Generalization Properties of Radial Basis Functions
Abstract
We examine the ability of radial basis functions (RBFs) to generalize. We compare the performance of several types of RBFs. We use the inverse dy(cid:173) namics of an idealized two-joint arm as a test case. We find that without a proper choice of a norm for the inputs, RBFs have poor generalization properties. A simple global scaling of the input variables greatly improves performance. We suggest some efficient methods to approximate this dis(cid:173) tance metric.
Cite
Text
Botros and Atkeson. "Generalization Properties of Radial Basis Functions." Neural Information Processing Systems, 1990.Markdown
[Botros and Atkeson. "Generalization Properties of Radial Basis Functions." Neural Information Processing Systems, 1990.](https://mlanthology.org/neurips/1990/botros1990neurips-generalization/)BibTeX
@inproceedings{botros1990neurips-generalization,
title = {{Generalization Properties of Radial Basis Functions}},
author = {Botros, Sherif M. and Atkeson, Christopher G.},
booktitle = {Neural Information Processing Systems},
year = {1990},
pages = {707-713},
url = {https://mlanthology.org/neurips/1990/botros1990neurips-generalization/}
}