Active learning for misspecified generalized linear models

Advances in Neural Information Processing Systems

Volumn , Issue , 2007, Pages 65-72

  Bach, Francis R ^a  

^a PSL RESEARCH UNIVERSITY   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVE LEARNING; ALGORITHMIC FRAMEWORK; BINARY CLASSIFICATION; CONVEX OPTIMIZATION PROBLEMS; GENERALIZATION ERROR; GENERALIZATION PERFORMANCE; GENERALIZED LINEAR MODEL; LEARNING METHODS; MERCER KERNEL; MISSPECIFICATION; MULTI-CLASS CLASSIFICATION; SAMPLING DISTRIBUTION; TRAINING DATA; UNBIASED ESTIMATOR;

ASYMPTOTIC ANALYSIS; CONVEX OPTIMIZATION; LEARNING SYSTEMS;

ALGORITHMS;

EID: 34248997891     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (26)

1. D. A. Cohn, Z. Ghahramani, and M. I. Jordan. Active learning with statistical models. J. Art. Intel. Res., 4:129-145, 1996.
2. V. V. Fedorov. Theory of optimal experiments. Academic Press, 1972.
3. P. Chaudhuri and P. A. Mykland. On efficient designing of nonlinear experiments. Stat. Sin., 5:421-440, 1995.
4. S. Dasgupta. Coarse sample complexity bounds for active learning. In Adv. NIPS 18, 2006.
5. N. Roy and A.McCallum. Toward optimal active learning through sampling estimation of error reduction. In Proc. ICML, 2001.
6. S. Tong and E. Chang. Support vector machine active learning for image retrieval. In Proc. ACM Multimedia, 2001.
7. M. Warmuth, G. Rätsch, M. Mathieson, J. Liao, and C. Lemmen. Active learning in the drug discovery process. In Adv. NIPS 14, 2002.
8. X. Zhu, J. Lafferty, and Z. Ghahramani. Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions. In Proc. ICML, 2003.
9. A I. Schein. Active Learning for Logistic Regression. Ph.D. diss., U. Penn., 2005. CIS Dpt.
10. P. McCullagh and J. A. Nelder. Generalized Linear Models. Chapman and Hall, 1989.
11. T. Zhang and F. J. Oles. A probability analysis on the value of unlabeled data for classification problems. In Proc. ICML, 2000.
12. O. Chapelle. Active learning for parzen window classifier. In Proc. AISTATS, 2005.
13. H. Shimodaira. Improving predictive inference under covariate shift by weighting the log-likelihood function. J. Stat. Plan. Inf., 90:227-244, 2000.
14. S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge Univ. Press, 2003.
15. J. Shawe-Taylor and N. Cristianini. Kernel Methods for Pattern Analysis. Cambridge Univ. Press, 2004.
16. S. Fine and K. Scheinberg. Efficient SVMtraining using low-rank kernel representations. J. Mach. Learn. Res., 2:243-264, 2001.
17. S. Tong and D. Koller. Support vector machine active learning with applications to text classification. In Proc. ICML, 2000.
18. K. Fukumizu. Active learning in multilayer perceptrons. In Adv. NIPS 8, 1996.
19. T. Kanamori and H. Shimodaira. Active learning algorithm using the maximum weighted log-likelihood estimator. J. Stat. Plan. Inf., 116:149-162, 2003.
20. T. Kanamori. Statistical asymptotic theory of active learning. Ann. Inst. Stat. Math., 54(3):459-475, 2002.
21. H. White. Maximum likelihood estimation of misspecified models. Econometrica, 50(1):1-26, 1982.
22. H. Akaike. A new look at statistical model identification. IEEE Trans. Aut. Cont., 19:716-722, 1974.
23. F. R. Bach. Active learning for misspecified generalized linear models. Technical Report N15/06/MM, Ecole des Mines de Paris, 2006.
24. A. W. Van der Vaart. Asymptotic Statistics. Cambridge Univ. Press, 1998.
25. D. MacKay. Information-based objective functions for active data selection. Neural Computation, 4(4):590-604, 1992.
26. Y. Bengio and Y Grandvalet. Semi-supervised learning by entropy minimization. In Adv. NIPS 17, 2005.