Distance-Based Test Feature Classifiers and Its Applications

In this paper, we present a class of combinatorial-logical classifiers called test feature classifiers. These are polynomial functions that can be used as pattern classifiers of binary-valued feature vectors. The method is based on so-called tests, sets of features, which are sufficient to distinguish patterns from different classes of training samples. Based on the concept of test we propose a new distance-based test feature classifiers. To test the performance of the classifiers, we apply them to a well-known phoneme database and to a textual region location problem where we propose a new effective textual region searching system that can locate textual regions in a complex background. Experimental results show that the proposed classifiers yield a high recognition rate than conventional ones, have a high ability of generalization, and suggest that they can be used in a variety of pattern recognition applications. key words: learning, textual region location, Fourier feature, test feature classifier