# Field Theoretical Analysis of On-line Learning of Probability Distributions

@article{Aida1999FieldTA,
title={Field Theoretical Analysis of On-line Learning of Probability Distributions},
author={Toshiaki Aida},
journal={Physical Review Letters},
year={1999},
volume={83},
pages={3554-3557}
}
• T. Aida
• Published 30 November 1999
• Computer Science
• Physical Review Letters
On-line learning of probability distributions is analyzed from the field theoretical point of view. We can obtain an optimal on-line learning algorithm, since renormalization group enables us to control the number of degrees of freedom of a system according to the number of examples. We do not learn parameters of a model, but probability distributions themselves. Therefore, the algorithm requires no a priori knowledge of a model.

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