Full metadata record
DC poleHodnotaJazyk
dc.contributor.authorVaněk, Jan
dc.contributor.authorMachlica, Lukáš
dc.contributor.authorPsutka, Josef
dc.date.accessioned2016-01-07T11:54:32Z-
dc.date.available2016-01-07T11:54:32Z-
dc.date.issued2013
dc.identifier.citationVANĚK, Jan; MACHLICA, Lukᚡ PSUTKA, Josef. Estimation of Single-Gaussian and Gaussian mixture models for pattern recognition. In: Progress in pattern recognition, image analysis, computer vision, and applications. Berlin: Springer, 2013, p. 49-56. (Lectures notes in computer science; 8258). ISBN 978-3-642-41821-1.en
dc.identifier.isbn978-3-642-41821-1
dc.identifier.urihttp://www.kky.zcu.cz/cs/publications/JanVanek_2013_Estimationof
dc.identifier.urihttp://hdl.handle.net/11025/17160
dc.format8 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherSpringeren
dc.relation.ispartofseriesLectures notes in computer science; 8258en
dc.rights© Jan Vaněk - Lukáš Machlica - Josef V. Psutka - Josef Psutkacs
dc.subjectodhad mí­ry pravděpodobnostics
dc.subjectsměsi Gaussovských modelĹůcs
dc.subjectKullback- Leiblerova divergencecs
dc.subjectodchylkacs
dc.subjectměří­tkocs
dc.titleEstimation of Single-Gaussian and Gaussian mixture models for pattern recognitionen
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedSingle-Gaussian and Gaussian-Mixture Models are utilized in various pattern recognition tasks. The model parameters are estimated usually via Maximum Likelihood Estimation (MLE) with respect to available training data. However, if only small amount of training data is available, the resulting model will not generalize well. Loosely speaking, classification performance given an unseen test set may be poor. In this paper, we propose a novel estimation technique of the model variances. Once the variances were estimated using MLE, they are multiplied by a scaling factor, which reflects the amount of uncertainty present in the limited sample set. The optimal value of the scaling factor is based on the Kullback-Leibler criterion and on the assumption that the training and test sets are sampled from the same source distribution. In addition, in the case of GMM, the proper number of components can be determined.en
dc.subject.translatedmaximum likelihood estimationen
dc.subject.translatedGaussian mixture modelsen
dc.subject.translatedKullback- Leibler divergenceen
dc.subject.translatedvarianceen
dc.subject.translatedscalingen
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:Články / Articles (KIV)
Články / Articles (KKY)

Soubory připojené k záznamu:
Soubor Popis VelikostFormát 
JanVanek_2013_Estimationof.pdfPlný text208,74 kBAdobe PDFZobrazit/otevřít
Zobrazit minimální záznam Zobrazit statistiky


Použijte tento identifikátor k citaci nebo jako odkaz na tento záznam: http://hdl.handle.net/11025/17160

Všechny záznamy v DSpace jsou chráněny autorskými právy, všechna práva vyhrazena.

hledání
navigace
  1. DSpace at University of West Bohemia
  2. Publikační činnost / Publications
  3. Fakulta aplikovaných věd / Faculty of Applied Sciences
  4. Katedra kybernetiky / Department of Cybernetics
  5. Články / Articles (KKY)