Fuzzy-Pattern-Classifier Training with Small Data Sets

Abstract

It is likely in real-world applications that only little data is available for training a knowledge-based system. We present a method for automatically training the knowledge-representing membership functions of a Fuzzy-Pattern-Classification system that works also when only little data is available and the universal set is described insufficiently. Actually, this paper presents how the Modified-Fuzzy-Pattern-Classifier's membership functions are trained using probability distribution functions. © Springer-Verlag Berlin Heidelberg 2010.