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Abstract:
In this paper we discuss regularisation in online/sequential
learning algorithms. In environments where data arrives
sequentially, techniques such as cross-validation to achieve
regularisation or model selection are not possible. Further,
bootstrapping to determine a confidence level is not practical. To
surmount these problems, a minimum variance estimation approach
that makes use of the extended Kalman algorithm for training
multi-layer perceptrons is employed. The novel contribution of this
paper is to show the theoretical links between extended Kalman
filtering, Sutton's variable learning rate algorithms and Mackay's
Bayesian estimation framework. In doing so, we propose algorithms
to overcome the need for heuristic choices of the initial
conditions and noise covariance matrices in the Kalman
approach.
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