Neural network training without spurious minima.

Abstract

In training student perceptions, recourse to information theory concepts allows one to select the best working hypothesis and obtain an exact solution for the associated probability distribution. We apply this training scheme to perceptions with binary weights and show that no phase transition ensues. By recourse to our approach fast learning is guaranteed and trapping by spurious local minima is avoided. \textcopyright{} 1996 The American Physical Society.