nips nips2009 nips2009-72 nips2009-72-reference knowledge-graph by maker-knowledge-mining
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Author: Novi Quadrianto, James Petterson, Alex J. Smola
Abstract: 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. 1
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