Physics – Condensed Matter – Disordered Systems and Neural Networks
Scientific paper
1998-01-28
Machine Learning 32 179-201 (1998)
Physics
Condensed Matter
Disordered Systems and Neural Networks
24 pages, 8 figures, to appear in Machine Learning Journal
Scientific paper
We review the application of Statistical Mechanics methods to the study of online learning of a drifting concept in the limit of large systems. The model where a feed-forward network learns from examples generated by a time dependent teacher of the same architecture is analyzed. The best possible generalization ability is determined exactly, through the use of a variational method. The constructive variational method also suggests a learning algorithm. It depends, however, on some unavailable quantities, such as the present performance of the student. The construction of estimators for these quantities permits the implementation of a very effective, highly adaptive algorithm. Several other algorithms are also studied for comparison with the optimal bound and the adaptive algorithm, for different types of time evolution of the rule.
Caticha Nestor
Kinouchi Osame
Vicente Renato
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