A cloning approach to classifier training

Author

Al-Alaoui, Mohamad Adnan ; Mouci, Rodolphe ; Mansour, Mohammad M. ; Ferzli, Rony

Author_Institution

Dept. of Electr. & Comput. Eng., American Univ. of Beirut, Lebanon

Volume

32

Issue

6

fYear

2002

fDate

11/1/2002 12:00:00 AM

Firstpage

746

Lastpage

752

Abstract

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.

Keywords

backpropagation; character recognition; mean square error methods; neural nets; pattern classification; Al-Alaoui algorithm; Bayes classifier; Levenberg-Marquardt algorithm; character recognition; mean-square error; multilayer neural networks; neural networks; pattern classification; pattern recognition; Algorithm design and analysis; Approximation algorithms; Character recognition; Classification algorithms; Cloning; Convergence; Multi-layer neural network; Neural networks; Pattern classification; Pattern recognition;

fLanguage

English

Journal_Title

Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on

Publisher

ieee

ISSN

1083-4427

Type

jour

DOI

10.1109/TSMCA.2002.807035

Filename

1177315