0461565 20240111140922.620160808235959.9 85013029030 000392610900063 10.5220/0005982805270534 8 s. C cav_un_epca*0461767978-989-758-198-4Volume 1527-534SetubalSCITEPRESS2016 classification recursive mixture estimation proximity Bayesian methods mixture based clustering cav_un_auth*0101167 Zpracování signálů Department of Signal Processing ZS ZS Department of Signal Processing Nagy Ivan UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0108105 Zpracování signálů Department of Signal Processing ZS ZS Department of Signal Processing Suzdaleva Evgenia UTIA-B RU Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0205791 Adaptivní systémy Department of Adaptive Systems AS AS Department of Signal Processing Pecherková Pavla UTIA-B CZ Ústav teorie informace a automatizace AV ČR, v. v. i. pdf http://library.utia.cas.cz/separaty/2016/ZS/suzdaleva-0461565.pdf cav_un_auth*0321440 GA15-03564S GA ČR CZ Classification is one of the frequently demanded tasks in data analysis. There exists a series of approaches in this area. This paper is oriented towards classification using the mixture model estimation, which is based on detection of density clusters in the data space and fitting the component models to them. A chosen function of proximity of the actually measured data to individual mixture components and the component shape play a significant role in solving the mixture-based classification task. This paper considers definitions of the proximity for several types of distributions describing the mixture components and compares their properties with respect to speed and quality of the resulting estimation interpreted as a classification task. Normal, exponential and uniform distributions as the most important models used for describing both Gaussian and non-Gaussian data are considered. Illustrative experiments with results of the comparison are provided. cav_un_auth*0332301 International Conference on Informatics in Control, Automation and Robotics /13./ (ICINCO 2016) 20160729 20160731 Lisbon PT BB 2017 3 4 A hod 4ah 20231122141811.8 PR RVO:67985556 http://hdl.handle.net/11104/0261344 1 Department of Signal Processing UTIA-B 10103 STATISTICS & PROBABILITY S n.a. Proceedings Paper Automation Control Systems|Engineering Electrical Electronic|Robotics 2016 Soubory v repozitáři: suzdaleva-0461565.pdf 85013029030 SCOPUS 000392610900063 WOS pdf cav_un_epca*0461767 Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2016) Volume 1 978-989-758-198-4 527 534 Setubal SCITEPRESS 2016