Designing multilayer perceptrons from nearest-neighbor systems

Although multilayer perceptrons have been shown to be adept at providing good solutions to many problems, they have a major drawback in the very large amount of time needed for training (for example, on the order of CPU days for some of the author´s experiments). The paper describes a method of producing a reasonable starting point by using a nearest-neighbor classifier. The method is further expanded to provide a method of `programming´ the upper layer of any network assuming the lower layers already exist