Generalized Constraint Neural Network Regression Model Subject to Linear Priors

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	Generalized Constraint Neural Network Regression Model Subject to Linear Priors
	Qu, Ya-Jun; Hu, Bao-Gang
刊名	IEEE TRANSACTIONS ON NEURAL NETWORKS
	2011-12-01
卷号	22 期号:12 页码:2447-2459
关键词	Linear constraints linear priors nonlinear regression radial basis function networks transparency
英文摘要	This paper is reports an extension of our previous investigations on adding transparency to neural networks. We focus on a class of linear priors (LPs), such as symmetry, ranking list, boundary, monotonicity, etc., which represent either linear-equality or linear-inequality priors. A generalized constraint neural network-LPs (GCNN-LPs) model is studied. Unlike other existing modeling approaches, the GCNN-LP model exhibits its advantages. First, any LP is embedded by an explicitly structural mode, which may add a higher degree of transparency than using a pure algorithm mode. Second, a direct elimination and least squares approach is adopted to study the model, which produces better performances in both accuracy and computational cost over the Lagrange multiplier techniques in experiments. Specific attention is paid to both "hard (strictly satisfied)" and "soft (weakly satisfied)" constraints for regression problems. Numerical investigations are made on synthetic examples as well as on the real-world datasets. Simulation results demonstrate the effectiveness of the proposed modeling approach in comparison with other existing approaches.
WOS标题词	Science & Technology ; Technology
类目[WOS]	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
研究领域[WOS]	Computer Science ; Engineering
关键词[WOS]	SUPPORT VECTOR MACHINES ; INCORPORATING PRIOR KNOWLEDGE ; KERNEL APPROXIMATION ; EXTRACTION
收录类别	SCI
语种	英语
WOS记录号	WOS:000299082900027
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/2805]
专题	自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队
作者单位	Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Qu, Ya-Jun,Hu, Bao-Gang. Generalized Constraint Neural Network Regression Model Subject to Linear Priors[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS,2011,22(12):2447-2459.
APA	Qu, Ya-Jun,&Hu, Bao-Gang.(2011).Generalized Constraint Neural Network Regression Model Subject to Linear Priors.IEEE TRANSACTIONS ON NEURAL NETWORKS,22(12),2447-2459.
MLA	Qu, Ya-Jun,et al."Generalized Constraint Neural Network Regression Model Subject to Linear Priors".IEEE TRANSACTIONS ON NEURAL NETWORKS 22.12(2011):2447-2459.