Learning when only some of the training data are from the same distribution as test data

Reference:

Jaakko Peltonen and Samuel Kaski. Learning when only some of the training data are from the same distribution as test data. In NIPS 2006 Workshop on Learning when Test and Training Inputs Have Different Distributions, 2006. Extended abstract at http://www.cis.hut.fi/projects/mi/papers/nips06_did_abstract.pdf, poster at http://www.cis.hut.fi/projects/mi/papers/nips06_did_poster.pdf.

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.

S. Kaski and J. Peltonen belong to the Adaptive Informatics Research Centre, a national centre of excellence of the Academy of Finland. They were supported by grant 108515, and by University of Helsinki's Research Funds. This work was also supported in part by the IST Programme of the European Community, under the PASCAL Network of Excellence, IST-2002-506778. This publication only reflects the authors' views. All rights are reserved because of other commitments.

Suggested BibTeX entry:

@inproceedings{Peltonen06covshift,
    author = {Jaakko Peltonen and Samuel Kaski},
    booktitle = {NIPS 2006 Workshop on Learning when Test and Training Inputs Have Different Distributions},
    note = {Extended abstract at \url{http://www.cis.hut.fi/projects/mi/papers/nips06_did_abstract.pdf}, poster at \url{http://www.cis.hut.fi/projects/mi/papers/nips06_did_poster.pdf}},
    title = {Learning when only some of the training data are from the same distribution as test data},
    year = {2006},
}