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Volumn 1, Issue , 2012, Pages 823-830

Semi-supervised learning of class balance under class-prior change by distribution matching

Author keywords

[No Author keywords available]

Indexed keywords

BIAS CORRECTION; DATA SETS; DISTRIBUTION MATCHING; ESTIMATION BIAS; LABELED DATA; RESAMPLING; SEMI-SUPERVISED LEARNING; TEST INPUTS; TRAINING DATASET;

EID: 84867122342     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (42)

References (35)
  • 1
    • 0001199215 scopus 로고
    • A general class of coefficients of divergence of one distribution from another
    • Ali, S. M. and Silvey, S. D. A general class of coefficients of divergence of one distribution from another. Journal of the Royal Statistical Society, Series B, 28:131-142, 1966.
    • (1966) Journal of the Royal Statistical Society, Series B , vol.28 , pp. 131-142
    • Ali, S.M.1    Silvey, S.D.2
  • 2
    • 0001640740 scopus 로고    scopus 로고
    • Robust and efficient estimation by minimising a density power divergence
    • Basu, A., Harris, I. R., Hjort, N. L., and Jones, M. C. Robust and efficient estimation by minimising a density power divergence. Biometrika, 85(3):549-559, 1998.
    • (1998) Biometrika , vol.85 , Issue.3 , pp. 549-559
    • Basu, A.1    Harris, I.R.2    Hjort, N.L.3    Jones, M.C.4
  • 9
    • 0000489740 scopus 로고
    • Information-type measures of difference of probability distributions and indirect observation
    • Csiszár, I. Information-type measures of difference of probability distributions and indirect observation. Studia Scientiarum Mathematicarum Hungarica, 2:229-318, 1967.
    • (1967) Studia Scientiarum Mathematicarum Hungarica , vol.2 , pp. 229-318
    • Csiszár, I.1
  • 14
    • 0000125534 scopus 로고
    • Sample selection bias as a specification error
    • Heckman, J. J. Sample selection bias as a specification error. Econometrica, 47(1):153-161, 1979.
    • (1979) Econometrica , vol.47 , Issue.1 , pp. 153-161
    • Heckman, J.J.1
  • 17
    • 84867133788 scopus 로고    scopus 로고
    • Statistical analysis of kernel-based least-squares density-ratio estimation
    • Kanamori, T., Suzuki, T., and Sugiyama, M. Statistical analysis of kernel-based least-squares density-ratio estimation. Machine Learning, 2012.
    • (2012) Machine Learning
    • Kanamori, T.1    Suzuki, T.2    Sugiyama, M.3
  • 18
    • 0038236399 scopus 로고    scopus 로고
    • Dual representation of φ-divergences and applications
    • Keziou, A. Dual representation of φ-divergences and applications. Comptes Rendus Mathématique, 336(10):857-862, 2003.
    • (2003) Comptes Rendus Mathématique , vol.336 , Issue.10 , pp. 857-862
    • Keziou, A.1
  • 20
    • 4744367074 scopus 로고    scopus 로고
    • Adjusting the outputs of a classifier to new a priori probabilities may significantly improve classification accuracy: Evidence from a multi-class problem in remote sensing
    • Latinne, P., Saerens, M., and Decaestecker, C. Adjusting the outputs of a classifier to new a priori probabilities may significantly improve classification accuracy: Evidence from a multi-class problem in remote sensing. In Proceedings of the 18th International Conference on Machine Learning, pp. 298-305, 2001.
    • (2001) Proceedings of the 18th International Conference on Machine Learning , pp. 298-305
    • Latinne, P.1    Saerens, M.2    Decaestecker, C.3
  • 21
    • 0036161029 scopus 로고    scopus 로고
    • Support vector machines for classification in nonstandard situations
    • Lin, Y., Lee, Y., and Wahba, G. Support vector machines for classification in nonstandard situations. Machine Learning, 46(1/3):191-202, 2002.
    • (2002) Machine Learning , vol.46 , Issue.1-3 , pp. 191-202
    • Lin, Y.1    Lee, Y.2    Wahba, G.3
  • 23
    • 77958588617 scopus 로고    scopus 로고
    • Estimating divergence functionals and the likelihood ratio by convex risk minimization
    • Nguyen, X., Wainwright, M. J., and Jordan, M. I. Estimating divergence functionals and the likelihood ratio by convex risk minimization. IEEE Transactions on Information Theory, 56(11):5847-5861, 2010.
    • (2010) IEEE Transactions on Information Theory , vol.56 , Issue.11 , pp. 5847-5861
    • Nguyen, X.1    Wainwright, M.J.2    Jordan, M.I.3
  • 24
    • 0001454867 scopus 로고
    • On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling
    • Pearson, K. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philosophical Magazine, 50:157-175, 1900.
    • (1900) Philosophical Magazine , vol.50 , pp. 157-175
    • Pearson, K.1
  • 26
    • 9444250658 scopus 로고    scopus 로고
    • Regularized least-squares classification. Advances in Learning Theory: Methods, Model and Applications
    • Rifkin, R., Yeo, G., and Poggio, T. Regularized least-squares classification. Advances in Learning Theory: Methods, Model and Applications. NATO Science Series III: Computer and Systems Sciences, 190:131-153, 2003.
    • (2003) NATO Science Series III: Computer and Systems Sciences , vol.190 , pp. 131-153
    • Rifkin, R.1    Yeo, G.2    Poggio, T.3
  • 27
    • 0004267646 scopus 로고
    • Princeton University Press, Princeton, NJ, USA
    • Rockafellar, R. T. Convex Analysis. Princeton University Press, Princeton, NJ, USA, 1970.
    • (1970) Convex Analysis
    • Rockafellar, R.T.1
  • 28
    • 0036134369 scopus 로고    scopus 로고
    • Adjusting the outputs of a classifier to new a priori probabilities: A simple procedure
    • Saerens, M., Patrice, M., and Decaestecker, C. Adjusting the outputs of a classifier to new a priori probabilities: A simple procedure. Neural Computation, 14:21-41, 2001.
    • (2001) Neural Computation , vol.14 , pp. 21-41
    • Saerens, M.1    Patrice, M.2    Decaestecker, C.3
  • 31
    • 77957851853 scopus 로고    scopus 로고
    • Superfast-trainable multi-class probabilistic classifier by least-squares posterior fitting
    • Sugiyama, M. Superfast-trainable multi-class probabilistic classifier by least-squares posterior fitting. IEICE Transactions on Information and Systems, E93-D:2690-2701, 2010.
    • (2010) IEICE Transactions on Information and Systems , vol.E93-D , pp. 2690-2701
    • Sugiyama, M.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.