0410888 20240103182246.520060210235959.9 0-7695-1699-8 2 Los Alamitos IEEE Computer Society 2002 4 s. 136-139KasturiR.LaurendeauD.SuenC. neural networks finite mixtures boosting cav_un_auth*0101091 Grim Jiří UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101182 Pudil Pavel UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101197 Somol Petr UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/historie/grim-boosting in probabilistic neural networks.pdf 09K GA402/01/0981 GA ČR cav_un_auth*0008962 KSK1019101 GA AV ČR cav_un_auth*0000219 CEZ:AV0Z1075907 It has been verified in practical experiments that the classification performance can be improved by increasing the weights of misclassified training samples. We prove that in case of maximum-likelihood estimation the weighting of discrete data vectors is asymptotically equivalent to multiplication of the estimated distributions by a positive function. Consequently, the Bayesian decision-making can be made asymptotically invariant with respect to arbitrary weighting of data under certain conditions. cav_un_auth*0212920 International Conference on Pattern Recognition /16./ Québec City CA 11.08.2002-15.08.2002 BB RO http://hdl.handle.net/11104/0130975 UTIA-B 20020102 2002 2002 Los Alamitos IEEE Computer Society 0-7695-1699-8 Proceedings of the 16th International Conference on Pattern Recognition 136 139 Kasturi R. 340 Laurendeau D. 340 Suen C. 340