CUED Publications database

Dynamic learning with the EM algorithm for neural networks

De Freitas, JFG and Niranjan, M and Gee, AH (2000) Dynamic learning with the EM algorithm for neural networks. Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology, 26. pp. 119-131. ISSN 0922-5773

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Abstract

In this paper, we derive an EM algorithm for nonlinear state space models. We use it to estimate jointly the neural network weights, the model uncertainty and the noise in the data. In the E-step we apply a forwardbackward Rauch-Tung-Striebel smoother to compute the network weights. For the M-step, we derive expressions to compute the model uncertainty and the measurement noise. We find that the method is intrinsically very powerful, simple and stable.

Item Type: Article
Subjects: UNSPECIFIED
Divisions: Div F > Machine Intelligence
Depositing User: Cron Job
Date Deposited: 07 Mar 2014 12:10
Last Modified: 08 Dec 2014 02:37
DOI: