Regularized Least Square Regression with Unbounded and Dependent Sampling
This paper mainly focuses on the least square regression problem for the -mixing and -mixing processes. The standard bound assumption for output data is abandoned and the learning algorithm is implemented with samples drawn from dependent sampling process with a more general output data condition. C...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2013/139318 |
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| _version_ | 1849402266157580288 |
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| author | Xiaorong Chu Hongwei Sun |
| author_facet | Xiaorong Chu Hongwei Sun |
| author_sort | Xiaorong Chu |
| collection | DOAJ |
| description | This paper mainly focuses on the least square regression problem for the -mixing and -mixing processes. The standard bound assumption for output data is abandoned and the learning algorithm is implemented with samples drawn from dependent sampling process with a more general output data condition. Capacity independent error bounds and learning rates are deduced by means of the integral operator technique. |
| format | Article |
| id | doaj-art-8b609da2ac3d44528f6d70e00e44521e |
| institution | Kabale University |
| issn | 1085-3375 1687-0409 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Abstract and Applied Analysis |
| spelling | doaj-art-8b609da2ac3d44528f6d70e00e44521e2025-08-20T03:37:34ZengWileyAbstract and Applied Analysis1085-33751687-04092013-01-01201310.1155/2013/139318139318Regularized Least Square Regression with Unbounded and Dependent SamplingXiaorong Chu0Hongwei Sun1School of Mathematical Science, University of Jinan, Shandong Provincial Key Laboratory of Network Based Intelligent Computing, Jinan 250022, ChinaSchool of Mathematical Science, University of Jinan, Shandong Provincial Key Laboratory of Network Based Intelligent Computing, Jinan 250022, ChinaThis paper mainly focuses on the least square regression problem for the -mixing and -mixing processes. The standard bound assumption for output data is abandoned and the learning algorithm is implemented with samples drawn from dependent sampling process with a more general output data condition. Capacity independent error bounds and learning rates are deduced by means of the integral operator technique.http://dx.doi.org/10.1155/2013/139318 |
| spellingShingle | Xiaorong Chu Hongwei Sun Regularized Least Square Regression with Unbounded and Dependent Sampling Abstract and Applied Analysis |
| title | Regularized Least Square Regression with Unbounded and Dependent Sampling |
| title_full | Regularized Least Square Regression with Unbounded and Dependent Sampling |
| title_fullStr | Regularized Least Square Regression with Unbounded and Dependent Sampling |
| title_full_unstemmed | Regularized Least Square Regression with Unbounded and Dependent Sampling |
| title_short | Regularized Least Square Regression with Unbounded and Dependent Sampling |
| title_sort | regularized least square regression with unbounded and dependent sampling |
| url | http://dx.doi.org/10.1155/2013/139318 |
| work_keys_str_mv | AT xiaorongchu regularizedleastsquareregressionwithunboundedanddependentsampling AT hongweisun regularizedleastsquareregressionwithunboundedanddependentsampling |