The University of British Columbia

The PAC learning theory creates a framework to assess the learning properties of static models for which the data are assumed to be independently and identically distributed (i.i.d.). The present paper first extends the idea of PAC learning to cover the learning of modelling tasks with In-dependent data, and then applies the resulting framework to evaluate learning of non-linear FIR models. Also, the learning properties of FIR modelling with radial basis function networks are further specified. These results include an upper bound on the size of the data set required to train an FIR radial basis function network, provided that the input data are uniformly distributed. Copyright (C) 2001 John Wiley & Sons, Ltd.