QQ客服 官方微博 反馈留言

	Conjugate and natural gradient rules for BYY harmony learning on Gaussian mixture with automated model selection
	Ma, JW ; Gao, B ; Wang, Y ; Cheng, QS
	2005
关键词	Bayesian Ying-Yang learning Gaussian mixture automated model selection conjugate gradient natural gradient EM ALGORITHM CRITERIA
英文摘要	Under the Bayesian Ying-Yang (BYY) harmony learning theory, a harmony function has been developed on a BI-directional architecture of the BYY system for Gaussian mixture with an important feature that, via its maximization through a general gradient rule, a model selection can be made automatically during parameter learning on a set of sample data from a Gaussian mixture. This paper further proposes the conjugate and natural gradient rules to efficiently implement the maximization of the harmony function, i.e. the BYY harmony learning, on Gaussian mixture. It is demonstrated by simulation experiments that these two new gradient rules not only work well, but also converge more quickly than the general gradient ones.; Computer Science, Artificial Intelligence; SCI(E); EI; 16; ARTICLE; 5; 701-713; 19
语种	英语
出处	EI ; SCI
出版者	international journal of pattern recognition and artificial intelligence
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/314989]
专题	数学科学学院
推荐引用方式 GB/T 7714	Ma, JW,Gao, B,Wang, Y,et al. Conjugate and natural gradient rules for BYY harmony learning on Gaussian mixture with automated model selection. 2005-01-01.

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。