Bayesian Query Construction for Neural Network Models
Abstract
If data collection is costly, there is much to be gained by actively se(cid:173) lecting particularly informative data points in a sequential way. In a Bayesian decision-theoretic framework we develop a query selec(cid:173) tion criterion which explicitly takes into account the intended use of the model predictions. By Markov Chain Monte Carlo methods the necessary quantities can be approximated to a desired preci(cid:173) sion. As the number of data points grows, the model complexity is modified by a Bayesian model selection strategy. The proper(cid:173) ties of two versions of the criterion ate demonstrated in numerical experiments.
Cite
Text
Paass and Kindermann. "Bayesian Query Construction for Neural Network Models." Neural Information Processing Systems, 1994.Markdown
[Paass and Kindermann. "Bayesian Query Construction for Neural Network Models." Neural Information Processing Systems, 1994.](https://mlanthology.org/neurips/1994/paass1994neurips-bayesian/)BibTeX
@inproceedings{paass1994neurips-bayesian,
title = {{Bayesian Query Construction for Neural Network Models}},
author = {Paass, Gerhard and Kindermann, Jörg},
booktitle = {Neural Information Processing Systems},
year = {1994},
pages = {443-450},
url = {https://mlanthology.org/neurips/1994/paass1994neurips-bayesian/}
}