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Field Theoretical Analysis of On-line Learning of Probability Distributions

Toshiaki Aida
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Abstract

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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Toshiaki Aida. 1999-11-30. Field Theoretical Analysis of On-line Learning of Probability Distributions. https://doi.org/10.1103/physrevlett.83.3554

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