Field theoretical analysis of on-line learning of probability distributions

Research output: Contribution to journalArticle

13 Citations (Scopus)

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 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.

Original languageEnglish
Pages (from-to)3554-3557
Number of pages4
JournalPhysical Review Letters
Volume83
Issue number17
DOIs
Publication statusPublished - Jan 1 1999
Externally publishedYes

ASJC Scopus subject areas

  • Physics and Astronomy(all)

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