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Rescaled Boosting in Classification
Wang Y(王尧)1,2,3; Liao, Xu4; Lin SB(林绍波)3,5
Department机器人学研究室
Corresponding AuthorLi, Shaobo(sblin1983@gmail.com)
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN2162-237X
2019
Volume30Issue:9Pages:2598-2610
Indexed BySCI
WOS IDWOS:000482589400003
Contribution Rank1
Funding OrganizationNational Natural Science Foundation of China [11501440, 61876133, 91546119] ; China Postdoctoral Science Foundation [2017M610628, 2018T111031] ; Key Research Program of Hunan Province, China [2017GK2273] ; State Key Laboratory of Robotics [2018-O05]
KeywordBoosting generalization error numerical convergence rate resealed boosting (RBoosting)
AbstractBoosting is a learning scheme that combines weak learners to produce a strong composite learner, with the underlying intuition that one can obtain accurate learner by combining "rough" ones. This paper aims at developing a new boosting strategy, called resealed boosting (RBoosting), to accelerate the numerical convergence rate and, consequently, improve learning performances of the original boosting. Our studies show that RBoosting possesses the almost optimal numerical convergence rate in the sense that, up to a logarithmic factor, it can reach the minimax nonlinear approximation rate. We then use RBoosting to tackle classification problems and deduce corresponding statistical consistency and tight generalization error estimates. A series of' theoretical and experimental results shows that RBoosting outperforms boosting in terms of generalization.
Language英语
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS KeywordREGRESSION ; APPROXIMATION ; CONVERGENCE ; ALGORITHMS ; STRENGTH
WOS Research AreaComputer Science ; Engineering
Funding ProjectNational Natural Science Foundation of China[11501440] ; National Natural Science Foundation of China[61876133] ; National Natural Science Foundation of China[91546119] ; China Postdoctoral Science Foundation[2017M610628] ; China Postdoctoral Science Foundation[2018T111031] ; Key Research Program of Hunan Province, China[2017GK2273] ; State Key Laboratory of Robotics[2018-O05]
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Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/25612
Collection机器人学研究室
Corresponding AuthorLin SB(林绍波)
Affiliation1.School of Management, Xi’an Jiaotong University, Xi’an 710049, China
2.Research Institute for Mathematics and Mathematical Technology, Xi’an Jiaotong University, Xi’an 710049, China
3.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
4.School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an 710049, China
5.Department of Mathematics, Wenzhou University, Wenzhou 325035, China
Recommended Citation
GB/T 7714
Wang Y,Liao, Xu,Lin SB. Rescaled Boosting in Classification[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2019,30(9):2598-2610.
APA Wang Y,Liao, Xu,&Lin SB.(2019).Rescaled Boosting in Classification.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,30(9),2598-2610.
MLA Wang Y,et al."Rescaled Boosting in Classification".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 30.9(2019):2598-2610.
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