TY - JOUR
A1 - Bickel, Steffen
A1 - Brückner, 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 - http://jmlr.csail.mit.edu/
SN - 1532-4435
ER -