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Abstract:
An important issue in neural computing concerns the
description of learning dynamics with macroscopic dynamical
variables. Recent progress on on-line learning only addresses the
often unrealistic case of an infinite training set. We introduce a
new framework to model batch learning of restricted sets of
examples, widely applicable to any learning cost function, and
fully taking into account the temporal correlations introduced by
the recycling of the examples. For illustration we analyze the
effects of weight decay and early stopping during the learning of
teacher-generated examples.
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