Number of the records: 1
Pattern Recognition by Probabilistic Neural Networks - Mixtures of Product Components versus Mixtures of Dependence Trees
- 1.
SYSNO ASEP 0434119 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Pattern Recognition by Probabilistic Neural Networks - Mixtures of Product Components versus Mixtures of Dependence Trees Author(s) Grim, Jiří (UTIA-B) RID, ORCID
Pudil, P. (CZ)Number of authors 2 Source Title NCTA2014 - International Conference on Neural Computation Theory and Applications. - Rome : SCITEPRESS, 2014 - ISBN 978-989-758-054-3 Pages s. 65-75 Number of pages 11 s. Publication form Print - P Action 6-th International Conference on Neural Computation Theory and Applications Event date 22.10.2014-24.10.2014 VEvent location Rome Country IT - Italy Event type WRD Language eng - English Country PT - Portugal Keywords Probabilistic Neural Networks ; Product Mixtures ; Mixtures of Dependence Trees ; EM Algorithm Subject RIV IN - Informatics, Computer Science R&D Projects GA14-02652S GA ČR - Czech Science Foundation (CSF) Institutional support UTIA-B - RVO:67985556 EID SCOPUS 84908887413 Annotation 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. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2015
Number of the records: 1