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NeuroCOLT
Technical Report NC-TR-95-006
A
Stop Criterion for the Boltzmann Machine Learning Algorithm
Berthold
Ruf
Technical University Graz
Abstract
Ackley, Hinton and Sejnowski introduced a very interesting
and versatile learning algorithm for the Boltzmann machine (BM). However
it is difficult to decide when to stop the learning procedure. Experiments
have shown that the BM may destroy previously achieved results when
the learning process is executed for too long. This paper introduces
a new quantity, the conditional divergence, measuring the learning
success for the inputs of the data set. To demonstrate its use, some
experiments are presented, based on the Encoder Problem.
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Postscript
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