A Classifier Design Technique for Discrete Variable Pattern Recognition Problems

This paper presents a new computerized technique to aid the designers of pattern classifiers when the measurement variables are discrete and the values form a simple nominal scale (no inherent metric). A theory of "prime events" which applies to patterns with measurements of this type is presented. A procedure for applying the theory of "prime events" and an analysis of the "prime event estimates" is given. To manifest additional characteristics of this technique, an example optical character recognition (OCR) application is discussed.