Reparametrization-covariant theory for on-line learning of probability distributions

Toshiaki Aida

doi:10.1103/PhysRevE.64.056128

Reparametrization-covariant theory for on-line learning of probability distributions

Research output: Contribution to journal › Article › peer-review

Abstract

We discuss the on-line learning of probability distributions in a reparametrization covariant formulation. Reparametrization covariance plays an essential role not only to respect an intrinsic property of “information” but also for pattern recognition problems. We can obtain an optimal on-line learning algorithm with reparametrization invariance, where the conformal gauge connects a covariant formulation with a noncovariant one in a natural way.

Original language	English
Number of pages	1
Journal	Physical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics
Volume	64
Issue number	5
DOIs	https://doi.org/10.1103/PhysRevE.64.056128
Publication status	Published - Jan 1 2001
Externally published	Yes

ASJC Scopus subject areas

Statistical and Nonlinear Physics
Statistics and Probability
Condensed Matter Physics

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10.1103/PhysRevE.64.056128

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Reparametrization-covariant theory for on-line learning of probability distributions

Abstract

ASJC Scopus subject areas

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