### 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 language | English |
---|---|

Pages (from-to) | 3554-3557 |

Number of pages | 4 |

Journal | Physical Review Letters |

Volume | 83 |

Issue number | 17 |

Publication status | Published - Oct 25 1999 |

Externally published | Yes |

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### ASJC Scopus subject areas

- Physics and Astronomy(all)

### Cite this

*Physical Review Letters*,

*83*(17), 3554-3557.

**Field Theoretical Analysis of On-Line Learning of Probability Distributions.** / Aida, Toshiaki.

Research output: Contribution to journal › Article

*Physical Review Letters*, vol. 83, no. 17, pp. 3554-3557.

}

TY - JOUR

T1 - Field Theoretical Analysis of On-Line Learning of Probability Distributions

AU - Aida, Toshiaki

PY - 1999/10/25

Y1 - 1999/10/25

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.

UR - http://www.scopus.com/inward/record.url?scp=0000430886&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0000430886&partnerID=8YFLogxK

M3 - Article

VL - 83

SP - 3554

EP - 3557

JO - Physical Review Letters

JF - Physical Review Letters

SN - 0031-9007

IS - 17

ER -