The piecewise linear classifier DIPOL92

Abstract

This paper presents a learning algorithm which constructs an optimised piecewise linear classifier for n-class problems. In the first step of the algorithm initial positions of the discriminating hyperplanes are determined by linear regression for each pair of classes. To optimise these positions depending on the misclassified patterns an error criterion function is defined. This function is minimised by a gradient descent procedure for each hyperplane separately. As an option in the case of non-convex classes, a clustering procedure decomposing the classes into appropriate subclasses can be applied. The classification of patterns is defined on a symbolic level on the basis of the signs of the discriminating hyperplanes.