Correcting sample selection bias by unlabeled data

Advances in Neural Information Processing Systems

Volumn , Issue , 2007, Pages 601-608

  Huang, Jiayuan ^a   Smola, Alexander J ^b   Gretton, Arthur ^c   Borgwardt, Karsten M ^d   Schölkopf, Bernhard ^c  

^a UNIVERSITY OF WATERLOO   (Canada)

^b AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^c MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

^d UNIVERSITY OF MUNICH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

DIFFERENT DISTRIBUTIONS; DISTRIBUTION ESTIMATION; FEATURE SPACE; NONPARAMETRIC METHODS; RESAMPLING; SAMPLE SELECTION BIAS; SAMPLING DISTRIBUTION; TEST DATA; TRAINING AND TESTING; UNLABELED DATA;

EID: 84864031047     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. G. Casella and R. Berger. Statistical Inference. Duxbury, Pacific Grove, CA, 2nd edition, 2002.
2. M. Dudik, R.E. Schapire, and S.J. Phillips. Correcting sample selection bias in maximum entropy density estimation. In Advances in Neural Information Processing Systems 17, 2005.
3. A. Gretton, K. Borgwardt, M. Rasch, B. Schölkopf, and A. Smola. A kernel method for the two-sampleproblem. In NIPS. MIT Press, 2006.
4. S. Gruvberger, M. Ringner, Y.Chen, S.Panavally, L.H. Saal, C. Peterson A.Borg, M. Ferno, and P.S.Meltzer. Estrogen receptor status in breast cancer is associated with remarkably distinct gene expression patterns. Cancer Research, 61, 2001.
5. J. Heckman. Sample selection bias as a specification error. Econometrica, 47(1):153-161, 1979.
6. W. Hoeffding. Probability inequalities for sums of bounded random variables. Journal of the American Statistical Association, 58:13-30, 1963.
7. J. Huang, A. Smola, A. Gretton, K. Borgwardt, and B. Schölkopf. Correcting sample selection bias by unlabeled data. Technical report, CS-2006-44, University of Waterloo, 2006.
8. Y. Lin, Y. Lee, and G. Wahba. Support vector machines for classification in nonstandard situations. Machine Learning, 46:191-202, 2002.
9. S. Rosset, J. Zhu, H. Zou, and T. Hastie. A method for inferring label sampling mechanisms in semisupervised learning. In Advances in Neural Information Processing Systems 17, 2004.
10. M. Schmidt and H. Gish. Speaker identification via support vector classifiers. In Proc. ICASSP '96, pages 105-108, Atlanta, GA, May 1996.
11. B. Schölkopf, J. Platt, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson. Estimating the support of a high-dimensional distribution. Neural Computation, 13(7):1443-1471, 2001.
12. H. Shimodaira. Improving predictive inference under convariance shift by weighting the log-likelihood function. Journal of Statistical Planning and Inference, 90, 2000.
13. D. Singh, P. Febbo, K. Ross, D. Jackson, J. Manola, C. Ladd, P. Tamayo, A. Renshaw, A. DAmico, and J. Richie. Gene expression correlates of clinical prostate cancer behavior. Cancer Cell, 1(2), 2002.
14. I. Steinwart. On the influence of the kernel on the consistency of support vector machines. Journal of Machine Learning Research, 2:67-93, 2002.
15. I. Steinwart. Support vector machines are universally consistent. J. Compl., 18:768-791, 2002.
16. M. Sugiyama and K.-R. Müller. Input-dependent estimation of generalization error under covariate shift. Statistics and Decisions, 23:249-279, 2005.
17. I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun. Large margin methods for structured and interdependent output variables. Journal of Machine Learning Research, 2005.
18. P. Warnat, R. Eils, and B. Brors. Cross-platform analysis of cancer microarray data improves gene expression based classification of phenotypes. BMC Bioinformatics, 6:265, Nov 2005.
19. M. West, C. Blanchette, H. Dressman, E. Huang, S. Ishida, R. Spang, H Zuzan, J.A. Olson Jr, J.R.Marks, and J.R.Nevins. Predicting the clinical status of human breast cancer by using gene expression profiles. PNAS, 98(20), 2001.
20. B. Zadrozny. Learning and evaluating classifiers under sample selection bias. In International Conference on Machine Learning ICML'04, 2004.