TY - JOUR
T1 - Field theoretical analysis of on-line learning of probability distributions
AU - Aida, Toshiaki
N1 - Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 1999/1/1
Y1 - 1999/1/1
N2 - 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 a 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.
AB - 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 a 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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U2 - 10.1103/PhysRevLett.83.3554
DO - 10.1103/PhysRevLett.83.3554
M3 - Article
AN - SCOPUS:0000430886
SN - 0031-9007
VL - 83
SP - 3554
EP - 3557
JO - Physical Review Letters
JF - Physical Review Letters
IS - 17
ER -