Field Theoretical Analysis of On-line Learning of Probability Distributions
Disordered Systems and Neural Networks
2009-01-23 v1 adap-org
High Energy Physics - Theory
Adaptation and Self-Organizing Systems
Data Analysis, Statistics and Probability
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.
Cite
@article{arxiv.cond-mat/9911474,
title = {Field Theoretical Analysis of On-line Learning of Probability Distributions},
author = {Toshiaki Aida},
journal= {arXiv preprint arXiv:cond-mat/9911474},
year = {2009}
}
Comments
4 pages, 1 figure, RevTex