Learning in neural networks with local minima

Abstract

An attempt is made to study learning in neural networks with local minima. For small learning parameters η, the transition time from one mimimum to another is asymptotically given by exp(η̃/η), with η̃, a constant independent of η, called the reference learning parameter. A general scheme to calculate the reference learning parameter is presented. This scheme is valid for a large class of learning rules.