A NOTE ON ERROR-BOUNDS FOR APPROXIMATION IN INNER-PRODUCT SPACES

In a recent paper a method is described for constructing certain appro ximations to a general element in the closure of the convex hull of a subset of an inner product space. This is of interest in connection wi th neural networks. Here we give an algorithm that generates simpler a pproximants with somewhat less computational cost.