Regularisation in Sequential Learning Algorithms

Part of Advances in Neural Information Processing Systems 10 (NIPS 1997)

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João de Freitas, Mahesan Niranjan, Andrew Gee


In this paper, we discuss regularisation in online/sequential learn(cid:173) ing algorithms. In environments where data arrives sequentially, techniques such as cross-validation to achieve regularisation or model selection are not possible. Further, bootstrapping to de(cid:173) termine 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 percep(cid:173) trons 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 estima(cid:173) tion 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.