ADJUNCTS AND ALTERNATIVES TO NEURAL NETWORKS FOR SUPERVISED CLASSIFICATION

While multilayer neural networks (NN's) are a powerful tool for supervised classification their intrinsic nonlinearity often leads to slow convergence or divergence when the training sets include multimodal and/or overlapping classes. Well known optimization techniques improves classification performance and convergence rate and reduces the tendency for divergence. Optimization techniques are also applied to the development of a noniterative perceptron-like algorithm, called vector valued perceptrons (VVP). A comparison of the VVP and the backpropagation (BP) algorithms for supervised classification indicates that the performance of VVP's is comparable to BP. VVP's are capable of solving multiclass classification problems such as the exclusive-or problem, but require significantly less time (by as much as several orders of magnitude) than BP. This is especially the case for sample data with overlapping classes where BP may converge very slowly, perform poorly or diverge. VVP's applied as an adjunct and preprocessor for NN's in such cases result in improved NN classification performance and reduction in computational time.