A Framework for Probability Density Estimation
Abstract
The paper introduces a new framework for learning probability density functions. A theoretical analysis suggests that we can tailor a distribution for a class of tasks by training it to fit a small subsample. Experimental evidence is given to support the theoretical analysis.
Cite
Text
Shawe-Taylor and Dolia. "A Framework for Probability Density Estimation." Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007.Markdown
[Shawe-Taylor and Dolia. "A Framework for Probability Density Estimation." Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007.](https://mlanthology.org/aistats/2007/shawetaylor2007aistats-framework/)BibTeX
@inproceedings{shawetaylor2007aistats-framework,
title = {{A Framework for Probability Density Estimation}},
author = {Shawe-Taylor, John and Dolia, Alex},
booktitle = {Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics},
year = {2007},
pages = {468-475},
volume = {2},
url = {https://mlanthology.org/aistats/2007/shawetaylor2007aistats-framework/}
}