A Framework for Probability Density Estimation

John Shawe-Taylor, Alex Dolia
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, PMLR 2:468-475, 2007.

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 this Paper

BibTeX
@InProceedings{pmlr-v2-shawe-taylor07a, 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}, pages = {468--475}, year = {2007}, editor = {Meila, Marina and Shen, Xiaotong}, volume = {2}, series = {Proceedings of Machine Learning Research}, address = {San Juan, Puerto Rico}, month = {21--24 Mar}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v2/shawe-taylor07a/shawe-taylor07a.pdf}, url = {https://proceedings.mlr.press/v2/shawe-taylor07a.html}, 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.} }
Endnote
%0 Conference Paper %T A Framework for Probability Density Estimation %A John Shawe-Taylor %A Alex Dolia %B Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2007 %E Marina Meila %E Xiaotong Shen %F pmlr-v2-shawe-taylor07a %I PMLR %P 468--475 %U https://proceedings.mlr.press/v2/shawe-taylor07a.html %V 2 %X 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.
RIS
TY - CPAPER TI - A Framework for Probability Density Estimation AU - John Shawe-Taylor AU - Alex Dolia BT - Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics DA - 2007/03/11 ED - Marina Meila ED - Xiaotong Shen ID - pmlr-v2-shawe-taylor07a PB - PMLR DP - Proceedings of Machine Learning Research VL - 2 SP - 468 EP - 475 L1 - http://proceedings.mlr.press/v2/shawe-taylor07a/shawe-taylor07a.pdf UR - https://proceedings.mlr.press/v2/shawe-taylor07a.html AB - 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. ER -
APA
Shawe-Taylor, J. & Dolia, A.. (2007). A Framework for Probability Density Estimation. Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 2:468-475 Available from https://proceedings.mlr.press/v2/shawe-taylor07a.html.

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