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Fast Learning With Polynomial Kernels
Lin, Shaobo1,2; Zeng, Jinshan3
Source PublicationIEEE Transactions on Cybernetics
Indexed ByEI
EI Accession number20182905567539
Contribution Rank1
Funding OrganizationNational 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
KeywordKernel Methods Learning Systems Learning Theory Polynomial Kernel

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.

Document Type期刊论文
Corresponding AuthorZeng, Jinshan
Affiliation1.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
Recommended Citation
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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