A fast globally supervised learning algorithm for Gaussian Mixture Models

	A fast globally supervised learning algorithm for Gaussian Mixture Models
	Ma, JY; Gao, W
	2000
发表期刊	WEB-AGE INFORMATION MANAGEMENT, PROCEEDINGS
ISSN	0302-9743
卷号	1846 页码:449-454
摘要	In this paper, a fast globally supervised learning algorithm for Gaussian Mixture Models based on the maximum relative entropy (MRE) is proposed. To reduce the computation complexity in Gaussian component probability densities, the concept of quasi-Gaussian probability density is used to compute the simplified probabilities. For four different learning algorithms such as the maximum mutual information algorithm (MMI), the maximum likelihood estimation (MLE), the generalized probabilistic descent (GPD) and the maximum relative entropy (MRE) algorithm, the random experiment approach is used to evaluate their performances. The experimental results show that the MRE is a better alternative algorithm in accuracy and training speed compared with GPD, MMI and MLE.
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems ; Computer Science, Theory & Methods
WOS记录号	WOS:000171155900042
出版者	SPRINGER-VERLAG BERLIN
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/13325
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Ma, JY
作者单位	Chinese Acad Sci, Inst Comp Technol, Beijing 100864, Peoples R China
推荐引用方式 GB/T 7714	Ma, JY,Gao, W. A fast globally supervised learning algorithm for Gaussian Mixture Models[J]. WEB-AGE INFORMATION MANAGEMENT, PROCEEDINGS,2000,1846:449-454.
APA	Ma, JY,&Gao, W.(2000).A fast globally supervised learning algorithm for Gaussian Mixture Models.WEB-AGE INFORMATION MANAGEMENT, PROCEEDINGS,1846,449-454.
MLA	Ma, JY,et al."A fast globally supervised learning algorithm for Gaussian Mixture Models".WEB-AGE INFORMATION MANAGEMENT, PROCEEDINGS 1846(2000):449-454.