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). |
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