TY - GEN

T1 - Distribution matching for transduction

AU - Quadrianto, Novi

AU - Petterson, James

AU - Smola, Alex J.

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=77956528369&partnerID=8YFLogxK

M3 - Conference contribution

SN - 9781615679119

T3 - Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference

SP - 1500

EP - 1508

BT - Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference

PB - Neural Information Processing Systems

T2 - 23rd Annual Conference on Neural Information Processing Systems, NIPS 2009

Y2 - 7 December 2009 through 10 December 2009

ER -