Subgoal chaining and the local minimum problem
J.P. Lewis, Michael K. Weir · 2003
It is well known that performing gradient descent method on fixed surfaces may result in poor travel through getting stuck in local minima and other surface features. Subgoal chaining in supervised learning is a method to improve travel for neural networks by directing local variation in the surface during training. This paper shows that linear subgoal chains such as those used in ERA are not sufficient to overcome the local minimum problem and examines nonlinear subgoal chains as a possible alternative.