A batch ensemble approach to active learning with model selection

Neural Networks

Volumn 21, Issue 9, 2008, Pages 1278-1286

  Sugiyama, Masashi ^a   Rubens, Neil ^a  

^a TOKYO INSTITUTE OF TECHNOLOGY   (Japan)

Author keywords

Active learning; Batch learning; Covariate shift; Generalization error; Importance sampling; Linear regression; Model selection; Sequential learning

Indexed keywords

REGRESSION ANALYSIS;

ACTIVE LEARNING; BATCH LEARNING; COVARIATE SHIFT; GENERALIZATION ERROR; IMPORTANCE SAMPLING; MODEL SELECTION; SEQUENTIAL LEARNING;

EDUCATION;

ARTICLE; CALCULATION; LEARNING; MATHEMATICAL MODEL; PRIORITY JOURNAL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DATA INTERPRETATION, STATISTICAL; LINEAR MODELS; MODELS, NEUROLOGICAL; MODELS, STATISTICAL;

EID: 54449091582     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neunet.2008.06.004     Document Type: Article

References (23)

1. Akaike H. A new look at the statistical model identification. IEEE Transactions on Automatic Control AC-19 (1974) 716-723
2. Bach F. Active learning for misspecified generalized linear models. In: Schölkopf B., Platt J., and Hoffman T. (Eds). Advances in neural information processing systems 19 (2007), MIT Press, Cambridge, MA
3. Cohn D.A., Ghahramani Z., and Jordan M.I. Active learning with statistical models. Journal of Artificial Intelligence Research 4 (1996) 129-145
4. Craven P., and Wahba G. Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation. Numerische Mathematik 31 (1979) 377-403
5. Fedorov V.V. Theory of optimal experiments (1972), Academic Press, New York
6. Fukumizu K. Statistical active learning in multilayer perceptrons. IEEE Transactions on Neural Networks 11 (2000) 17-26
7. Henkel R.E. Tests of significance (1979), SAGE Publication, Beverly Hills
8. Kanamori T., and Shimodaira H. Active learning algorithm using the maximum weighted log-likelihood estimator. Journal of Statistical Planning and Inference 116 (2003) 149-162
9. MacKay D.J.C. Bayesian interpolation. Neural Computation 4 (1992) 415-447
10. MacKay D.J.C. Information-based objective functions for active data selection. Neural Computation 4 (1992) 590-604
11. Rao C.R. Linear statistical inference and its applications (1965), Wiley, New York
12. Rasmussen C.E., Neal R.M., Hinton G.E., van Camp D., Revow M., Ghahramani M., et al. The DELVE manual (1996)
13. Rissanen J. Modeling by shortest data description. Automatica 14 (1978) 465-471
14. Schwarz G. Estimating the dimension of a model. The Annals of Statistics 6 (1978) 461-464
15. Shimodaira H. Improving predictive inference under covariate shift by weighting the log-likelihood function. Journal of Statistical Planning and Inference 90 (2000) 227-244
16. Sugiyama M. Active learning in approximately linear regression based on conditional expectation of generalization error. Journal of Machine Learning Research 7 (2006) 141-166
17. Sugiyama M., Krauledat M., and Müller K.-R. Covariate shift adaptation by importance weighted cross validation. Journal of Machine Learning Research 8 (2007) 985-1005
18. Sugiyama M., and Müller K.-R. Input-dependent estimation of generalization error under covariate shift. Statistics & Decisions 23 (2005) 249-279
19. Sugiyama M., and Ogawa H. Active learning with model selection-Simultaneous optimization of sample points and models for trigonometric polynomial models. IEICE Transactions on Information and Systems E86-D (2003) 2753-2763
20. Watanabe S. Algebraic analysis for nonidentifiable learning machines. Neural Computation 13 (2001) 899-933
21. White H. Maximum likelihood estimation of misspecified models. Econometrica 50 (1982) 1-25
22. Wiens D.P. Robust weights and designs for biased regression models: Least squares and generalized M-estimation. Journal of Statistical Planning and Inference 83 (2000) 395-412
23. Zadrozny B. Learning and evaluating classifiers under sample selection bias. Proceedings of the twenty-first international conference on machine learning (2004), ACM Press, New York, NY