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Sequential pattern recognition by maximum conditional informativity
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SYSNO ASEP 0428565 Druh ASEP J - Článek v odborném periodiku Zařazení RIV J - Článek v odborném periodiku Poddruh J Článek ve WOS Název Sequential pattern recognition by maximum conditional informativity Tvůrce(i) Grim, Jiří (UTIA-B) RID, ORCID Celkový počet autorů 1 Zdroj.dok. Pattern Recognition Letters. - : Elsevier - ISSN 0167-8655
Roč. 45, č. 1 (2014), s. 39-45Poč.str. 7 s. Forma vydání Tištěná - P Jazyk dok. eng - angličtina Země vyd. GB - Velká Británie Klíč. slova Multivariate statistics ; Statistical pattern recognition ; Sequential decision making ; Product mixtures ; EM algorithm ; Shannon information Vědní obor RIV IN - Informatika CEP GA14-02652S GA ČR - Grantová agentura ČR GA14-10911S GA ČR - Grantová agentura ČR UT WOS 000337219200006 EID SCOPUS 84897530375 DOI 10.1016/j.patrec.2014.02.024 Anotace 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. Pracoviště Ústav teorie informace a automatizace Kontakt Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Rok sběru 2015
Počet záznamů: 1