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Sequential pattern recognition by maximum conditional informativity
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SYSNO ASEP 0428565 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Sequential pattern recognition by maximum conditional informativity Author(s) Grim, Jiří (UTIA-B) RID, ORCID Number of authors 1 Source Title Pattern Recognition Letters. - : Elsevier - ISSN 0167-8655
Roč. 45, č. 1 (2014), s. 39-45Number of pages 7 s. Publication form Print - P Language eng - English Country GB - United Kingdom Keywords Multivariate statistics ; Statistical pattern recognition ; Sequential decision making ; Product mixtures ; EM algorithm ; Shannon information Subject RIV IN - Informatics, Computer Science R&D Projects GA14-02652S GA ČR - Czech Science Foundation (CSF) GA14-10911S GA ČR - Czech Science Foundation (CSF) UT WOS 000337219200006 EID SCOPUS 84897530375 DOI 10.1016/j.patrec.2014.02.024 Annotation 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. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2015
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