A review on optimization of least squares support vector machine for time series forecasting
Support Vector Machine has appeared as an active study in machine learning community and extensively used in various fields including in prediction, pattern recognition and many more. However, the Least Squares Support Vector Machine which is a variant of Support Vector Machine offers better solut...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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AIRCC Publishing Corporation
2016
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| Online Access: | https://repo.uum.edu.my/id/eprint/18308/1/IJAIA%207%202%202016%2035-49.pdf |
| _version_ | 1825804016157720576 |
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| author | Yusof, Yuhanis Mustaffa, Zuriani |
| author_facet | Yusof, Yuhanis Mustaffa, Zuriani |
| author_sort | Yusof, Yuhanis |
| collection | UUM |
| description | Support Vector Machine has appeared as an active study in machine learning community and extensively
used in various fields including in prediction, pattern recognition and many more. However, the Least
Squares Support Vector Machine which is a variant of Support Vector Machine offers better solution
strategy. In order to utilize the LSSVM capability in data mining task such as prediction, there is a need to optimize its hyper parameters. This paper presents a review on techniques used to optimize the parameters based on two main classes; Evolutionary Computation and Cross Validation. |
| first_indexed | 2024-07-04T06:07:30Z |
| format | Article |
| id | uum-18308 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T06:07:30Z |
| publishDate | 2016 |
| publisher | AIRCC Publishing Corporation |
| record_format | eprints |
| spelling | uum-183082016-08-09T07:59:22Z https://repo.uum.edu.my/id/eprint/18308/ A review on optimization of least squares support vector machine for time series forecasting Yusof, Yuhanis Mustaffa, Zuriani QA76 Computer software Support Vector Machine has appeared as an active study in machine learning community and extensively used in various fields including in prediction, pattern recognition and many more. However, the Least Squares Support Vector Machine which is a variant of Support Vector Machine offers better solution strategy. In order to utilize the LSSVM capability in data mining task such as prediction, there is a need to optimize its hyper parameters. This paper presents a review on techniques used to optimize the parameters based on two main classes; Evolutionary Computation and Cross Validation. AIRCC Publishing Corporation 2016 Article PeerReviewed application/pdf en https://repo.uum.edu.my/id/eprint/18308/1/IJAIA%207%202%202016%2035-49.pdf Yusof, Yuhanis and Mustaffa, Zuriani (2016) A review on optimization of least squares support vector machine for time series forecasting. International Journal of Artificial Intelligence & Applications, 7 (2). pp. 35-49. ISSN 0976-2191 http://doi.org/10.5121/ijaia.2016.7203 doi:10.5121/ijaia.2016.7203 doi:10.5121/ijaia.2016.7203 |
| spellingShingle | QA76 Computer software Yusof, Yuhanis Mustaffa, Zuriani A review on optimization of least squares support vector machine for time series forecasting |
| title | A review on optimization of least squares support vector machine for time series forecasting |
| title_full | A review on optimization of least squares support vector machine for time series forecasting |
| title_fullStr | A review on optimization of least squares support vector machine for time series forecasting |
| title_full_unstemmed | A review on optimization of least squares support vector machine for time series forecasting |
| title_short | A review on optimization of least squares support vector machine for time series forecasting |
| title_sort | review on optimization of least squares support vector machine for time series forecasting |
| topic | QA76 Computer software |
| url | https://repo.uum.edu.my/id/eprint/18308/1/IJAIA%207%202%202016%2035-49.pdf |
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