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Adaptive on-line learning of probability distributions from field theories

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1 Author(s)
Aida, T. ; Dept. of Aeronaut., Tokyo Metropolitan Coll. of Aeronaut. Eng., Japan

An adaptive algorithm is considered in on-line learning of probability functions, which infers a distribution underlying observed data x1, x2, …, xN. The algorithm is based on how we can detect the change of a source function in an unsupervised learning scheme. This is an extension of an optimal on-line learning algorithm of probability distributions, which is derived from the field theoretical point of view. Since we learn not parameters of a model but probability functions themselves, the algorithm has the advantage that it requires no a priori knowledge of a model

Published in:

Information Intelligence and Systems, 1999. Proceedings. 1999 International Conference on

Date of Conference:

1999