PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Distribution Matching for Transduction
Novi Quadrianto, James Petterson and Alex Smola
In: NIPS 2009, 6-11 Dec 2009, Vancouver, Canada.


Many transductive inference algorithms assume that distributions over training and test estimates should be related, e.g. by providing a large margin of separation on both sets. We use this idea to design a transduction algorithm which can be used without modification for classification, regression, and structured estimation. At its heart we exploit the fact that for a good learner the distributions over the outputs on training and test sets should match. This is a classical two-sample problem which can be solved efficiently in its most general form by using distance measures in Hilbert Space. It turns out that a number of existing heuristics can be viewed as special cases of our approach.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:5684
Deposited By:James Petterson
Deposited On:08 March 2010