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Correcting Sample Selection Bias by Unlabeled Data

MPG-Autoren
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Huang,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Smola,  A
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Gretton,  A
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Borgwardt,  KM
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Huang, J., Smola, A., Gretton, A., Borgwardt, K., & Schölkopf, B. (2007). Correcting Sample Selection Bias by Unlabeled Data. Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference, 601-608.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0013-CBDB-0
Zusammenfassung
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.