A Framework for Probability Density Estimation

author: Shai Ben-David, School of Computer Science, University of Waterloo
published: Feb. 25, 2008,   recorded: December 2007,   views: 4433


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The talk introduces a new framework for learning probability density functions by assessing their performance against a set of 'test functions'. A theoretical analysis suggests that we can tailor a distribution for a class of tasks by training it to fit a small subsample. There is a trade-off between the complexity of the class of distributions used for the distribution and the complexity of the set of tasks. Experimental evidence is given to support the theoretical analysis.

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