TY  - JOUR
A1  - Bickel, Steffen
A1  - Brueckner, Michael
A1  - Scheffer, Tobias
T1  - Discriminative learning under covariate shift
N2  - We address classification problems for which the training instances are governed by an input 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. The problem of learning under covariate shift can be written as an integrated optimization problem. Instantiating the general optimization problem leads to a kernel logistic regression and an exponential model classifier for covariate shift. The optimization problem is convex under certain conditions; our findings also clarify the relationship to the known kernel mean matching procedure. We report on experiments on problems of spam filtering, text classification, and landmine detection.
Y1  - 2009
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/31582
UR  - http://jmlr.csail.mit.edu/
SN  - 1532-4435
ER  -