Using the tools of complexity theory, Stephen Judd develops a formal description of associative learning in connectionist networks. He rigorously exposes the co

Neural networks usually work adequately on small problems but can run into trouble when they are scaled up to problems involving large amounts of input data. Ci

Neural networks usually work adequately on small problems but can run into trouble when they are scaled up to problems involving large amounts of input data. Ci

This book describes recent theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, an