Presentation	2008-03-12 The Dependence of EM Algorithm on Initial Conditions Xin LU, Kiyoshi NISHIYAMA,
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Abstract(in English)	The EM algorithm is considered as a methodology that the problem of incomplete data is solved by the framework of complete data gradually. It is also regarded as an all-purpose algorithm that the maximum estimators of model parameters are intellectually calculated from the incomplete data. Except that simplicity and low computational complexity, the EM algorithm is strongly depend on the initial setting of the estimated parameters of model. In this paper, the EM algorithm is used to estimate the mixture Gaussian distribution in order to confirm its efficiency.
Keyword(in Japanese)	(See Japanese page)
Keyword(in English)	EM algorithm / mixture Gaussian distribution / maximum likelihood estimation
Paper #	NC2007-128
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Conference Information
Committee	NC
Conference Date	2008/3/5(1days)
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Paper Information
Registration To	Neurocomputing (NC)
Language	JPN
Title (in Japanese)	(See Japanese page)
Sub Title (in Japanese)	(See Japanese page)
Title (in English)	The Dependence of EM Algorithm on Initial Conditions
Sub Title (in English)
Keyword(1)	EM algorithm
Keyword(2)	mixture Gaussian distribution
Keyword(3)	maximum likelihood estimation
1st Author's Name	Xin LU
1st Author's Affiliation	Faculty of Engineering, Iwata University()
2nd Author's Name	Kiyoshi NISHIYAMA
2nd Author's Affiliation	Faculty of Engineering, Iwata University
Date	2008-03-12
Paper #	NC2007-128
Volume (vol)	vol.107
Number (no)	542
Page	pp.pp.-
#Pages	6
Date of Issue