Estimation of Single-Gaussian and Gaussian Mixture Models for Pattern Recognition
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F13%3A43920627" target="_blank" >RIV/49777513:23520/13:43920627 - isvavai.cz</a>
Result on the web
<a href="http://link.springer.com/chapter/10.1007%2F978-3-642-41822-8_7" target="_blank" >http://link.springer.com/chapter/10.1007%2F978-3-642-41822-8_7</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-642-41822-8_7" target="_blank" >10.1007/978-3-642-41822-8_7</a>
Alternative languages
Result language
angličtina
Original language name
Estimation of Single-Gaussian and Gaussian Mixture Models for Pattern Recognition
Original language description
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.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
—
Result continuities
Project
<a href="/en/project/TA01011264" target="_blank" >TA01011264: Elimination of the language barriers faced by the handicapped watchers of the Czech Television II</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Progress in Pattern Recognition, Image Analysis, ComputerVision, and Applications
ISBN
978-3-642-41821-1
ISSN
0302-9743
e-ISSN
—
Number of pages
8
Pages from-to
49-56
Publisher name
Springer
Place of publication
Berlin
Event location
Havana, Cuba
Event date
Nov 20, 2013
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—