Agnostic active learning

ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning

Volumn 2006, Issue , 2006, Pages 65-72

  Balcan, Maria Florina ^a   Beygelzimer, Alina ^b   Langford, John ^c  

^a Carnegie Mellon University   (United States)

^b IBM T J WATSON RESEARCH CENTER   (United States)

^c TOYOTA TECHNOLOGICAL INSTITUTE AT CHICAGO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVE LEARNING ALGORITHM; LINEAR SEPARATORS; SUPERVISED LEARNING;

ALGORITHMS; COMPUTATIONAL COMPLEXITY; LINEAR SYSTEMS; THRESHOLD ELEMENTS;

LEARNING SYSTEMS;

EID: 33749242994     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. Angluin, D. (1998). Queries and concept learning. Machine Learning, 2, 319-342.
2. Angluin, D. (2001). Queries revisited. Proceedings of the 12th International Conference on Learning Theory.
3. Anthony, M., & Bartlett, P. (1999). Neural Network Learning: Theoretical Foundations. Cambridge University Press.
4. Baum, E., & Lang, K. (1992). Query learning can work poorly when a human oracle is used. International Joint Conference on Neural Networks.
5. Blumer, A., Ehrenfeucht, A., Haussler, D., & Warmuth, M. (1987). Occam's razor. Information Processing Letters, 24, 377-380.
6. Bshouty, N. H., & Eiron, N. (2002). Learning monotone dnf from a teacher that almost does not answer membership queries. Journal of Machine Learning Research, 3, 49-57.
7. Burnashev, M., & Zigangirov, K. (1974). An interval estimation problem for controlled observations. Problems in Information Transmission, 10, 223-231.
8. Castro, R., Willett, R., & Nowak, R. (2005). Fast rates in regression via active learning. University of Wisconsin Technical Report ECE-05-03.
9. Cohen, D., Atlas, L., & Ladner, R. (1994). Improving generalzation with active learning. Machine Learning, 15(2), 201-221.
10. Czyzowicz, J., Mundici, D., &: Pelc, A. (1989). Ulam's searching game with lies. Journal of Combinatorial Theory, Series A, 52, 62-76.
11. Dasgupta, S. (2004). Analysis of a greedy active learning strategy. Advances in Neural Information Processing Systems (NIPS).
12. Dasgupta, S. (2005). Coarse sample complexity bounds for active learning. Neural Information Processing Systems.
13. Dasgupta, S., Kalai, A., & Monteleoni, C. (2005). Analysis of perceptron-based active learning. COLT.
14. Freund, Y., Seung, H. S., Shamir, E., & Tishby, N. (1993). Information, prediction, and query by comittee. Neural Information Processing Systems.
15. Jackson, J. (1997). An efficient membership-query algorithm for learning dnf with respect to the uniform distribution. Journal of Computer and System Sciences, 55(3).
16. Kaariainen, M. (2005). On active learning in the nonrealizable case. NIPS Workshop on Foundations of Active Learning.
17. Vapnik, V., &; Chervonenkis, A. (1971). On the uniform convergence of relative frequencies of events to their probabilities. Theory of Probability and its Applications, 16(2), 264-280.