Generalized Constraint Neural Network Regression Model Subject to Linear Priors
文献类型：期刊论文
| 作者 | Qu, Ya-Jun; Hu, Bao-Gang
|
| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS
 |
| 出版日期 | 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 |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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