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Abstract:
We present a method for determining the globally optimal
on-line learning rule for a soft committee machine under a
statistical mechanics framework. This work complements previous
results on locally optimal rules, where only the rate of change in
generalization 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.
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