Learning when only some of the training data are from the same distribution as test data
Jaakko Peltonen and Samuel Kaski
In: Learning when test and training inputs have different distributions - NIPS 2006 workshop, 9 Dec 2006, Whistler, Canada.

Abstract

The most difficult learning scenario is when the training and test distributions differ both in the data density and in the conditional class distributions. Learning is still possible assuming that some of the learning samples are known to come from the same distribution as the test samples. We formulate a simple nonparametric learner for this task, and apply it for building a “personalized recommender system” that uses the recommendations of other users as possibly useful parts of the training data.

PDF - Archive staff only - Requires Adobe Acrobat Reader or other PDF viewer.

[Edit]