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746 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS--PART A: SYSTEMS AND HUMANS, VOL. 32, NO. 6, NOVEMBER 2002 Correspondence________________________________________________________________________
 

Summary: 746 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS--PART A: SYSTEMS AND HUMANS, VOL. 32, NO. 6, NOVEMBER 2002
Correspondence________________________________________________________________________
A Cloning Approach to Classifier Training
Mohamad Adnan Al-Alaoui, Rodolphe Mouci,
Mohammad M. Mansour, and Rony Ferzli
Abstract--The Al-Alaoui algorithm is a weighted mean-square error
(MSE) approach to pattern recognition. It employs cloning of the erro-
neously 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 conver-
gence of the back-propagation algorithm.
Index Terms--Al-Alaoui algorithm, back-propagation algorithm, Bayes
classifier, character recognition, Levenberg­Marquardt algorithm, neural
networks, pattern classification.
I. INTRODUCTION
The development of the Al-Alaoui algorithm for pattern classi-
fication[1]­[6] using a single-layer neural network was motivated
by the proofs of Patterson and Womack [7] and of Wee [8] that

  

Source: Al-Alaoui, Mohamad Adnan - Faculty of Engineering and Architecture, American University of Beirut, Lebanon

 

Collections: Engineering