Investigation on different learning techniques for weighted kernel regression in solving small sample problem with noise
Previously, weighted kernel regression (WKR) for solving small samples problem has been reported. The enhanced WKR has also been proposed with focus on the improvement of the learning techniques and learning functions. In the existing study, the investigation of the learning techniques and learning...
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| Format: | Article |
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ICIC Express Letters Office
2015
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| _version_ | 1796860315384676352 |
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| author | Shapiai, Mohd. Ibrahim Sudin, Shahdan Arshad, Nurul Wahidah Ibrahim, Zuwairie |
| author_facet | Shapiai, Mohd. Ibrahim Sudin, Shahdan Arshad, Nurul Wahidah Ibrahim, Zuwairie |
| author_sort | Shapiai, Mohd. Ibrahim |
| collection | ePrints |
| description | Previously, weighted kernel regression (WKR) for solving small samples problem has been reported. The enhanced WKR has also been proposed with focus on the improvement of the learning techniques and learning functions. In the existing study, the investigation of the learning techniques and learning functions of the WKR is investigated separately. Hence, in this study, one particular learning function that is appropriate to address noisy samples problem with several learning techniques is employed in one investigation framework. The employed learning function is defined as closed form solution function which consists of the error term and regularization term in L2-norm term. For this purpose, an iteration technique, a Ridge Regression (RR) and genetic algorithm (GA) are used as learning techniques to estimate the weight parameters of the WKR for the formulated learning function. Through a number of computational experiments, it is found that RR and GA can offer better prediction accuracy as compared to iteration technique when solving noisy and small sample problems. |
| first_indexed | 2024-03-05T19:39:32Z |
| format | Article |
| id | utm.eprints-56066 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T19:39:32Z |
| publishDate | 2015 |
| publisher | ICIC Express Letters Office |
| record_format | dspace |
| spelling | utm.eprints-560662017-09-14T03:36:14Z http://eprints.utm.my/56066/ Investigation on different learning techniques for weighted kernel regression in solving small sample problem with noise Shapiai, Mohd. Ibrahim Sudin, Shahdan Arshad, Nurul Wahidah Ibrahim, Zuwairie TK Electrical engineering. Electronics Nuclear engineering Previously, weighted kernel regression (WKR) for solving small samples problem has been reported. The enhanced WKR has also been proposed with focus on the improvement of the learning techniques and learning functions. In the existing study, the investigation of the learning techniques and learning functions of the WKR is investigated separately. Hence, in this study, one particular learning function that is appropriate to address noisy samples problem with several learning techniques is employed in one investigation framework. The employed learning function is defined as closed form solution function which consists of the error term and regularization term in L2-norm term. For this purpose, an iteration technique, a Ridge Regression (RR) and genetic algorithm (GA) are used as learning techniques to estimate the weight parameters of the WKR for the formulated learning function. Through a number of computational experiments, it is found that RR and GA can offer better prediction accuracy as compared to iteration technique when solving noisy and small sample problems. ICIC Express Letters Office 2015-01 Article PeerReviewed Shapiai, Mohd. Ibrahim and Sudin, Shahdan and Arshad, Nurul Wahidah and Ibrahim, Zuwairie (2015) Investigation on different learning techniques for weighted kernel regression in solving small sample problem with noise. ICIC Express Letters, 9 (4). pp. 965-971. ISSN 1881-803X https://pure.utm.my/en/publications/investigation-on-different-learning-techniques-for-weighted-kerne |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Shapiai, Mohd. Ibrahim Sudin, Shahdan Arshad, Nurul Wahidah Ibrahim, Zuwairie Investigation on different learning techniques for weighted kernel regression in solving small sample problem with noise |
| title | Investigation on different learning techniques for weighted kernel regression in solving small sample problem with noise |
| title_full | Investigation on different learning techniques for weighted kernel regression in solving small sample problem with noise |
| title_fullStr | Investigation on different learning techniques for weighted kernel regression in solving small sample problem with noise |
| title_full_unstemmed | Investigation on different learning techniques for weighted kernel regression in solving small sample problem with noise |
| title_short | Investigation on different learning techniques for weighted kernel regression in solving small sample problem with noise |
| title_sort | investigation on different learning techniques for weighted kernel regression in solving small sample problem with noise |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| work_keys_str_mv | AT shapiaimohdibrahim investigationondifferentlearningtechniquesforweightedkernelregressioninsolvingsmallsampleproblemwithnoise AT sudinshahdan investigationondifferentlearningtechniquesforweightedkernelregressioninsolvingsmallsampleproblemwithnoise AT arshadnurulwahidah investigationondifferentlearningtechniquesforweightedkernelregressioninsolvingsmallsampleproblemwithnoise AT ibrahimzuwairie investigationondifferentlearningtechniquesforweightedkernelregressioninsolvingsmallsampleproblemwithnoise |