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Modeling and Clustering the Behavior of Animals Using Hidden Markov Models
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SYSNO ASEP 0462894 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV D - Článek ve sborníku Název Modeling and Clustering the Behavior of Animals Using Hidden Markov Models Tvůrce(i) Šabata, T. (CZ)
Borovička, T. (CZ)
Holeňa, Martin (UIVT-O) SAI, RIDZdroj.dok. Proceedings ITAT 2016: Information Technologies - Applications and Theory. - Aachen & Charleston : Technical University & CreateSpace Independent Publishing Platform, 2016 / Brejová B. - ISSN 1613-0073 - ISBN 978-1-5370-1674-0 Rozsah stran s. 172-178 Poč.str. 7 s. Forma vydání Online - E Akce ITAT 2016. Conference on Theory and Practice of Information Technologies /16./ Datum konání 15.09.2016-19.09.2016 Místo konání Tatranské Matliare Země SK - Slovensko Typ akce EUR Jazyk dok. eng - angličtina Země vyd. DE - Německo Klíč. slova behavior patterns ; behavioral sequences ; clustering ; hidden Markov models ; Kullback-Leibler divergence Vědní obor RIV IN - Informatika Institucionální podpora UIVT-O - RVO:67985807 EID SCOPUS 85046265121 Anotace The objectives of this article are to model behavior of individual animals and to cluster the resulting models in order to group animals with similar behavior patterns. Hidden Markov models are considered suitable for clustering purposes. Their clustering is well studied, however, only if the observable variables can be assumed to be Gaussian mixtures, which is not valid in our case. Therefore, we use the Kullback-Leibler divergence to cluster hidden Markov models with observable variables that have an arbitrary distribution. Hierarchical and spectral clustering is applied. To evaluate the modeling approach, an experiment was performed and an accuracy of 83.86% was reached in predicting behavioral sequences of individual animals. Results of clustering were evaluated by means of statistical descriptors of the animals and by a domain expert, both methods confirm that the results of clustering are meaningful. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2017 Elektronická adresa http://ceur-ws.org/Vol-1649/172.pdf
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