Počet záznamů: 1
Pattern Recognition by Probabilistic Neural Networks - Mixtures of Product Components versus Mixtures of Dependence Trees
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SYSNO ASEP 0434119 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV D - Článek ve sborníku Název Pattern Recognition by Probabilistic Neural Networks - Mixtures of Product Components versus Mixtures of Dependence Trees Tvůrce(i) Grim, Jiří (UTIA-B) RID, ORCID
Pudil, P. (CZ)Celkový počet autorů 2 Zdroj.dok. NCTA2014 - International Conference on Neural Computation Theory and Applications. - Rome : SCITEPRESS, 2014 - ISBN 978-989-758-054-3 Rozsah stran s. 65-75 Poč.str. 11 s. Forma vydání Tištěná - P Akce 6-th International Conference on Neural Computation Theory and Applications Datum konání 22.10.2014-24.10.2014 Místo konání Rome Země IT - Itálie Typ akce WRD Jazyk dok. eng - angličtina Země vyd. PT - Portugalsko Klíč. slova Probabilistic Neural Networks ; Product Mixtures ; Mixtures of Dependence Trees ; EM Algorithm Vědní obor RIV IN - Informatika CEP GA14-02652S GA ČR - Grantová agentura ČR Institucionální podpora UTIA-B - RVO:67985556 EID SCOPUS 84908887413 Anotace We compare two probabilistic approaches to neural networks - the first one based on the mixtures of product components and the second one using the mixtures of dependence-tree distributions. The product mixture models can be efficiently estimated from data by means of EM algorithm and have some practically important properties. However, in some cases the simplicity of product components could appear too restrictive and a natural idea is to use a more complex mixture of dependence-tree distributions. By considering the concept of dependence tree we can explicitly describe the statistical relationships between pairs of variables at the level of individual components and therefore the approximation power of the resulting mixture may essentially increase. Nonetheless, in application to classification of numerals we have found that both models perform comparably and the contribution of the dependence-tree structures decreases in the course of EM iterations. Thus the optimal estimate of the dependence-tree mixture tends to converge to a simple product mixture model. Pracoviště Ústav teorie informace a automatizace Kontakt Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Rok sběru 2015
Počet záznamů: 1