Theory Analysis for the Convergence of Kernel-Regularized Online Binary Classification Learning Associated with RKBSs
It is known that more and more mathematicians have paid their attention to the field of learning with a Banach space since Banach spaces may provide abundant inner-product structures. We give investigations on the convergence of a kernel-regularized online binary classification learning algorithm in...
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2023/6566375 |
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| _version_ | 1850175000496046080 |
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| author | Lin Liu Xiaoling Pan Baohuai Sheng |
| author_facet | Lin Liu Xiaoling Pan Baohuai Sheng |
| author_sort | Lin Liu |
| collection | DOAJ |
| description | It is known that more and more mathematicians have paid their attention to the field of learning with a Banach space since Banach spaces may provide abundant inner-product structures. We give investigations on the convergence of a kernel-regularized online binary classification learning algorithm in the setting of reproducing kernel Banach spaces (RKBSs), design an online iteration algorithm with the subdifferential of the norm and the logistic loss, and provide an upper bound for the learning rate, which shows that the online learning algorithm converges if RKBSs satisfy 2-uniform convexity. |
| format | Article |
| id | doaj-art-d741697bb1eb4927b61ce2d1d8a2ffc8 |
| institution | OA Journals |
| issn | 2314-4785 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Mathematics |
| spelling | doaj-art-d741697bb1eb4927b61ce2d1d8a2ffc82025-08-20T02:19:33ZengWileyJournal of Mathematics2314-47852023-01-01202310.1155/2023/6566375Theory Analysis for the Convergence of Kernel-Regularized Online Binary Classification Learning Associated with RKBSsLin Liu0Xiaoling Pan1Baohuai Sheng2School of Mathematical Physics and InformationSchool of Mathematical Physics and InformationSchool of Mathematical Physics and InformationIt is known that more and more mathematicians have paid their attention to the field of learning with a Banach space since Banach spaces may provide abundant inner-product structures. We give investigations on the convergence of a kernel-regularized online binary classification learning algorithm in the setting of reproducing kernel Banach spaces (RKBSs), design an online iteration algorithm with the subdifferential of the norm and the logistic loss, and provide an upper bound for the learning rate, which shows that the online learning algorithm converges if RKBSs satisfy 2-uniform convexity.http://dx.doi.org/10.1155/2023/6566375 |
| spellingShingle | Lin Liu Xiaoling Pan Baohuai Sheng Theory Analysis for the Convergence of Kernel-Regularized Online Binary Classification Learning Associated with RKBSs Journal of Mathematics |
| title | Theory Analysis for the Convergence of Kernel-Regularized Online Binary Classification Learning Associated with RKBSs |
| title_full | Theory Analysis for the Convergence of Kernel-Regularized Online Binary Classification Learning Associated with RKBSs |
| title_fullStr | Theory Analysis for the Convergence of Kernel-Regularized Online Binary Classification Learning Associated with RKBSs |
| title_full_unstemmed | Theory Analysis for the Convergence of Kernel-Regularized Online Binary Classification Learning Associated with RKBSs |
| title_short | Theory Analysis for the Convergence of Kernel-Regularized Online Binary Classification Learning Associated with RKBSs |
| title_sort | theory analysis for the convergence of kernel regularized online binary classification learning associated with rkbss |
| url | http://dx.doi.org/10.1155/2023/6566375 |
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