Iterative improvement of a nearest neighbor classifier

In practical pattern recognition applications, the nearest neighbor classifier (NNC) is often applied because it does not require an a priori knowledge of the joint probability density of the input feature vectors. As the number of example vectors is increased, the error probability of the NNC approaches that of the Baysian classifier. However, at the same time, the computational complexity of the NNC increases. Also, for a small number of example vectors, the NNC is not optimal with respect to the training data. In this paper, we attack these problems by mapping the NNC to a sigma-pi neural network, to which it is partially isomorphic. A modified form of back-propagation (BP) learning is then developed and used to improve classifier performance. As examples, we apply our approach to the problems of hand-printed numeral recognition and geometrical shape recognition. Significant improvements in classification error percentages are observed for both the training data and testing data. Send reprint requests to Prof. Michael T. Manry, Department of Electrical Engineering, University of Texas at Arlington, Arlington, Texas 76019. Phone: 817-273-3483.