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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 pape...

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Main Authors: Shapiai, Mohd. Ibrahim, Mohamad, Mohd. Saberi, Satiman, Siti Nurzulaikha, Arshad, Nurul Wahidah, Ibrahim, Zuwairie
Format: Article
Published: Asian Research Publishing Network 2015
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/60074/
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spelling my.utm.600742021-08-15T09:33:15Z http://eprints.utm.my/id/eprint/60074/ A complete investigation of using weighted kernel regression for the case of small sample problem with noise Shapiai, Mohd. Ibrahim Mohamad, Mohd. Saberi Satiman, Siti Nurzulaikha Arshad, Nurul Wahidah Ibrahim, Zuwairie QA75 Electronic computers. Computer science 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. Asian Research Publishing Network 2015 Article PeerReviewed Shapiai, Mohd. Ibrahim and Mohamad, Mohd. Saberi and Satiman, Siti Nurzulaikha and Arshad, Nurul Wahidah and Ibrahim, Zuwairie (2015) A complete investigation of using weighted kernel regression for the case of small sample problem with noise. ARPN Journal of Engineering and Applied Sciences, 10 (23). pp. 17514-17520. ISSN 1819-6608
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Shapiai, Mohd. Ibrahim
Mohamad, Mohd. Saberi
Satiman, Siti Nurzulaikha
Arshad, Nurul Wahidah
Ibrahim, Zuwairie
A complete investigation of using weighted kernel regression for the case of small sample problem with noise
description 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 Article
author Shapiai, Mohd. Ibrahim
Mohamad, Mohd. Saberi
Satiman, Siti Nurzulaikha
Arshad, Nurul Wahidah
Ibrahim, Zuwairie
author_facet Shapiai, Mohd. Ibrahim
Mohamad, Mohd. Saberi
Satiman, Siti Nurzulaikha
Arshad, Nurul Wahidah
Ibrahim, Zuwairie
author_sort Shapiai, Mohd. 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
publisher Asian Research Publishing Network
publishDate 2015
url http://eprints.utm.my/id/eprint/60074/
_version_ 1709667358480531456
score 13.18916

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