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.

## 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.

Item Type: | Article |
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Subjects: | UNSPECIFIED |

Divisions: | Div F > Computational and Biological Learning |

Depositing User: | Cron Job |

Date Deposited: | 09 Dec 2016 18:47 |

Last Modified: | 29 Apr 2017 04:13 |

DOI: |