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Learning With Coefficient-Based Regularized Regression on Markov Resampling
Li, Luoqing1; Li, Weifu1; Zou, Bin1; Wang, Yulong2,3; Tang, Yuan Yan3; Han, Hua4
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
2018-09-01
Volume29Issue:9Pages:4166-4176
SubtypeArticle
AbstractBig data research has become a globally hot topic in recent years. One of the core problems in big data learning is how to extract effective information from the huge data. In this paper, we propose a Markov resampling algorithm to draw useful samples for handling coefficient-based regularized regression (CBRR) problem. The proposed Markov resampling algorithm is a selective sampling method, which can automatically select uniformly ergodic Markov chain (u.e.M.c.) samples according to transition probabilities. Based on u.e.M.c. samples, we analyze the theoretical performance of CBRR algorithm and generalize the existing results on independent and identically distributed observations. To be specific, when the kernel is infinitely differentiable, the learning rate depending on the sample size m can be arbitrarily close to O(m(-1)) under a mild regularity condition on the regression function. The good generalization ability of the proposed method is validated by experiments on simulated and real data sets.
KeywordCoefficient-based Regularized Regression (Cbrr) Learning Rate Markov Resampling Uniformly Ergodic Markov Chain (U.e.m.c.)
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TNNLS.2017.2757140
WOS KeywordNEURAL-NETWORKS ; GENERALIZATION PERFORMANCE ; ERROR ANALYSIS ; CLASSIFICATION ; KERNELS ; CHAINS ; OPTIMIZATION ; ALGORITHMS ; TOKAMAK ; MODELS
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(11771130 ; Strategic Priority Research Program of the CAS(XDB02060000) ; University of Macau(MYRG205(Y1-L4)-FST11-TYY ; Science and Technology Development Fund (FDCT) of Macau(100-2012-A3 ; 11371007 ; MYRG187(Y1-L3)-FST11-TYY ; 026-2013-A) ; 61702057 ; RDG009/FST-TYY/2012) ; 61273244)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000443083700019
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21825
Collection类脑智能研究中心
Affiliation1.Hubei Univ, Fac Math & Stat, Hubei Key Lab Appl Math, Wuhan 430062, Hubei, Peoples R China
2.Chengdu Univ, Sch Informat Sci & Engn, Chengdu 610106, Sichuan, Peoples R China
3.Univ Macau, Fac Sci & Technol, Macau 999078, Peoples R China
4.Chinese Acad Sci, Inst Automat, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Li, Luoqing,Li, Weifu,Zou, Bin,et al. Learning With Coefficient-Based Regularized Regression on Markov Resampling[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2018,29(9):4166-4176.
APA Li, Luoqing,Li, Weifu,Zou, Bin,Wang, Yulong,Tang, Yuan Yan,&Han, Hua.(2018).Learning With Coefficient-Based Regularized Regression on Markov Resampling.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,29(9),4166-4176.
MLA Li, Luoqing,et al."Learning With Coefficient-Based Regularized Regression on Markov Resampling".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 29.9(2018):4166-4176.
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