Generalization Performance of Radial Basis Function Networks

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 人工智能与机器学习（杨雪冰）-技术团队

	Generalization Performance of Radial Basis Function Networks
	Lei, Yunwen 1; Ding, Lixin 1; Zhang, Wensheng2
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
	2015-03-01
卷号	26 期号:3 页码:551-564
文章类型	Article
摘要	This paper studies the generalization performance of radial basis function (RBF) networks using local Rademacher complexities. We propose a general result on controlling local Rademacher complexities with the L-1-metric capacity. We then apply this result to estimate the RBF networks' complexities, based on which a novel estimation error bound is obtained. An effective approximation error bound is also derived by carefully investigating the Holder continuity of the l(p) loss function's derivative. Furthermore, it is demonstrated that the RBF network minimizing an appropriately constructed structural risk admits a significantly better learning rate when compared with the existing results. An empirical study is also performed to justify the application of our structural risk in model selection.
关键词	Learning Theory Local Rademacher Complexity Radial Basis Function (Rbf) Networks Structural Risk Minimization (Srm)
WOS标题词	Science & Technology ; Technology
关键词[WOS]	MODEL SELECTION ; NEURAL-NETWORKS ; APPROXIMATION ; COMPLEXITY ; BOUNDS ; RISK ; REGULARIZATION ; TRACTABILITY ; REGRESSION ; ERROR
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000351834400011
引用统计	被引频次：22[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/9999
专题	多模态人工智能系统全国重点实验室_人工智能与机器学习（杨雪冰）-技术团队
作者单位	1.Wuhan Univ, Sch Comp, State Key Lab Software Engn, Wuhan 430072, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Lei, Yunwen,Ding, Lixin,Zhang, Wensheng. Generalization Performance of Radial Basis Function Networks[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2015,26(3):551-564.
APA	Lei, Yunwen,Ding, Lixin,&Zhang, Wensheng.(2015).Generalization Performance of Radial Basis Function Networks.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,26(3),551-564.
MLA	Lei, Yunwen,et al."Generalization Performance of Radial Basis Function Networks".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 26.3(2015):551-564.