Abstract
This article discusses the adaptation of recently developed regression techniques to classifier design. Apart from finite sample effects, projection pursuit (PP) regression may be used to model a desired response (class) as a sum of ridge functions according to a minimum expected squared error criterion. This approach can be shown to furnish an optimal discriminant function which can satisfy the Neyman-Pearson criterion over all possible thresholds. Basis function expansions are used instead of smoothed histograms to reduce computation. Since good approximation of a discriminant by a linear combination of moderate number of ridge functions may not be easy, we introduce an improved method utilizing a nonlinear weighting function.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have