UNSUPERVISED CONSTRUCTION OF DECISION NETWORKS FOR PATTERN CLASSIFICATION.

Abstract

An unsupervised classifier for encoding machine printed documents is presented. The method provides a speedup of a more conventional pattern-matching approach to the same task. It calls for successive extensions of an initially null decision network as new classes are defined. Extensions are performed on the basis of the assumption that within-class character variations occur at the edges of patterns.