0428565 20240103204258.020140624235959.9 000337219200006 84897530375 10.1016/j.patrec.2014.02.024 7 s. P cav_un_epca*02573890167-8655451 (2014)39-45Elsevier Multivariate statistics Statistical pattern recognition Sequential decision making Product mixtures EM algorithm Shannon information cav_un_auth*0101091 Grim Jiří Rozpoznávání obrazu Department of Pattern Recognition RO RO UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2014/RO/grim-0428565.pdf GA14-02652S GA ČR CZ cav_un_auth*0303412 GA14-10911S GA ČR CZ cav_un_auth*0303439 Sequential pattern recognition assumes the features to be measured successively, one at a time, and therefore the key problem is to choose the next feature optimally. However, the choice of the features may be strongly influenced by the previous feature measurements and therefore the on-line ordering of features is difficult. There are numerous methods to estimate class-conditional probability distributions but it is usually computationally intractable to derive the corresponding conditional marginals. In literature there is no exact method of on-line feature ordering except for the strongly simplifying naive Bayes models. We show that the problem of sequential recognition has an explicit analytical solution which is based on approximation of the class-conditional distributions by mixtures of product components. 2015 IN 1 4 A 4a 20231122140244.7 http://hdl.handle.net/11104/0234221 S COMPUTERSCIENCEARTIFICIALINTELLIGENCE 0.689 0.730 Q1 60.262 55.691 Q2 Q2 2014 Soubory v repozitáři: grim-0428565.pdf 84897530375 SCOPUS 000337219200006 WOS cav_un_epca*0257389 Pattern Recognition Letters 0167-8655 1872-7344 Roč. 45 č. 1 2014 39 45 Elsevier