Discriminative Learning for Differing Training and Test Distributions
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.