A cloning approach to classifier training

The Al-Alaoui algorithm is a weighted mean-square error (MSE) approach to pattern recognition. It employs cloning of the erroneously classified samples to increase the population of their corresponding classes. The algorithm was originally developed for linear classifiers. In this paper, the algorit...

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Published inIEEE transactions on systems, man and cybernetics. Part A, Systems and humans Vol. 32; no. 6; pp. 746 - 752
Main Authors Al-Alaoui, M.A., Mouci, R., Mansour, M.M., Ferzli, R.
Format Journal Article
LanguageEnglish
Published IEEE 01.11.2002
Subjects
Algorithm design and analysis
Algorithms
Aluminum
Approximation algorithms
Character recognition
Classification algorithms
Classifiers
Cloning
Convergence
Cybernetics
Multi-layer neural network
Multilayers
Neural networks
Pattern classification
Pattern recognition
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Summary:The Al-Alaoui algorithm is a weighted mean-square error (MSE) approach to pattern recognition. It employs cloning of the erroneously classified samples to increase the population of their corresponding classes. The algorithm was originally developed for linear classifiers. In this paper, the algorithm is extended to multilayer neural networks which may be used as nonlinear classifiers. It is also shown that the application of the Al-Alaoui algorithm to multilayer neural networks speeds up the convergence of the back-propagation algorithm.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
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ISSN:1083-4427
1558-2426
DOI:10.1109/TSMCA.2002.807035