A fast exact parallel implementation of the k-nearest neighbour pattern classifier

A novel neural network architecture is presented that precisely implements the k-nearest-neighbour (k-NN) pattern classification rule. Given n exemplars, the size of the architecture grows O(n) and the time taken per classification grows O(log n). This offers perhaps the most useful neural implementation of the k-NN classifier compared to previous implementations, which suffer either from worst-case exponential training time, excessively large networks, unpredictable classification times, or inexact implementations of the classification rule.