Počet záznamů: 1
Comparison of Various Definitions of Proximity in Mixture Estimation
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SYSNO ASEP 0461565 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV D - Článek ve sborníku Název Comparison of Various Definitions of Proximity in Mixture Estimation Tvůrce(i) Nagy, Ivan (UTIA-B) RID, ORCID
Suzdaleva, Evgenia (UTIA-B) RID, ORCID
Pecherková, Pavla (UTIA-B) RIDCelkový počet autorů 3 Zdroj.dok. Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2016), Volume 1. - Setubal : SCITEPRESS, 2016 - ISBN 978-989-758-198-4 Rozsah stran s. 527-534 Poč.str. 8 s. Forma vydání Nosič - C Akce International Conference on Informatics in Control, Automation and Robotics /13./ (ICINCO 2016) Datum konání 29.07.2016 - 31.07.2016 Místo konání Lisbon Země PT - Portugalsko Typ akce WRD Jazyk dok. eng - angličtina Země vyd. PT - Portugalsko Klíč. slova classification ; recursive mixture estimation ; proximity ; Bayesian methods ; mixture based clustering Vědní obor RIV BB - Aplikovaná statistika, operační výzkum CEP GA15-03564S GA ČR - Grantová agentura ČR Institucionální podpora UTIA-B - RVO:67985556 UT WOS 000392610900063 EID SCOPUS 85013029030 DOI 10.5220/0005982805270534 Anotace 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. Pracoviště Ústav teorie informace a automatizace Kontakt Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Rok sběru 2017
Počet záznamů: 1