Department of Automatic Control and Systems Engineering
Abstract
The nonlinear discriminant function obtained using a minimum squared error cost function can be shown to be directly related to the nonlinear Fisher discriminant. With the squared error cost function, the orthogonal least squares algorithm can be used to find a parsimonious description of the nonlinear discriminant function. Two simple classification techniques will be introduced and tested on a number of real and artificial data sets. The results show that the new classification technique can often perform favourably with other state of the art classification techniques
