Cart (Loading....) | Create Account
Close category search window
 

Online Bayesian Learning With Natural Sequential Prior Distribution

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

The purchase and pricing options are temporarily unavailable. Please try again later.
3 Author(s)
Nakada, Y. ; Coll. of Sci. & Eng., Aoyama Gakuin Univ., Sagamihara, Japan ; Wakahara, M. ; Matsumoto, T.

Online Bayesian learning has been successfully applied to online learning for multilayer perceptrons and radial basis functions. In online Bayesian learning, typically, the conventional transition model has been used. Although the conventional transition model is based on the squared norm of the difference between the current parameter vector and the previous parameter vector, the transition model does not adequately consider the difference between the current observation model and the previous observation model. To adequately consider this difference between the observation models, we propose a natural sequential prior. The proposed transition model uses a Fisher information matrix to consider the difference between the observation models more naturally. For validation, the proposed transition model is applied to an online learning problem for a three-layer perceptron.

Published in:

Neural Networks and Learning Systems, IEEE Transactions on  (Volume:25 ,  Issue: 1 )

Date of Publication:

Jan. 2014

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.