A Framework for Probability Density Estimation
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, PMLR 2:468-475, 2007.
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.