@inproceedings{f09ad950e4784649a15d749117f5bba2,
title = "Distribution matching for transduction",
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.",
author = "Novi Quadrianto and James Petterson and Smola, \{Alex J.\}",
year = "2009",
language = "English",
isbn = "9781615679119",
series = "Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference",
publisher = "Neural Information Processing Systems",
pages = "1500--1508",
booktitle = "Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference",
note = "23rd Annual Conference on Neural Information Processing Systems, NIPS 2009 ; Conference date: 07-12-2009 Through 10-12-2009",
}