A hybrid technique for selecting support vector regression parameters based on a practical selection method and grid search procedure
In order to enhance the generalization ability of the practical selection (PLSN) method for choosing the optimal parameters of the support vector regression (SVR) model that was proposed by Cherkassky and Ma (2004), we investigate a new hybrid technique that combines the PLSN method and the grid sea...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Academy of Economic Studies
2016
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| Online Access: | http://psasir.upm.edu.my/id/eprint/14224/ http://psasir.upm.edu.my/id/eprint/14224/1/A%20hybrid%20technique%20for%20selecting%20support%20vector%20regression%20parameters%20based%20on%20a%20practical%20selection%20method%20and%20grid%20search%20procedure.pdf |
| _version_ | 1848842334826921984 |
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| author | Rana, Sohel Dhhan, Waleed Midi, Habshah |
| author_facet | Rana, Sohel Dhhan, Waleed Midi, Habshah |
| author_sort | Rana, Sohel |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | In order to enhance the generalization ability of the practical selection (PLSN) method for choosing the optimal parameters of the support vector regression (SVR) model that was proposed by Cherkassky and Ma (2004), we investigate a new hybrid technique that combines the PLSN method and the grid search procedure. We explore this and find it to be suitable for different types of additive noise including Laplacian noise density. We show that the proposed parameter selection for SVR achieves a good generalization performance by testing several regression problems (low-and high-dimensional data). Moreover, the proposed method is effective for finding the optimal parameters of SVR for all kinds of noise, including Laplacian noise. The generalization performance of the proposed method is compared with that of the PLSN method, with some numerical studies for Gaussian noise as well as non-Gaussian noise. The results show that the proposed method is superior to the PLSN method for various types of noise. |
| first_indexed | 2025-11-15T07:57:29Z |
| format | Article |
| id | upm-14224 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T07:57:29Z |
| publishDate | 2016 |
| publisher | Academy of Economic Studies |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-142242018-10-08T02:45:47Z http://psasir.upm.edu.my/id/eprint/14224/ A hybrid technique for selecting support vector regression parameters based on a practical selection method and grid search procedure Rana, Sohel Dhhan, Waleed Midi, Habshah In order to enhance the generalization ability of the practical selection (PLSN) method for choosing the optimal parameters of the support vector regression (SVR) model that was proposed by Cherkassky and Ma (2004), we investigate a new hybrid technique that combines the PLSN method and the grid search procedure. We explore this and find it to be suitable for different types of additive noise including Laplacian noise density. We show that the proposed parameter selection for SVR achieves a good generalization performance by testing several regression problems (low-and high-dimensional data). Moreover, the proposed method is effective for finding the optimal parameters of SVR for all kinds of noise, including Laplacian noise. The generalization performance of the proposed method is compared with that of the PLSN method, with some numerical studies for Gaussian noise as well as non-Gaussian noise. The results show that the proposed method is superior to the PLSN method for various types of noise. Academy of Economic Studies 2016 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/14224/1/A%20hybrid%20technique%20for%20selecting%20support%20vector%20regression%20parameters%20based%20on%20a%20practical%20selection%20method%20and%20grid%20search%20procedure.pdf Rana, Sohel and Dhhan, Waleed and Midi, Habshah (2016) A hybrid technique for selecting support vector regression parameters based on a practical selection method and grid search procedure. Economic Computation and Economic Cybernetics Studies and Research, 50 (2). pp. 231-246. ISSN 0424-267X; ESSN: 1842-3264 http://www.ecocyb.ase.ro/Articles2016_2.htm |
| spellingShingle | Rana, Sohel Dhhan, Waleed Midi, Habshah A hybrid technique for selecting support vector regression parameters based on a practical selection method and grid search procedure |
| title | A hybrid technique for selecting support vector regression parameters based on a practical selection method and grid search procedure |
| title_full | A hybrid technique for selecting support vector regression parameters based on a practical selection method and grid search procedure |
| title_fullStr | A hybrid technique for selecting support vector regression parameters based on a practical selection method and grid search procedure |
| title_full_unstemmed | A hybrid technique for selecting support vector regression parameters based on a practical selection method and grid search procedure |
| title_short | A hybrid technique for selecting support vector regression parameters based on a practical selection method and grid search procedure |
| title_sort | hybrid technique for selecting support vector regression parameters based on a practical selection method and grid search procedure |
| url | http://psasir.upm.edu.my/id/eprint/14224/ http://psasir.upm.edu.my/id/eprint/14224/ http://psasir.upm.edu.my/id/eprint/14224/1/A%20hybrid%20technique%20for%20selecting%20support%20vector%20regression%20parameters%20based%20on%20a%20practical%20selection%20method%20and%20grid%20search%20procedure.pdf |