Active learning via perfect selective classification

Journal of Machine Learning Research

Volumn 13, Issue , 2012, Pages 255-279

  El Yaniv, Ran ^a   Wiener, Yair ^a  

^a TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

Author keywords

Active learning; Classification with a reject option; Disagreement coefficient; Exploration vs. exploitation; Perfect classification; Selective classification; Selective sampling; Teaching dimension

Indexed keywords

ACTIVE LEARNING; COMPLEXITY MEASURES; DATA DISTRIBUTION; DISAGREEMENT COEFFICIENT; EXPLORATION VS. EXPLOITATION; FAST RATE; HIGH-DIMENSIONAL; LEARNING MODELS; LINEAR CLASSIFIERS; MIXTURE OF GAUSSIANS; NON-HOMOGENEOUS; SELECTIVE SAMPLING;

ARTIFICIAL INTELLIGENCE;

SOFTWARE ENGINEERING;

EID: 84857868393     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

References (36)

1. M. Anthony and P.L. Bartlett. Neural Network Learning; Theoretical Foundations. Cambridge University Press, 1999.
2. L. Atlas, D. Cohn, R. Ladner, A.M. El-Sharkawi, and R.J. Marks. Training connectionist networks with queries and selective sampling. In Neural Information Processing Systems (NIPS), pages 566-573, 1990.
3. M.F. Balcan, S. Hanneke, and J. Wortman. The true sample complexity of active learning. In 21st Annual Conference on Learning Theory (COLT), pages 45-56, 2008.
4. Y. Baram, R. El-Yaniv, and K. Luz. Online choice of active learning algorithms. Journal of Machine Learning Research, 5:255-291, 2004.
5. P.L. Bartlett and M.H. Wegkamp. Classification with a reject option using a hinge loss. Journal of Machine Learning Research, 9:1823-1840, 2008.
6. R. Begleiter, R. El-Yaniv, and D. Pechyony. Repairing self-confident active-transductive learners using systematic exploration. Pattern Recognition Letters, 29(9):1245-1251, 2008.
7. A. Blumer, A. Ehrenfeucht, D. Haussler, and M.K. Warmuth. Learnability and the Vapnik-Chervonenkis dimension. Journal of the ACM, 36, 1989.
8. C.K. Chow. An optimum character recognition system using decision function. IEEE Transactions on Computers, 6(4):247-254, 1957.
9. C.K. Chow. On optimum recognition error and reject trade-off. IEEE Transactions on Information Theory, 16:41-36, 1970.
10. D. Cohn, L. Atlas, and R. Ladner. Improving generalization with active learning. Machine Learning, 15(2):201-221, 1994.
11. S. Dasgupta. Coarse sample complexity bounds for active learning. In Advances in Neural Information Processing Systems 18, pages 235-242, 2005.
12. S. Dasgupta, A. Tauman Kalai, and C. Monteleoni. Analysis of perceptron-based active learning. Journal of Machine Learning Research, 10:281-299, 2009.
13. R. El-Yaniv and Y. Wiener. On the foundations of noise-free selective classification. Journal of Machine Learning Research, 11:1605-1641, 2010.
14. R. El-Yaniv and Y. Wiener. Agnostic selective classification. In Neural Information Processing Systems (NIPS), 2011.
15. S. Fine, R. Gilad-Bachrach, and E. Shamir. Query by committee, linear separation and random walks. Theoretical Computer Science, 284(1):25-51, 2002. (Pubitemid 34661528)
16. Y. Freund, H.S. Seung, E. Shamir, and N. Tishby. Information, prediction, and Query by Committee. In Advances in Neural Information Processing Systems (NIPS) 5, pages 483-490, 1993.
17. Y. Freund, H.S. Seung, E. Shamir, and N. Tishby. Selective sampling using the query by committee algorithm. Machine Learning, 28:133-168, 1997. (Pubitemid 127506338)
18. Y. Freund, Y. Mansour, and R.E. Schapire. Generalization bounds for averaged classifiers. Annals of Statistics, 32(4):1698-1722, 2004. (Pubitemid 41250282)
19. E. Friedman. Active learning for smooth problems. In Proceedings of the 22nd Annual Conference on Learning Theory (COLT), 2009.
20. R. Gilad-Bachrach. To PAC and Beyond. PhD thesis, the Hebrew University of Jerusalem, 2007.
21. S. Goldman andM. Kearns. On the complexity of teaching. JCSS: Journal of Computer and System Sciences, 50, 1995.
22. S. Hanneke. A bound on the label complexity of agnostic active learning. In ICML '07: Proceedings of the 24th international conference on Machine learning, pages 353-360, 2007a.
23. S. Hanneke. Teaching dimension and the complexity of active learning. In Proceedings of the 20th Annual Conference on Learning Theory (COLT), volume 4539 of Lecture Notes in Artificial Intelligence, pages 66-81, 2007b.
24. S. Hanneke. Theoretical Foundations of Active Learning. PhD thesis, Carnegie Mellon University, 2009.
25. S. Hanneke. Activized learning: Transforming passive to active with improved label complexity. CoRR, abs/1108.1766, 2011a. URL http://arxiv.org/abs/ 1108.1766. informal publication.
26. S. Hanneke. Rates of convergence in active learning. Annals of Statistics, 37(1):333-361, 2011b.
27. T. Hegedüs. Generalized teaching dimensions and the query complexity of learning. In COLT: Proceedings of the Workshop on Computational Learning Theory, Morgan Kaufmann Publishers, 1995.
28. R. Herbei andM.H.Wegkamp. Classification with reject option. The Canadian Journal of Statistics, 34(4):709-721, 2006.
29. W. Hoeffding. Probability inequalities for sums of bounded random variables. Journal of the American Statistical Association, 58(301):13-30, March 1963.
30. D. Hug and M. Reitzner. Gaussian polytopes: variances and limit theorems, June 2005.
31. D. Hug, G. O. Munsonious, and M. Reitzner. Asymptotic mean values of Gaussian polytopes. Beiträge Algebra Geom., 45:531-548, 2004. (Pubitemid 39519108)
32. C. McDiarmid. Concentration. In M. Habib, C. McDiarmid, J. Ramirez-Alfonsin, and B. Reed, editors, Probabilistic Methods for Algorithmic Discrete Mathematics, volume 16, pages 195-248. Springer-Verlag, 1998.
33. T. Mitchell. Version spaces: a candidate elimination approach to rule learning. In IJCAI'77: Proceedings of the 5th international joint conference on Artificial Intelligence, pages 305-310, 1977.
34. H.S. Seung, M. Opper, and H. Sompolinsky. Query by committee. In Proceedings of the Fifth Annual Workshop on Computational Learning theory (COLT), pages 287-294, 1992. (Pubitemid 23615454)
35. V. Vapnik and A. Chervonenkis. On the uniform convergence of relative frequencies of events to their probabilities. Theory of Probability and its Applications, 16:264-280, 1971.
36. M.H. Wegkamp. Lasso type classifiers with a reject option. Electronic Journal of Statistics, 1: 155-168, 2007.