Emergence of learning: an approach to coping with NP-complete problems in learning

Various theoretical results show that learning in conventional feedforward neural networks such as multilayer perceptrons is NP-complete. In this paper we show that learning in min-max modular (M/sup 3/) neural networks is tractable. The key to coping with NP-complete problems in M/sup 3/ networks is to decompose a large-scale problem into a number of manageable, independent subproblems and to make the learning of a large-scale problem emerge from the learning of a number of related small subproblems.