Batch-mode active learning via error bound minimization

Uncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014

Volumn , Issue , 2014, Pages 300-309

  Gu, Quanquan ^a   Zhang, Tong ^b   Han, Jiawei ^a  

^a University of Illinois at Urbana Champaign   (United States)

^b RUTGERS UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ITERATIVE METHODS; LEARNING SYSTEMS; REGRESSION ANALYSIS; SAMPLING;

ACTIVE LEARNING METHODS; ACTIVE-LEARNING ALGORITHM; BATCH MODE ACTIVE LEARNING; CLASS DISTRIBUTIONS; ESTIMATION ERRORS; LOGISTIC REGRESSIONS; PASSIVE LEARNING; STATE OF THE ART;

ARTIFICIAL INTELLIGENCE;

EID: 84923270964     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (30)

1. A. Agarwal. Selective sampling algorithms for costsensitive multiclass prediction. In ICML (3), pages 1220-1228, 2013.
2. A. D. Anthony Atkinson and R. Tobias. Optimum Experimental Designs. Oxford Statistical Science Series. Oxford University Press, May 2007.
3. A. Asuncion and D. Newman. UCI machine learning repository, 2007.
4. J. Azimi, A. Fern, and X. Fern. Batch bayesian optimization via simulation matching. In NIPS, pages 109-117, 2010.
5. M.-F. Balcan, A. Beygelzimer, and J. Langford. Agnostic active learning. In ICML, pages 65-72, 2006.
6. M. Belkin, P. Niyogi, and V. Sindhwani. Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research, 7:2399-2434, 2006.
7. A. Beygelzimer, D. Hsu, J. Langford, and T. Zhang. Agnostic active learning without constraints. In NIPS, pages 199-207, 2010.
8. O. Bousquet, S. Boucheron, and G. Lugosi. Introduction to statistical learning theory. In Advanced Lectures on Machine Learning, pages 169-207, 2003.
9. S. Boyd and L. Vandenberghe. Convex optimization. Cambridge University Press, Cambridge, 2004.
10. Y. Chen and A. Krause. Near-optimal batch mode active learning and adaptive submodular optimization. In International Conference on Machine Learning (ICML), 2013.
11. D. A. Cohn, L. E. Atlas, and R. E. Ladner. Improving generalization with active learning. Machine Learning, 15(2):201-221, 1994.
12. S. Dasgupta, D. Hsu, and C. Monteleoni. A general agnostic active learning algorithm. In NIPS, 2007.
13. Y. Freund, H. S. Seung, E. Shamir, and N. Tishby. Selective sampling using the query by committee algorithm. Machine Learning, 28(2-3):133-168, 1997.
14. Q. Gu, T. Zhang, C. H. Q. Ding, and J. Han. Selective labeling via error bound minimization. In NIPS, pages 332-340, 2012.
15. A. Guillory and J. A. Bilmes. Label selection on graphs. In NIPS, pages 691-699, 2009.
16. Y. Guo. Active instance sampling via matrix partition. In NIPS, pages 802-810, 2010.
17. Y. Guo and D. Schuurmans. Discriminative batch mode active learning. In NIPS, 2007.
18. S. Hanneke. Rates of convergence in active learning. The Annals of Statistics, 39(1):333-361, 2011.
19. T. Hastie, R. Tibshirani, and J. H. Friedman. The elements of statistical learning: data mining, inference, and prediction: with 200 full-color illustrations. New York: Springer-Verlag, 2001.
20. S. C. H. Hoi, R. Jin, J. Zhu, and M. R. Lyu. Batch mode active learning and its application to medical image classification. In ICML, pages 417-424, 2006.
21. S. C. H. Hoi, R. Jin, J. Zhu, and M. R. Lyu. Semisupervised svm batch mode active learning for image retrieval. In CVPR, 2008.
22. S.-J. Huang, R. Jin, and Z.-H. Zhou. Active learning by querying informative and representative examples. In NIPS, pages 892-900, 2010.
23. B. Korte and J. Vygen. Combinatorial Optimization: Theory and Algorithms. Springer Publishing Company, Incorporated, 4th edition, 2007.
24. A. Krause, A. P. Singh, and C. Guestrin. Near-optimal sensor placements in gaussian processes: Theory, efficient algorithms and empirical studies. Journal of Machine Learning Research, 9:235-284, 2008.
25. P. Ravikumar, M. J. Wainwright, and J. D. Lafferty. High-dimensional ising model selection using 1-regularized logistic regression. The Annals of Statistics, 38(3):1287C1319, 2010.
26. A. I. Schein and L. H. Ungar. Active learning for logistic regression: An evaluation. Machine Learning, 68(3):235-265, 2007.
27. S. Tong and D. Koller. Support vector machine active learning with applications to text classification. In ICML, pages 999-1006, 2000.
28. Z. Wang and J. Ye. Querying discriminative and representative samples for batch mode active learning. In KDD, pages 158-166, 2013.
29. K. Yu, J. Bi, and V. Tresp. Active learning via transductive experimental design. In ICML, pages 1081-1088, 2006.
30. T. Zhang and F. J. Oles. A probability analysis on the value of unlabeled data for classification problems. In ICML, 2000.