A Framework for Probability Density Estimation
A Framework for Probability Density Estimation
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.
468-475
Shawe-Taylor, John
b1931d97-fdd0-4bc1-89bc-ec01648e928b
Dolia, Alexander N.
1b610224-d5f1-46d7-a35a-db5918d25076
27 October 2007
Shawe-Taylor, John
b1931d97-fdd0-4bc1-89bc-ec01648e928b
Dolia, Alexander N.
1b610224-d5f1-46d7-a35a-db5918d25076
Shawe-Taylor, John and Dolia, Alexander N.
(2007)
A Framework for Probability Density Estimation.
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, San Juan, Puerto Rico.
21 - 24 Mar 2007.
.
Record type:
Conference or Workshop Item
(Paper)
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.
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Published date: 27 October 2007
Venue - Dates:
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, San Juan, Puerto Rico, 2007-03-21 - 2007-03-24
Identifiers
Local EPrints ID: 57917
URI: http://eprints.soton.ac.uk/id/eprint/57917
PURE UUID: 1e81f689-fdb5-4c3b-815a-7a535f8e70c7
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Date deposited: 14 Aug 2008
Last modified: 08 Jan 2022 19:04
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Contributors
Author:
John Shawe-Taylor
Author:
Alexander N. Dolia
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