LTF-C: Architecture, Training Algorithm and Applications of New Neural Classifier

This paper presents a new model of an artificial neural network solving classification problems, called Local Transfer Function Classifier (LTF-C), which is very similar to this of the Radial Basis Function neural network (RBF), however it utilizes an entirely different learning algorithm.

Abstract

This paper presents a new model of an artificial neural network solving classification problems, called Local Transfer Function Classifier (LTF-C). Its architecture is very similar to this of the Radial Basis Function neural network (RBF), however it utilizes an entirely different learning algorithm. This algorithm is composed of four main parts: changing positions of reception fields, changing their sizes, insertion of new hidden neurons and removal of unnecessary ones during the training. The paper presents also results of LTF-C application to three real-life tasks: handwritten digit recognition, credit approval and cancer diagnosis. LTF-C was able to solve each of these problems with better accuracy than most popular classification systems. Moreover, LTF-C was relatively small and fast.