A redundancy approach to classifier training

The Al-Alaoui algorithm is a weighted 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 Al-Alaoui algorithm to multilayer neural networks speeds up the convergence of the backpropagation algorithm. The application of the Al-Alaoui algorithm to the Levenberg-Marquardt algorithm for difficult pattern classification problems reduces the number of patterns that are erroneously classified