0434119 20240103204926.120141106235959.9 84908887413 11 s. P cav_un_epca*0434276978-989-758-054-365-75RomeSCITEPRESS2014 Probabilistic Neural Networks Product Mixtures Mixtures of Dependence Trees EM Algorithm cav_un_auth*0101091 Rozpoznávání obrazu Department of Pattern Recognition RO RO Department of Pattern Recognition 70 Grim Jiří UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0021092 30 Pudil P. CZ http://library.utia.cas.cz/separaty/2014/RO/grim-0434119.pdf cav_un_auth*0303412 GA14-02652S GA ČR CZ cav_un_auth*0308953 GAP403/12/1557 GA ČR CZ 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. cav_un_auth*0308845 6-th International Conference on Neural Computation Theory and Applications 22.10.2014-24.10.2014 Rome IT IN 2015 2 PR RVO:67985556 http://hdl.handle.net/11104/0238366 S 2014 84908887413 SCOPUS cav_un_epca*0434276 NCTA2014 - International Conference on Neural Computation Theory and Applications 978-989-758-054-3 65 75 Rome SCITEPRESS 2014