Abstract
A connectionist classifier with a new network architecture, Chebychev networks, is proposed for classifying continuous patterns in non-covex decision regions. The conventional connectionist classifier with monotonic sigmoid functions as its units is shown to face the problems of scaling and generalization. The new multi-layer networks, Chebychev networks, consist of nonmonotonic, nonlinear Chebychev functions as their input units, and monotonic sigmoid functions as their hidden and output units. The proposed connectionist classifier consists of two modules, a Chebychev networks module and a classification module. The Chebychev networks module transforms a complex pattern space into a high-order simple space. The classification module works on this transformed simple space.
It is shown the problems of scaling and generalization can be overcome with the new connectionist classifier with Chebychev networks, and the classification performance is increased significantly compared with the conventional connectionist classifier.
