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Fast Learning With Polynomial Kernels
Lin, Shaobo1,2; Zeng, Jinshan3
作者部门机器人学研究室
关键词Kernel Methods Learning Systems Learning Theory Polynomial Kernel
发表期刊IEEE Transactions on Cybernetics
ISSN2168-2267
2018
页码1-13
收录类别EI
EI收录号20182905567539
产权排序1
资助机构National Natural Science Foundation of China under Grant 61502342 and Grant 11771012, and in part by the State Key Laboratory of Robotics (2018-O05) ; NNSFC under Grant 61603162, Grant 11501440, Grant 61772246, and Grant 61603163, and in part by the Doctoral Start-Up Foundation of Jiangxi Normal University
摘要

This paper proposes a new learning system of low computational cost, called fast polynomial kernel learning (FPL), based on regularized least squares with polynomial kernel and subsampling. The almost optimal learning rate as well as the feasibility verifications including the subsampling mechanism and solvability of FPL are provided in the framework of learning theory. Our theoretical assertions are verified by numerous toy simulations and real data applications. The studies in this paper show that FPL can reduce the computational burden of kernel methods without sacrificing its generalization ability very much.

语种英语
文献类型期刊论文
条目标识符http://ir.sia.cn/handle/173321/22139
专题机器人学研究室
通讯作者Zeng, Jinshan
作者单位1.Department of Mathematics, Wenzhou University, Wenzhou 325035, China
2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
3.School of Computer and Information Engineering, Jiangxi Normal University, Nanchang 330022, China
推荐引用方式
GB/T 7714
Lin, Shaobo,Zeng, Jinshan. Fast Learning With Polynomial Kernels[J]. IEEE Transactions on Cybernetics,2018:1-13.
APA Lin, Shaobo,&Zeng, Jinshan.(2018).Fast Learning With Polynomial Kernels.IEEE Transactions on Cybernetics,1-13.
MLA Lin, Shaobo,et al."Fast Learning With Polynomial Kernels".IEEE Transactions on Cybernetics (2018):1-13.
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