Název:	Estimation of Single-Gaussian and Gaussian mixture models for pattern recognition
Autoři:	Vaněk, Jan Machlica, Lukáš Psutka, Josef
Citace zdrojového dokumentu:	VANĚK, 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.
Datum vydání:	2013
Nakladatel:	Springer
Typ dokumentu:	článek article
URI:	http://www.kky.zcu.cz/cs/publications/JanVanek_2013_Estimationof http://hdl.handle.net/11025/17160
ISBN:	978-3-642-41821-1
Klíčová slova:	odhad mí­ry pravděpodobnosti;směsi Gaussovských modelĹů;Kullback- Leiblerova divergence;odchylka;měří­tko
Klíčová slova v dalším jazyce:	maximum likelihood estimation;Gaussian mixture models;Kullback- Leibler divergence;variance;scaling
Abstrakt v dalším jazyce:	Single-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.
Práva:	© Jan Vaněk - Lukáš Machlica - Josef V. Psutka - Josef Psutka
Vyskytuje se v kolekcích:	Články / Articles (KIV) Články / Articles (KKY)

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