An iterative algorithm for entropy regularized likelihood learning on   Gaussian mixture with automatic model selection

doi:10.1016/j.neucom.2006.01.001

CORC > 北京大学 > 信息科学技术学院

	An iterative algorithm for entropy regularized likelihood learning on Gaussian mixture with automatic model selection
	Lu, Zhiwu
刊名	neurocomputing
	2006
关键词	Gaussian mixture regularization theory model selection
DOI	10.1016/j.neucom.2006.01.001
英文摘要	As for Gaussian mixture modeling, the key problem is to select the number of Gaussians in the mixture. Based on regularization theory, we aim to make this kind of model selection by implementing an iterative algorithm for entropy regularized likelihood (ERL) learning on Gaussian mixture. The simulation experiments have demonstrated that the ERL algorithm can automatically detect the number of Gaussians with a good estimation of the parameters in the original mixture, even on a sample set with a high degree of overlap. Moreover, the ERL algorithm also leads to a promising result when applied to the classification of iris data. (c) 2006 Elsevier B.V. All rights reserved.; Computer Science, Artificial Intelligence; SCI(E); EI; 8; ARTICLE; 13-15; 1674-1677; 69
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/161912]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Lu, Zhiwu. An iterative algorithm for entropy regularized likelihood learning on Gaussian mixture with automatic model selection[J]. neurocomputing,2006.
APA	Lu, Zhiwu.(2006).An iterative algorithm for entropy regularized likelihood learning on Gaussian mixture with automatic model selection.neurocomputing.
MLA	Lu, Zhiwu."An iterative algorithm for entropy regularized likelihood learning on Gaussian mixture with automatic model selection".neurocomputing (2006).