Abstract for freitas_nips97

In M. I. Jordan, M. J. Kearns and S. A. Solla, editors, Advances in Neural Information Processing Systems 10, pages 458-464. MIT Press, 1998.


JFG de Freitas, M Niranjan and AH Gee

December 1997

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.

(ftp:) freitas_nips97.ps.gz (http:) freitas_nips97.ps.gz
PDF (automatically generated from original PostScript document - may be badly aliased on screen):
  (ftp:) freitas_nips97.pdf | (http:) freitas_nips97.pdf

If you have difficulty viewing files that end '.gz', which are gzip compressed, then you may be able to find tools to uncompress them at the gzip web site.

If you have difficulty viewing files that are in PostScript, (ending '.ps' or '.ps.gz'), then you may be able to find tools to view them at the gsview web site.

We have attempted to provide automatically generated PDF copies of documents for which only PostScript versions have previously been available. These are clearly marked in the database - due to the nature of the automatic conversion process, they are likely to be badly aliased when viewed at default resolution on screen by acroread.