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Reformulated parametric learning based on ordinary differential equations
Yang, Shuang-Hong; Hu, Bao-Gang; Huang, DS; Li, K; Irwin, GW
2006
发表期刊COMPUTATIONAL INTELLIGENCE, PT 2, PROCEEDINGS
卷号4114页码:256-267
文章类型Article
摘要This paper presents a new parametric learning scheme, namely, Reformulated Parametric Learning (RPL). Instead of learning the parameters directly on the original model, this scheme reformulates the model into a simpler yet equivalent one, and all parameters are estimated on the reformulated model. While a set of simpler equivalent models can be obtained from deriving Equivalent Decomposition Models (EDM) through their associated ordinary differential equations, to achieve the simplest EDM is a combination optimization problem. For a preliminary study, we apply the RPL to a simple class of models, named 'Additive Pseudo-Exponential Models' (APEM). While conventional approaches have to adopt nonlinear programming to learn APEM, the proposed RPL can obtain equivalent solutions through Linear Least -Square (LLS) method. Numeric work confirms the better performance of the proposed scheme in comparing with conventional learning scheme.
WOS标题词Science & Technology ; Technology
收录类别ISTP ; SCI
语种英语
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000240083300033
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/9206
专题09年以前成果
作者单位1.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100080, Peoples R China
2.Chinese Acad Sci, Beijing Grad Sch, Beijing 100080, Peoples R China
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GB/T 7714
Yang, Shuang-Hong,Hu, Bao-Gang,Huang, DS,et al. Reformulated parametric learning based on ordinary differential equations[J]. COMPUTATIONAL INTELLIGENCE, PT 2, PROCEEDINGS,2006,4114:256-267.
APA Yang, Shuang-Hong,Hu, Bao-Gang,Huang, DS,Li, K,&Irwin, GW.(2006).Reformulated parametric learning based on ordinary differential equations.COMPUTATIONAL INTELLIGENCE, PT 2, PROCEEDINGS,4114,256-267.
MLA Yang, Shuang-Hong,et al."Reformulated parametric learning based on ordinary differential equations".COMPUTATIONAL INTELLIGENCE, PT 2, PROCEEDINGS 4114(2006):256-267.
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