Asymptotic Bayesian generalization error when training and test distributions are different

ACM International Conference Proceeding Series

Volumn 227, Issue , 2007, Pages 1079-1086

  Yamazaki, Keisuke ^a   Kawanabe, Motoaki ^b   Watanabe, Sumio ^a   Sugiyama, Masashi ^a   Müller, Klaus Robert ^c  

^a TOKYO INSTITUTE OF TECHNOLOGY   (Japan)

^b FRAUNHOFER FIRST   (Germany)

^c TECHNISCHE UNIVERSITÄT BERLIN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ASYMPTOTIC ANALYSIS; BAYESIAN NETWORKS; ERROR ANALYSIS; MATHEMATICAL MODELS; PARAMETER ESTIMATION; SAMPLING;

APPROPRIATE MODELS; ASYMPTOTIC BAYESIAN GENERALIZATION; BAYESIAN ESTIMATION;

SUPERVISED LEARNING;

EID: 34547980509     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1273496.1273632     Document Type: Conference Paper

References (22)

1. Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, AC-19, 716-723.
2. Baldi, P., Brunak, S., & Stolovitzky, G. A. (1998). Bioinformatics: The machine learning approach. Cambridge: MIT Press.
3. Fedorov, V. V. (1972). Theory of optimal experiments. New York: Academic Press.
4. Heckman, J. J. (1979). Sample selection bias as a specification error. Econometrica, 47, 153-162.
5. Huang, J., Smola, A., Gretton, A., Borgwardt, K. M., & Scholkopf, B. (2007). Correcting sample selection bias by unlabeled data. In B. Schölkopf, J. Platt and T. Hoffman (Eds.), Advances in neural information processing systems 19. Cambridge, MA: MIT Press.
6. Kanamori, T., & Shimodaira, H. (2003). Active learning algorithm using the maximum weighted log-likelihood estimator. Journal of Statistical Planning and Inference, 116, 149-162.
7. Lin, Y., Lee, Y., & Wahba, G. (2002). Support vector machines for classification in nonstandard situations. Machine Learning, 46, 191-202.
8. Rissanen, J. (1986). Stochastic complexity and modeling. Annals of Statistics, 14, 1080-1100.
9. Scholkopf, B., &, Smola, A. J. (2002). Learning with kernels. Cambridge, MA: MIT Press.
10. Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6 (2), 461-464.
11. Shelton, C. R. (2001). Importance sampling for reinforcement learning with multiple objectives. PhD thesis. Massachusetts Institute of Technology.
12. Shimodaira, H. (2000). Improving predictive inference under covariate shift by weighting the log-likelihood function. Journal of Statistical Planning and Inference, 90, 227-244.
13. Stone, M. (1974). Cross-validatory choice and assessment . of statistical predictions. Journal of the Royal Statistical Society, Series B, 36, 111-147.
14. Sugiyama, M. (2006). Active learning in approximately linear regression based on conditional expectation of generalization error. Journal of Machine Learning Research, 7, 141-166.
15. Sugiyama, M., Krauledat, M., & Müller, K.-R. (2007). Covariate shift adaptation by importance weighted cross validation. Journal of Machine Learning Research, 8.
16. Sugiyama, M., & Müller, K.-R. (2005). Input-dependent estimation of generalization error under covariate shift. Statistics & Decisions, 23, 249-279.
17. Watanabe, S. (1999). Algebraic analysis for singular statistical estimation. Lecture Notes on Computer Science Springer, 1720, 39-50.
18. Watanabe, S. (2001a). Algebraic analysis for non-identifiable learning machines. Neural Computation, 13 (4), 899-933.
19. Watanabe, S. (2001b). Algebraic information geometry for learning machines with singularities. Advances in Neural Information Processing Systems, 14, 329-336.
20. White, H. (1982). Maximum likelihood estimation of mis-specified models. Econométrica, 50, 1-25.
21. Wiens, D. P. (2000). Robust weights and designs for biased regression models: Least squares and generalized M-estimation. Journal of Statistical Planning and Inference, 83, 395-412.
22. Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002). Brain-computer interfaces for communication and control. Clinical Neurophysiology, 113, 767-791.