Proceedings of the Annual Meeting of the Cognitive Science Society

Machine learning methods for connectionist models usuallyoperate by attaching weights to a prespecified network so that a certainfunctionality is achieved. This is the classical credit assignment problem.This paper explores a constructive approach to connectionist learningwhere both a network and weights must be generated. It is argued that thisis an easier problem to solve and is sufficient for many applications sincenetwork topology is usually not as important as functionality.Three algorithms are presented for constructing networks from trainingexamples. A s cells are added and iterations are made, each method produces a network having optimal expected behavior (i.e. it correctly classifiesthe maximu m number of training examples possible) with arbitrarily highprobability p < 1.Learning speed for these algorithms is currently being investigated.