Automatic basis selection for RBF networks using Stein's unbiased risk estimator
- Ali Ghodsi, University of Waterloo
- Dale Schuurmans, AICML
The problem of selecting the appropriate number of basis functions is a critical issue for radial basis function neural networks. An RBF network with an overly restricted basis gives poor predictions on new data, since the model has too little flexibility (yielding high bias and low variance). By contrast, an RBF network with too many basis functions also gives poor generalization performance since it is too flexible and fits too much of the noise on the training data (yielding low bias but
Citation
A. Ghodsi, D. Schuurmans. "Automatic basis selection for RBF networks using Stein's unbiased risk estimator". IJCNN, June 2003.| Keywords: | |
| Category: | In Conference |
BibTeX
@incollection{Ghodsi+Schuurmans:IJCNN03,
author = {Ali Ghodsi and Dale Schuurmans},
title = {Automatic basis selection for RBF networks using Stein's unbiased
risk estimator},
booktitle = {},
year = 2003,
}Last Updated: August 17, 2007Submitted by Russ Greiner
