Kernelized radial basis probabilistic neural network for classification of river water quality
Radial Basis Probabilistic Neural Network (RBPNN) demonstrates broader and much more generalized capabilities which have been successfully applied to different fields.In this paper, the RBPNN is extended by calculating the Euclidean distance of each data point based on a kernel-induced distance in...
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my.uum.repo.134742015-04-01T03:38:24Z http://repo.uum.edu.my/13474/ Kernelized radial basis probabilistic neural network for classification of river water quality Lim, Eng Aik Zainuddin, Zarita QA76 Computer software Radial Basis Probabilistic Neural Network (RBPNN) demonstrates broader and much more generalized capabilities which have been successfully applied to different fields.In this paper, the RBPNN is extended by calculating the Euclidean distance of each data point based on a kernel-induced distance instead of the conventional sum-of squares distance.The kernel function is a generalization of the distance metric that measures the distance between two data points as the data points are mapped into a high dimensional space.Through comparing the four constructed classification models with Kernelized RBPNN, Radial Basis Function networks, RBPNN and Back-Propagation networks as intended, results showed that, model classification on River water quality of Langat river in Selangor, Malaysia by Kernelized RBPNN exhibited excellent performance in this regard. 2009-06-24 Conference or Workshop Item PeerReviewed application/pdf en http://repo.uum.edu.my/13474/1/PID59.pdf Lim, Eng Aik and Zainuddin, Zarita (2009) Kernelized radial basis probabilistic neural network for classification of river water quality. In: International Conference on Computing and Informatics 2009 (ICOCI09), 24-25 June 2009, Legend Hotel, Kuala Lumpur. http://www.icoci.cms.net.my |
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QA76 Computer software Lim, Eng Aik Zainuddin, Zarita Kernelized radial basis probabilistic neural network for classification of river water quality |
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Radial Basis Probabilistic Neural Network (RBPNN)
demonstrates broader and much more generalized capabilities which have been successfully applied to different fields.In this paper, the RBPNN is extended by calculating the Euclidean distance of each data point based on a kernel-induced distance
instead of the conventional sum-of squares distance.The kernel function is a generalization of the distance metric that measures the distance between two data points as the data points are
mapped into a high dimensional space.Through comparing the four constructed classification models with Kernelized RBPNN, Radial Basis Function networks, RBPNN and Back-Propagation
networks as intended, results showed that, model classification on River water quality of Langat river in Selangor, Malaysia by Kernelized RBPNN exhibited excellent performance in this regard. |
format |
Conference or Workshop Item |
author |
Lim, Eng Aik Zainuddin, Zarita |
author_facet |
Lim, Eng Aik Zainuddin, Zarita |
author_sort |
Lim, Eng Aik |
title |
Kernelized radial basis probabilistic neural network for classification of river water quality |
title_short |
Kernelized radial basis probabilistic neural network for classification of river water quality |
title_full |
Kernelized radial basis probabilistic neural network for classification of river water quality |
title_fullStr |
Kernelized radial basis probabilistic neural network for classification of river water quality |
title_full_unstemmed |
Kernelized radial basis probabilistic neural network for classification of river water quality |
title_sort |
kernelized radial basis probabilistic neural network for classification of river water quality |
publishDate |
2009 |
url |
http://repo.uum.edu.my/13474/1/PID59.pdf http://repo.uum.edu.my/13474/ http://www.icoci.cms.net.my |
_version_ |
1644281193962668032 |
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13.160551 |