A Redundancy Approach to Classifier Training

The Al4laoui algorithm is aweighted mean-square-error (MSE) approach to pattern recognition. It employs redundancy, reintroducing the erroneously classified samples to increase the population of their corresponding classes. The algorithm was originally developed for single-layer neural networks. In this paper the algorithm is extended to multilayer neural networks. It is also shown that the application of the Mnoui algorithm to multilayer neural networks speeds up the convergence of theBackpropagation algorithm. The application of the Al-ABlaoui algorithm to the Levenberg-Marquardt algorithm for difficult pattern classification problems reduces the number of patterns that are erroneously classified.