Simulating access to hidden information while learning

Proceedings of the Annual ACM Symposium on Theory of Computing

Volumn Part F129502, Issue , 1994, Pages 263-272

  Auer, Peter ^a   Long, Philip M ^b  

^a GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

^b DUKE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATION THEORY; EQUIVALENCE CLASSES; ALGORITHMS; BOOLEAN FUNCTIONS; COMPUTATIONAL COMPLEXITY; DATA ACQUISITION; DATA REDUCTION; MATHEMATICAL MODELS; ONLINE SYSTEMS; PROBABILISTIC LOGICS; RANDOM PROCESSES; THEOREM PROVING;

ALGORITHM TRANSFORMATION; COMPUTATIONALLY EFFICIENT; DETERMINISTIC ALGORITHMS; EQUIVALENCE QUERIES; HIDDEN INFORMATION; NOISE-FREE ENVIRONMENTS; PAC LEARNING MODEL; PROBABILISTIC LEARNING;

LEARNING ALGORITHMS; LEARNING SYSTEMS;

HIDDEN INFORMATION; PROBABILISTIC LEARNING ALGORITHMS;

EID: 0027986367     PISSN: 07378017     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/195058.195152     Document Type: Conference Paper

References (23)

1. D. Angluin. Queries and concept learning. Machine Learning, 2:319-342, 1988.
2. D. Angluin and M. Kharitonov. When won't membership queries help? In Proceedings of the 23rd Annual ACM Symposium on Theory of Computing, pages 444-454, New Orleans, May 1991. ACM.
3. P. Auer and P.M. Long. Structural results for on-line learning models with and without queries, 1993. Submitted.
4. P. Auer, P.M. Long, W. Maass, and G.J. Woeginger. On the complexity of function learning. The 1993 Workshop on Computational Learning Theory, 1993.
5. G. Benedek and A. Itai. Learnability with respect to fixed distributions. Theoretical Computer Science 86(2):377-389, 1991.
6. A. Blumer, A. Ehrenfeucht, D. Haussler, and M.K. Warmuth. Learnability and the Vapnik- Chervonenkis dimension. JACM, 36(4):929-965, 1989.
7. N.H. Bshouty, S.A. Goldman, T.R. Hancock, and S. Matar. Asking questions to minimize errors. The 1993 Workshop on Computational Learning Theory, 1993.
8. N. Cesa-Bianchi, Y. Freund, D. Helmbold, and M.K. Warmuth. On-line prediction and conversion strategies. The 1993 IMA European conference on Computational Learning Theory, 1993.
9. N. Cesa-Bianchi, Y. Freund, D.P. Helmbold, D. Haussler, R.E. Schapire, and M.K. Warmuth. How to use expert advice. Proceedings of the 25th ACM Symposium on the Theory of Computation, 1993.
10. A. Ehrenfeucht, D. Haussler, M. Kearns, and L.G. Valiant. A general lower bound on the number of examples needed for learning. Information and Computation, 82(3):247-251, 1989.
11. B. Eisenberg and R.L. Rivest. On the sample complexity of PAC-learning using random and chosen examples. The 1990 Workshop on Computational Learning Theory, pages 154-162, 1990.
12. D. Haussler, N. Littlestone, and M.K. Warmuth. Predicting {0, l}-functions on randomly drawn points. Technical Report UCSC-CRL-90-54, University of California Santa Cruz, Computer Research Laboratory, December 1990. To appear in Information and Computation.
13. S.R. Kulkarni, S.K. Mitter, and J.N. Tsitsiklis. Active learning using arbitrary binary valued queries. Machine Learning, 11(1), 1993.
14. N. Littlestone. Learning quickly when irrelevant attributes abound: A new linear-Threshold algorithm. Machine Learning, 2:285-318, 1988.
15. N. Littlestone. Mistake Bounds and Logarithmic Linear-Threshold Learning Algorithms. PhD thesis, UC Santa Cruz, 1989.
16. N. Littlestone and M.K. Warmuth. The weighted majority algorithm. Technical Report UCSC-CRL- 91-28, UC Santa Cruz, October 1991. To appear, Information and Computation.
17. W. Maass. On-line learning with an oblivious environment and the power of randomization. The 1991 Workshop on Computational Learning Theory, pages 167-175, 1991.
18. W. Maass and G. Turan. Lower bound methods and separation results for on-line learning models. Machine Learning, 9:107-145, 1992.
19. G. Turan. Lower bounds for PAC learning with queries. The 1993 Workshop on Computational Learning Theory, 1993.
20. L.G. Valiant. A theory of the learnable. Communications of the ACM, 27(11):1134-1142, 1984.
21. V.N. Vapnik and A.Y. Chervonenkis. On the uniform convergence of relative frequencies of events to their probabilities. Theory of Probability and its Applications, 16(2):264-280, 1971.
22. V. Vovk. Aggregating strategies. In Proceedings of the 3nd Workshop on Computational Learning Theory, pages 371-383. Morgan Kaufmann, 1990.
23. V. Vovk. Universal forecasting algorithms. Information and Computation, 96(2):245-277, 1992.