18
%2 sid.inpe.br/banon/2002/11.28.12.19
%@doi 10.1109/SIBGRAPI.2001.963037
%T Ongoing learning for supervised pattern recognition
%D 2001
%A R., Barandela.,
%A Juarez, M.,
%E Borges, Leandro Díbio,
%E Wu, Shin-Ting,
%B Brazilian Symposium on Computer Graphics and Image Processing, 14 (SIBGRAPI)
%C Florianópolis, SC, Brazil
%8 15-18 Oct. 2001
%I IEEE Computer Society
%J Los Alamitos
%P 51-58
%S Proceedings
%1 SBC - Brazilian Computer Society
%K anytime supervised learning, training sample correction, reject option, nearest neighbor rule.
%X This paper presents a procedure to implement an automatic system for supervised pattern recognition with an ongoing learning capability. The purpose is to continuously increase the knowledge of the system and, accordingly, to enhance its performance in classification tasks.The Nearest Neighbor rule is employed as the central classifier and several techniques are added to cope with the increase in computational load and with the peril of incorporating noisy data to the training sample. Experimental results confirm the improvement in classification accuracy.
%@language en
%3 51-58.pdf
%O The conference was held in Florianópolis, SC, Brazil, from October 15 to 18.