Globally Optimal On-line Learning Rules

We present a method for determining the globally optimal on-line learning rule for a soft committee machine under a statistical me(cid:173) chanics framework. This work complements previous results on locally optimal rules, where only the rate of change in general(cid:173) ization error was considered. We maximize the total reduction in generalization error over the whole learning process and show how the resulting rule can significantly outperform the locally optimal rule.