The Generalisation Cost of RAMnets
Abstract
Given unlimited computational resources, it is best to use a crite(cid:173) rion of minimal expected generalisation error to select a model and determine its parameters. However, it may be worthwhile to sac(cid:173) rifice some generalisation performance for higher learning speed. A method for quantifying sub-optimality is set out here, so that this choice can be made intelligently. Furthermore, the method is applicable to a broad class of models, including the ultra-fast memory-based methods such as RAMnets. This brings the added benefit of providing, for the first time, the means to analyse the generalisation properties of such models in a Bayesian framework .
Cite
Text
Rohwer and Morciniec. "The Generalisation Cost of RAMnets." Neural Information Processing Systems, 1996.Markdown
[Rohwer and Morciniec. "The Generalisation Cost of RAMnets." Neural Information Processing Systems, 1996.](https://mlanthology.org/neurips/1996/rohwer1996neurips-generalisation/)BibTeX
@inproceedings{rohwer1996neurips-generalisation,
title = {{The Generalisation Cost of RAMnets}},
author = {Rohwer, Richard and Morciniec, Michal},
booktitle = {Neural Information Processing Systems},
year = {1996},
pages = {253-259},
url = {https://mlanthology.org/neurips/1996/rohwer1996neurips-generalisation/}
}