Selective Sampling Using the Query by Committee Algorithm

Machine Learning

Volumn 28, Issue 2-3, 1997, Pages 133-168

  Freund, Yoav ^a   Seung, H Sebastian ^b   Shamir, Eli ^c   Tishby, Naftali ^c  

^a AT AND T LABS RESEARCH   (United States)

^b LUCENT TECHNOLOGIES   (United States)

^c HEBREW UNIVERSITY OF JERUSALEM   (Israel)

Author keywords

Bayesian Learning; Experimental design; Query learning; Selective sampling

Indexed keywords

LEARNING SYSTEMS; NEURAL NETWORKS; RANDOM PROCESSES; SAMPLING;

BAYESIAN LEARNING; QUERY BY COMMITTEE ALGORITHM;

LEARNING ALGORITHMS;

EID: 0031209604     PISSN: 08856125     EISSN: None     Source Type: Journal    
DOI: 10.1023/a:1007330508534     Document Type: Article

References (20)

1. Dana Angluin. Queries and concept learning. Machine Learning, 2(4):319-342, April 1988.
2. A. C. Atkinson and A. N. Donev. Optimum Experimental Designs. Oxford science publications, 1992.
3. Ian Barland. Some ideas on learning with directional feedback. Master's thesis, University of California at Santa Cruz, June 1992.
4. E. Baum. Neural net algorithms that learn in polynomial time from examples and queries. IEEE Transactions on Neural Networks, 2:5-19, 1991.
5. E. B. Baum and K. Lang. Query learning can work poorly when a human oracle is used. In International Joint Conference in Neural Networks, Beijing, China, 1992.
6. A. Blumer, A. Ehrenfeucht, D. Haussler, and M. K. Warmuth. Learnability and the Vapnik-Chervonenkis dimension. J. ACM, 36(4):929-965, 1989.
7. T. Bonnesen and W. Fenchel. Theory of Convex Bodies. BCS Associates, Moscow, Idaho, USA, 1987.
8. David Cohn, Les Atlas, and Richard Ladner. Training connectionist networks with queries and selective sampling. In D. Touretzky, editor, Advances in Neural Information Processing Systems 2, San Mateo, CA, 1990. Morgan Kaufmann.
9. Ido Dagan and Sean P. Engelson. Committee-based sampling for training probabilistic classfiers. In Priedits and Russel, editors, The XII International Conference on Machine Learning, pages 150-157. Morgan Kaufmann, 1995.
10. Bonnie Eisenberg and Ronald L. Rivest. On the sample complexity of pac-learning using random and chosen examples. In Proceedings of the 1990 Workshop on Computational Learning Theory, pages 154-162, 1990.
11. V. V. Fedorov. Theory of Optimal Experiments. Academic Press, New York, 1972.
12. David Haussler, Michael Kearns, and Robert E. Schapire. Bounds on the sample complexity of Bayesian learning using information theory and the VC dimension. Machine Learning, 14:83-113, 1994.
13. W. Kinzel and P. Ruján. Improving a network generalization ability by selecting examples. Europhys. Lett., 13:473-477, 1990.
14. D. V. Lindley. On a measure of the information provided by an experiment. Ann. Math. Statist., 27:986-1005, 1956.
15. C. McDiarmid. On the method of bounded differences. In Survey of Combinatorics, 10th British Combinatorial Conference, 1989.
16. T.M. Mitchell. Generalization as search. Artificial Intelligence, 18(2), 1982.
17. N. Sauer. On the density of families of sets. J. Combinatorial Theory (A), 13:145-147, 1972.
18. H.S Seung, M. Opper, and H. Sompolinsky. Query by committee. In Proceedings of the Fifth Workshop on Computational Learning Theory, pages 287-294, San Mateo, CA, 1992. Morgan Kaufmann.
19. Peter Smith. Convexity Methods in Variational Calculus. Research studies press, John Wiley & Sons, 1985.
20. L. G. Valiant. A theory of the learnable. Communications of the ACM, 27(11):1134-1142, November 1984.