Bayesian on-line learning: a sequential Monte Carlo with importance resampling

Author

Kurihara, T. ; Nakada, Y. ; Yosui, K. ; Matsumoto, T.

Author_Institution

Dept. of Electr., Electron. & Comput. Eng., Waseda Univ., Tokyo, Japan

fYear

2001

fDate

2001

Firstpage

163

Lastpage

172

Abstract

A Bayesian online learning scheme with sequential Monte Carlo incorporating importance resampling is proposed. The proposed scheme adjusts not only parameters for data fitting but also adjusts hyperparameters online so that the scheme attempts to avoid overfitting in an adaptive manner. One of the advantages of the scheme is the fact that it can adapt to environmental changes, i.e., it can perform learning, even when the underlying input-output relationship varies over time. The scheme is tested against simple examples and is shown to be functional

Keywords

Bayes methods; data handling; importance sampling; learning systems; Bayesian online learning; data fitting; environmental changes; hyperparameters; importance resampling; input-output relationship; overfitting; sequential Monte Carlo; training data set; Bayesian methods; Integral equations; Monte Carlo methods; Nonlinear equations; Sequential analysis; State estimation; Testing; Training data; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks for Signal Processing XI, 2001. Proceedings of the 2001 IEEE Signal Processing Society Workshop

Conference_Location

North Falmouth, MA

ISSN

1089-3555

Print_ISBN

0-7803-7196-8

Type

conf

DOI

10.1109/NNSP.2001.943121

Filename

943121