Adaptive soft k-nearest-neighbour classifiers

A novel classifier is introduced to overcome the limitations of the k-NN classification systems. It estimates the posterior class probabilities using a local Parzen window estimation with the k-nearest-neighbour prototypes (in the Euclidean sense) to the pattern to classify. A learning algorithm is also presented to reduce the number of data paints to store. Experimental results in two hand-written classification problems demonstrate the potential of the proposed classification system. (C) 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.