Assessing and Improving Neural Network Predictions by the Bootstrap Algorithm
Abstract
The bootstrap algorithm is a computational intensive procedure to derive nonparametric confidence intervals of statistical estimators in situations where an analytic solution is intractable. It is ap(cid:173) plied to neural networks to estimate the predictive distribution for unseen inputs. The consistency of different bootstrap procedures and their convergence speed is discussed. A small scale simulation experiment shows the applicability of the bootstrap to practical problems and its potential use.
Cite
Text
Paass. "Assessing and Improving Neural Network Predictions by the Bootstrap Algorithm." Neural Information Processing Systems, 1992.Markdown
[Paass. "Assessing and Improving Neural Network Predictions by the Bootstrap Algorithm." Neural Information Processing Systems, 1992.](https://mlanthology.org/neurips/1992/paass1992neurips-assessing/)BibTeX
@inproceedings{paass1992neurips-assessing,
title = {{Assessing and Improving Neural Network Predictions by the Bootstrap Algorithm}},
author = {Paass, Gerhard},
booktitle = {Neural Information Processing Systems},
year = {1992},
pages = {196-203},
url = {https://mlanthology.org/neurips/1992/paass1992neurips-assessing/}
}