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 0922-5773Full text not available from this repository.
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.
|Divisions:||Div F > Machine Intelligence|
|Depositing User:||Unnamed user with email email@example.com|
|Date Deposited:||15 Dec 2015 13:38|
|Last Modified:||06 May 2016 23:07|