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  Correcting Sample Selection Bias by Unlabeled Data

Huang, J., Smola, A., Gretton, A., Borgwardt, K., & Schölkopf, B. (2007). Correcting Sample Selection Bias by Unlabeled Data. In B. Schölkopf, J. Platt, & T. Hoffman (Eds.), Advances in Neural Information Processing Systems 19 (pp. 601-608). Cambridge, MA, USA: MIT Press.

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 Creators:
Huang, J, Author           
Smola, A, Author           
Gretton, A1, 2, Author           
Borgwardt, KM, Author           
Schölkopf, B1, 2, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 Abstract: We consider the scenario where training and test data are drawn from different distributions, commonly referred to as sample selection bias. Most algorithms for this setting try to first recover sampling distributions and then make appropriate corrections based on the distribution estimate. We present a nonparametric method which directly produces resampling weights without distribution estimation. Our method works by matching distributions between training and
testing sets in feature space. Experimental results demonstrate that our method works well in practice.

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 Dates: 2007-09
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 4194
 Degree: -

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Title: Twentieth Annual Conference on Neural Information Processing Systems (NIPS 2006)
Place of Event: Vancouver, BC, Canada
Start-/End Date: 2006-12-04 - 2006-12-07

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Title: Advances in Neural Information Processing Systems 19
Source Genre: Proceedings
 Creator(s):
Schölkopf, B1, Editor           
Platt, JC, Editor
Hoffman, T, Editor
Affiliations:
1 Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795            
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 601 - 608 Identifier: ISBN: 0-262-19568-2