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.Full text not available from this repository.
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.
|Divisions:||Div F > Computational and Biological Learning|
|Depositing User:||Cron Job|
|Date Deposited:||09 Dec 2016 18:47|
|Last Modified:||26 Mar 2017 00:46|