QQ客服 官方微博 反馈留言

	Training sparse MS-SVR with an expectation-maximization algorithm
	Zheng, D. N. ; Wang, J. X. ; Zhao, Y. N.
	2010-05-06 ; 2010-05-06
关键词	multi-scale support vector regression (MS-SVR) hierarchical-Bayes model maximum a posteriori (MAP) estimation expectation-maximization (EM) algorithm RELEVANCE VECTOR MACHINE REGRESSION Computer Science, Artificial Intelligence
中文摘要	The solution of multi-scale support vector regression (MS-SVR) with the quadratic loss function can be obtained by solving a time-consuming quadratic programming (QP) problem and a post-processing. This paper adapts an expectation-maximization (EM) algorithm based on two 2-level hierarchical-Bayes models, which implement the l(1)-norm and the l(0)-norm regularization term asymptotically, to fast train MS-SVR. Experimental results illuminate that the EM algorithm is faster than the QP algorithm for large data sets, the l(0)-norm regularization term promotes a far sparser solution than the l(1)-norm, and the good performance of MS-SVR should be attributed to the multi-scale kernels and the regularization terms. (c) 2006 Elsevier B.V. All rights reserved.
语种	英语 ; 英语
出版者	ELSEVIER SCIENCE BV ; AMSTERDAM ; PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/9900]
专题	清华大学
推荐引用方式 GB/T 7714	Zheng, D. N.,Wang, J. X.,Zhao, Y. N.. Training sparse MS-SVR with an expectation-maximization algorithm[J],2010, 2010.
APA	Zheng, D. N.,Wang, J. X.,&Zhao, Y. N..(2010).Training sparse MS-SVR with an expectation-maximization algorithm..
MLA	Zheng, D. N.,et al."Training sparse MS-SVR with an expectation-maximization algorithm".(2010).

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。