A Complete Investigation of Using Weighted Kernel Regression for The Case of Small Sample Problem With Noise
Weighted kernel regression (WKR) is a kernel-based regression approach for small sample problems. Previously, for the case of small sample problems with noise, we have done preliminary studies which investigated different learning techniques and different learning functions, separately. In this pa...
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2015
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Online Access: | http://umpir.ump.edu.my/id/eprint/11347/1/A%20Complete%20Investigation%20of%20Using%20Weighted%20Kernel%20Regression%20for%20the%20Case%20of%20Small%20Sample%20Problem%20With%20Noise.pdf http://umpir.ump.edu.my/id/eprint/11347/ |
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my.ump.umpir.113472018-02-08T03:22:18Z http://umpir.ump.edu.my/id/eprint/11347/ A Complete Investigation of Using Weighted Kernel Regression for The Case of Small Sample Problem With Noise Zuwairie, Ibrahim Mohd Ibrahim, Shapiai Siti Nurzulaikha, Satiman Mohd Saberi, Mohamad Nurul Wahidah, Arshad TK Electrical engineering. Electronics Nuclear engineering Weighted kernel regression (WKR) is a kernel-based regression approach for small sample problems. Previously, for the case of small sample problems with noise, we have done preliminary studies which investigated different learning techniques and different learning functions, separately. In this paper, a complete investigation of using WKR for the case of noisy and small training samples is presented. Analysis and discussion are provided in detail. 2015 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/11347/1/A%20Complete%20Investigation%20of%20Using%20Weighted%20Kernel%20Regression%20for%20the%20Case%20of%20Small%20Sample%20Problem%20With%20Noise.pdf Zuwairie, Ibrahim and Mohd Ibrahim, Shapiai and Siti Nurzulaikha, Satiman and Mohd Saberi, Mohamad and Nurul Wahidah, Arshad (2015) A Complete Investigation of Using Weighted Kernel Regression for The Case of Small Sample Problem With Noise. In: International Conferences on Electrical, Control and Computer Engineering (INECCE2015), 27-28 Oct 2015 , Kuantan, Pahang. . (Unpublished) |
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TK Electrical engineering. Electronics Nuclear engineering Zuwairie, Ibrahim Mohd Ibrahim, Shapiai Siti Nurzulaikha, Satiman Mohd Saberi, Mohamad Nurul Wahidah, Arshad A Complete Investigation of Using Weighted Kernel Regression for The Case of Small Sample Problem With Noise |
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Weighted kernel regression (WKR) is a kernel-based regression approach for small sample problems. Previously,
for the case of small sample problems with noise, we have done preliminary studies which investigated different learning
techniques and different learning functions, separately. In this paper, a complete investigation of using WKR for the case of
noisy and small training samples is presented. Analysis and discussion are provided in detail. |
format |
Conference or Workshop Item |
author |
Zuwairie, Ibrahim Mohd Ibrahim, Shapiai Siti Nurzulaikha, Satiman Mohd Saberi, Mohamad Nurul Wahidah, Arshad |
author_facet |
Zuwairie, Ibrahim Mohd Ibrahim, Shapiai Siti Nurzulaikha, Satiman Mohd Saberi, Mohamad Nurul Wahidah, Arshad |
author_sort |
Zuwairie, Ibrahim |
title |
A Complete Investigation of Using Weighted Kernel Regression for The Case of Small Sample Problem With Noise |
title_short |
A Complete Investigation of Using Weighted Kernel Regression for The Case of Small Sample Problem With Noise |
title_full |
A Complete Investigation of Using Weighted Kernel Regression for The Case of Small Sample Problem With Noise |
title_fullStr |
A Complete Investigation of Using Weighted Kernel Regression for The Case of Small Sample Problem With Noise |
title_full_unstemmed |
A Complete Investigation of Using Weighted Kernel Regression for The Case of Small Sample Problem With Noise |
title_sort |
complete investigation of using weighted kernel regression for the case of small sample problem with noise |
publishDate |
2015 |
url |
http://umpir.ump.edu.my/id/eprint/11347/1/A%20Complete%20Investigation%20of%20Using%20Weighted%20Kernel%20Regression%20for%20the%20Case%20of%20Small%20Sample%20Problem%20With%20Noise.pdf http://umpir.ump.edu.my/id/eprint/11347/ |
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13.209306 |