Pattern classification by a condensed neural network

Abstract

Neural networks have come to the fore as potent pattern classifiers. More amenable to parallel computation, they are much faster than the nearest neighbor classifier (NN), which, however, has distinctly outperformed them in several applications. The purpose of this study is to investigate a condensed neural network that combines the classification speed of neural networks and the low error rate of the nearest neighbor classifier. This condensed network is a fast, accurate classifier of simple architecture and function: it consists of a set of generalized perceptrons that draw maximal hyperspherical boundaries centered on patterns of memory units, each circumscribing reference patterns of a single category. The generalized perceptrons carry out classification, assisted by sporadic nearest neighbor matching to patterns of a small reference set. We compare the condensed network to a high performance neural network pattern classifier (Kohonen) and to NN in experiments on hand-printed character recognition.