Input data redundancy in interpolation-based neural nets

Summary form only given, as follows. The problem of associative memory synthesis via multivariate interpolation is considered. The particular issue addressed is the possibility of detecting and eliminating redundant input data from the set of exemplars. The redundancy is detected via orthogonalization carried out in a reproducing kernel Hilbert space setting