CUED Publications database

Distribution matching for transduction

Quadrianto, N and Petterson, J and Smola, AJ (2009) Distribution matching for transduction. Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference. pp. 1500-1508.

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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.

Item Type: Article
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:23
Last Modified: 05 Sep 2017 01:51