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	A generalized competitive learning algorithm on Gaussian mixture with automatic model selection
	Lu, Zhiwu ; Lu, Xiaoqing
	2006
关键词	Gaussian mixture model selection regularization theory competitive learning CLASSIFICATION
英文摘要	Derived from regularization theory, an adaptive entropy regularized likelihood (ERL) learning algorithm is presented for Gaussian mixture modeling, which is then proved to be actually a generalized competitive learning. The simulation experiments demonstrate that our adaptive ERL learning algorithm can make the parameter estimation with automatic model selection for Gaussian mixture even when two or more Gaussians are overlapped in a high degree,; http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000239623500081&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701 ; Computer Science, Artificial Intelligence; SCI(E); EI; CPCI-S(ISTP); 0
语种	英语
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/321108]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Lu, Zhiwu,Lu, Xiaoqing. A generalized competitive learning algorithm on Gaussian mixture with automatic model selection. 2006-01-01.

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