The design of a nearest-neighbor classifier and its use for Japanese character recognition

Abstract

The nearest neighbor (NN) approach is a powerful nonparametric technique for pattern classification tasks. In this paper, algorithms for prototype reduction, hierarchical prototype organization and fast NN search are described. To remove redundant category prototypes and to avoid redundant comparisons, the algorithms explain geometrical information of a given prototype set which is represented approximately by computing k-nearest/farthest neighbors of each prototype. The performance of a NN classifier using those algorithms for Japanese character recognition is reported.