Designing Linear Threshold Based Neural Network Pattern Classifiers
Abstract
The three problems that concern us are identifying a natural domain of pattern classification applications of feed forward neural networks, select(cid:173) ing an appropriate feedforward network architecture, and assessing the tradeoff between network complexity, training set size, and statistical reli(cid:173) ability as measured by the probability of incorrect classification. We close with some suggestions, for improving the bounds that come from Vapnik(cid:173) Chervonenkis theory, that can narrow, but not close, the chasm between theory and practice.
Cite
Text
Fine. "Designing Linear Threshold Based Neural Network Pattern Classifiers." Neural Information Processing Systems, 1990.Markdown
[Fine. "Designing Linear Threshold Based Neural Network Pattern Classifiers." Neural Information Processing Systems, 1990.](https://mlanthology.org/neurips/1990/fine1990neurips-designing/)BibTeX
@inproceedings{fine1990neurips-designing,
title = {{Designing Linear Threshold Based Neural Network Pattern Classifiers}},
author = {Fine, Terrence L.},
booktitle = {Neural Information Processing Systems},
year = {1990},
pages = {811-817},
url = {https://mlanthology.org/neurips/1990/fine1990neurips-designing/}
}