DocumentCode
3002290
Title
The expectation-maximization algorithm: Gaussian case
Author
Iatan, I.F.
Author_Institution
Dept. of Math. & Comput. Sci., Tech. Univ. of Civil Eng., Bucharest, Romania
fYear
2010
fDate
11-12 June 2010
Firstpage
590
Lastpage
593
Abstract
There are some situations when in the pattern recognition applications can appear some objects which are missing data. This thing one happens since the process of data acquisition isn´t perfect. In this paper we shall present the EM algorithm (Expectation Maximization) which is used in order to estimate the parameters corresponding to a probability density function when we dispose by missing data. In our case, the class labels are the missing data.
Keywords
Gaussian processes; expectation-maximisation algorithm; pattern clustering; EM algorithm; expectation-maximization algorithm; mixturealgorithm; parameter estimation; pattern recognition; probability density function; Data acquisition; Parameter estimation; Pattern recognition; Probability density function; Expectation-Maximization algorithm; a posteriori probability; gaussian mixture; missing data; probability density function;
fLanguage
English
Publisher
ieee
Conference_Titel
Networking and Information Technology (ICNIT), 2010 International Conference on
Conference_Location
Manila
Print_ISBN
978-1-4244-7579-7
Type
conf
DOI
10.1109/ICNIT.2010.5508443
Filename
5508443
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