Discriminative Learning for Differing Training and Test Distributions |
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Steffen Bickel - Max Planck Institute for Computer Science, Germany Michael Brüeckner - Max Planck Institute for Computer Science, Germany Tobias Scheffer - Max Planck Institute for Computer Science, Germany |
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. |