Learning Rates for -Regularized Kernel Classifiers
We consider a family of classification algorithms generated from a regularization kernel scheme associated with -regularizer and convex loss function. Our main purpose is to provide an explicit convergence rate for the excess misclassification error of the produced classifiers. The error decompositi...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2013/496282 |
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| _version_ | 1850172603571896320 |
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| author | Hongzhi Tong Di-Rong Chen Fenghong Yang |
| author_facet | Hongzhi Tong Di-Rong Chen Fenghong Yang |
| author_sort | Hongzhi Tong |
| collection | DOAJ |
| description | We consider a family of classification algorithms generated from a regularization kernel scheme associated with -regularizer and convex loss function. Our main purpose is to provide an explicit convergence rate for the excess misclassification error of the produced classifiers. The error decomposition includes approximation error, hypothesis error, and sample error. We apply some novel techniques to estimate the hypothesis error and sample error. Learning rates are eventually derived under some assumptions on the kernel, the input space, the marginal distribution, and the approximation error. |
| format | Article |
| id | doaj-art-d0a69bfed8dd4f57803fe8975da1f3ff |
| institution | OA Journals |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-d0a69bfed8dd4f57803fe8975da1f3ff2025-08-20T02:20:02ZengWileyJournal of Applied Mathematics1110-757X1687-00422013-01-01201310.1155/2013/496282496282Learning Rates for -Regularized Kernel ClassifiersHongzhi Tong0Di-Rong Chen1Fenghong Yang2School of Statistics, University of International Business and Economics, Beijing 100029, ChinaDepartment of Mathematics and LMIB, Beijing University of Aeronautics and Astronautics, Beijing 100083, ChinaSchool of Applied Mathematics, Central University of Finance and Economics, Beijing 100081, ChinaWe consider a family of classification algorithms generated from a regularization kernel scheme associated with -regularizer and convex loss function. Our main purpose is to provide an explicit convergence rate for the excess misclassification error of the produced classifiers. The error decomposition includes approximation error, hypothesis error, and sample error. We apply some novel techniques to estimate the hypothesis error and sample error. Learning rates are eventually derived under some assumptions on the kernel, the input space, the marginal distribution, and the approximation error.http://dx.doi.org/10.1155/2013/496282 |
| spellingShingle | Hongzhi Tong Di-Rong Chen Fenghong Yang Learning Rates for -Regularized Kernel Classifiers Journal of Applied Mathematics |
| title | Learning Rates for -Regularized Kernel Classifiers |
| title_full | Learning Rates for -Regularized Kernel Classifiers |
| title_fullStr | Learning Rates for -Regularized Kernel Classifiers |
| title_full_unstemmed | Learning Rates for -Regularized Kernel Classifiers |
| title_short | Learning Rates for -Regularized Kernel Classifiers |
| title_sort | learning rates for regularized kernel classifiers |
| url | http://dx.doi.org/10.1155/2013/496282 |
| work_keys_str_mv | AT hongzhitong learningratesforregularizedkernelclassifiers AT dirongchen learningratesforregularizedkernelclassifiers AT fenghongyang learningratesforregularizedkernelclassifiers |