The Generalization Error Bound for the Multiclass Analytical Center Classifier
This paper presents the multiclass classifier based on analytical center of feasible space (MACM). This multiclass classifier is formulated as quadratic constrained linear optimization and does not need repeatedly constructing classifiers to separate a single class from all the others. Its generaliz...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2013/574748 |
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| _version_ | 1832564594536611840 |
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| author | Zeng Fanzi Ma Xiaolong |
| author_facet | Zeng Fanzi Ma Xiaolong |
| author_sort | Zeng Fanzi |
| collection | DOAJ |
| description | This paper presents the multiclass classifier based on analytical center of feasible space (MACM). This multiclass classifier is formulated as quadratic constrained linear optimization and does not need repeatedly constructing classifiers to separate a single class from all the others. Its generalization error upper bound is proved theoretically. The experiments on benchmark datasets validate the generalization performance of MACM. |
| format | Article |
| id | doaj-art-2338944574f44a27b27293f79101fa33 |
| institution | Kabale University |
| issn | 1537-744X |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-2338944574f44a27b27293f79101fa332025-02-03T01:10:35ZengWileyThe Scientific World Journal1537-744X2013-01-01201310.1155/2013/574748574748The Generalization Error Bound for the Multiclass Analytical Center ClassifierZeng Fanzi0Ma Xiaolong1Key Laboratory for Embedded and Network Computing of Hunan Province, Hunan University, Changsha 410082, ChinaKey Laboratory for Embedded and Network Computing of Hunan Province, Hunan University, Changsha 410082, ChinaThis paper presents the multiclass classifier based on analytical center of feasible space (MACM). This multiclass classifier is formulated as quadratic constrained linear optimization and does not need repeatedly constructing classifiers to separate a single class from all the others. Its generalization error upper bound is proved theoretically. The experiments on benchmark datasets validate the generalization performance of MACM.http://dx.doi.org/10.1155/2013/574748 |
| spellingShingle | Zeng Fanzi Ma Xiaolong The Generalization Error Bound for the Multiclass Analytical Center Classifier The Scientific World Journal |
| title | The Generalization Error Bound for the Multiclass Analytical Center Classifier |
| title_full | The Generalization Error Bound for the Multiclass Analytical Center Classifier |
| title_fullStr | The Generalization Error Bound for the Multiclass Analytical Center Classifier |
| title_full_unstemmed | The Generalization Error Bound for the Multiclass Analytical Center Classifier |
| title_short | The Generalization Error Bound for the Multiclass Analytical Center Classifier |
| title_sort | generalization error bound for the multiclass analytical center classifier |
| url | http://dx.doi.org/10.1155/2013/574748 |
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