The Learning Dynamcis of a Universal Approximator
Abstract
The learning properties of a universal approximator, a normalized committee machine with adjustable biases, are studied for on-line back-propagation learning. Within a statistical mechanics frame(cid:173) work, numerical studies show that this model has features which do not exist in previously studied two-layer network models with(cid:173) out adjustable biases, e.g., attractive suboptimal symmetric phases even for realizable cases and noiseless data.
Cite
Text
West et al. "The Learning Dynamcis of a Universal Approximator." Neural Information Processing Systems, 1996.Markdown
[West et al. "The Learning Dynamcis of a Universal Approximator." Neural Information Processing Systems, 1996.](https://mlanthology.org/neurips/1996/west1996neurips-learning/)BibTeX
@inproceedings{west1996neurips-learning,
title = {{The Learning Dynamcis of a Universal Approximator}},
author = {West, Ansgar H. L. and Saad, David and Nabney, Ian T.},
booktitle = {Neural Information Processing Systems},
year = {1996},
pages = {288-294},
url = {https://mlanthology.org/neurips/1996/west1996neurips-learning/}
}