Discriminative learning for differing training and test distributions (2007)
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| Venue: | In ICML |
| Citations: | 43 - 4 self |
BibTeX
@INPROCEEDINGS{Bickel07discriminativelearning,
author = {Steffen Bickel and Michael Brückner and Tobias Scheffer},
title = {Discriminative learning for differing training and test distributions},
booktitle = {In ICML},
year = {2007},
pages = {81--88},
publisher = {ACM Press}
}
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Abstract
We address classification problems for which the training instances are governed by a distribution that is allowed to differ arbitrarily from the test distribution—problems also referred to as classification under covariate shift. We derive a solution that is purely discriminative: neither training nor test distribution are modeled explicitly. We formulate the general problem of learning under covariate shift as an integrated optimization problem. We derive a kernel logistic regression classifier for differing training and test distributions. 1.
