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Enhanced weighted kernel regression with prior knowledge in solving small sample problems

Previously, weighted kernel regression (WKR) for solving small samples problem has been reported. In general, WKR has proven to be effective when learning from small samples as compared to artificial neural network with back-propagation (ANNBP) and some other techniques. In order to extend the capab...

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Main Authors: Shaipai, Mohd. Ibrahim, Sudin, Shahdan, Ibrahim, Zuwairie, Khalid, Marzuki
Format: Conference or Workshop Item
Published: 2011
Online Access:http://eprints.utm.my/id/eprint/45825/
http://dx.doi.org/10.1016/j.proeng.2012.07.146
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spelling my.utm.458252017-08-29T03:40:52Z http://eprints.utm.my/id/eprint/45825/ Enhanced weighted kernel regression with prior knowledge in solving small sample problems Shaipai, Mohd. Ibrahim Sudin, Shahdan Ibrahim, Zuwairie Khalid, Marzuki Previously, weighted kernel regression (WKR) for solving small samples problem has been reported. In general, WKR has proven to be effective when learning from small samples as compared to artificial neural network with back-propagation (ANNBP) and some other techniques. In order to extend the capability of the technique, we introduce a new approach to improve the WKR by incorporating the prior knowledge. In practice, different forms of prior knowledge may be available and it might avoid the weakness of the training samples limitation. In this study, the incorporation of the prior knowledge will produce a set of solutions by considering the available training samples and prior knowledge in modeling. The process involved in obtaining a set of solutions can be regarded as a bi-objective optimization problem. The proposed technique is derived based on the pareto optimality concept (POC) by using multi-objective optimization technique (MOPT). We only focus the study on the challenges of formulating the two objective functions. We demonstrate the capability of the proposed technique to robot manipulator problem. It is shown that the incorporation of the prior knowledge based on POC can be implemented and relatively improved the regression performance. Some related issues of the proposed technique are also discussed. 2011 Conference or Workshop Item PeerReviewed Shaipai, Mohd. Ibrahim and Sudin, Shahdan and Ibrahim, Zuwairie and Khalid, Marzuki (2011) Enhanced weighted kernel regression with prior knowledge in solving small sample problems. In: CIMSIM 2011 - 3rd International Conference On Computational Intelligence, Modelling & Simulation, CIMSIM 2011. http://dx.doi.org/10.1016/j.proeng.2012.07.146
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/
description Previously, weighted kernel regression (WKR) for solving small samples problem has been reported. In general, WKR has proven to be effective when learning from small samples as compared to artificial neural network with back-propagation (ANNBP) and some other techniques. In order to extend the capability of the technique, we introduce a new approach to improve the WKR by incorporating the prior knowledge. In practice, different forms of prior knowledge may be available and it might avoid the weakness of the training samples limitation. In this study, the incorporation of the prior knowledge will produce a set of solutions by considering the available training samples and prior knowledge in modeling. The process involved in obtaining a set of solutions can be regarded as a bi-objective optimization problem. The proposed technique is derived based on the pareto optimality concept (POC) by using multi-objective optimization technique (MOPT). We only focus the study on the challenges of formulating the two objective functions. We demonstrate the capability of the proposed technique to robot manipulator problem. It is shown that the incorporation of the prior knowledge based on POC can be implemented and relatively improved the regression performance. Some related issues of the proposed technique are also discussed.
format Conference or Workshop Item
author Shaipai, Mohd. Ibrahim
Sudin, Shahdan
Ibrahim, Zuwairie
Khalid, Marzuki
spellingShingle Shaipai, Mohd. Ibrahim
Sudin, Shahdan
Ibrahim, Zuwairie
Khalid, Marzuki
Enhanced weighted kernel regression with prior knowledge in solving small sample problems
author_facet Shaipai, Mohd. Ibrahim
Sudin, Shahdan
Ibrahim, Zuwairie
Khalid, Marzuki
author_sort Shaipai, Mohd. Ibrahim
title Enhanced weighted kernel regression with prior knowledge in solving small sample problems
title_short Enhanced weighted kernel regression with prior knowledge in solving small sample problems
title_full Enhanced weighted kernel regression with prior knowledge in solving small sample problems
title_fullStr Enhanced weighted kernel regression with prior knowledge in solving small sample problems
title_full_unstemmed Enhanced weighted kernel regression with prior knowledge in solving small sample problems
title_sort enhanced weighted kernel regression with prior knowledge in solving small sample problems
publishDate 2011
url http://eprints.utm.my/id/eprint/45825/
http://dx.doi.org/10.1016/j.proeng.2012.07.146
_version_ 1643651855350235136
score 13.211869

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