Using concept learning for knowledge acquisition
International Journal of Man-Machine Studies
Pattern recognition using neural networks: theory and algorithms for engineers and scientists
Pattern recognition using neural networks: theory and algorithms for engineers and scientists
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Applied Multivariate Statistics with SAS Software
Applied Multivariate Statistics with SAS Software
Neural Networks for Optimization and Signal Processing
Neural Networks for Optimization and Signal Processing
An approximate median search algorithm in non-metric spaces
Pattern Recognition Letters
Statistical calibration of the natural gas consumption model
WSEAS TRANSACTIONS on SYSTEMS
A survey on feature selection methods
Computers and Electrical Engineering
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K-means and k-median clustering algorithms can help in the selection of centres for the Radial Basis Functional Link Nets. Radial Basis Functional Link Nets is used to classify the data. In this paper, we will show the importance of knowing the skewness of the data in deciding to choose between k-means or k-median clustering algorithm in finding the centre of Radial Basis Functional Link Nets and we will also show that this initial selection criterion will result in the improvement of efficiency in terms of speed and accuracy in data classification. Two sets of real data are used to demonstrate our results.