Regularisation in Sequential Learning Algorithms
Abstract
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.
Cite
Text
de Freitas et al. "Regularisation in Sequential Learning Algorithms." Neural Information Processing Systems, 1997.Markdown
[de Freitas et al. "Regularisation in Sequential Learning Algorithms." Neural Information Processing Systems, 1997.](https://mlanthology.org/neurips/1997/defreitas1997neurips-regularisation/)BibTeX
@inproceedings{defreitas1997neurips-regularisation,
title = {{Regularisation in Sequential Learning Algorithms}},
author = {de Freitas, João F. G. and Niranjan, Mahesan and Gee, Andrew H.},
booktitle = {Neural Information Processing Systems},
year = {1997},
pages = {458-464},
url = {https://mlanthology.org/neurips/1997/defreitas1997neurips-regularisation/}
}