Robust Parameter Estimation and Model Selection for Neural Network Regression
Abstract
In this paper, it is shown that the conventional back-propagation (BPP) algorithm for neural network regression is robust to lever(cid:173) ages (data with :n corrupted), but not to outliers (data with y corrupted). A robust model is to model the error as a mixture of normal distribution. The influence function for this mixture model is calculated and the condition for the model to be robust to outliers is given. EM algorithm [5] is used to estimate the parameter. The usefulness of model selection criteria is also discussed. Illustrative simulations are performed.
Cite
Text
Liu. "Robust Parameter Estimation and Model Selection for Neural Network Regression." Neural Information Processing Systems, 1993.Markdown
[Liu. "Robust Parameter Estimation and Model Selection for Neural Network Regression." Neural Information Processing Systems, 1993.](https://mlanthology.org/neurips/1993/liu1993neurips-robust/)BibTeX
@inproceedings{liu1993neurips-robust,
title = {{Robust Parameter Estimation and Model Selection for Neural Network Regression}},
author = {Liu, Yong},
booktitle = {Neural Information Processing Systems},
year = {1993},
pages = {192-199},
url = {https://mlanthology.org/neurips/1993/liu1993neurips-robust/}
}