A Framework for Probability Density Estimation
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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