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ICML
2007
IEEE

Discriminative learning for differing training and test distributions

9 years 12 months ago
Discriminative learning for differing training and test distributions
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.
Michael Brückner, Steffen Bickel, Tobias Sche
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2007
Where ICML
Authors Michael Brückner, Steffen Bickel, Tobias Scheffer
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