skip to main content
10.1145/167088.167198acmconferencesArticle/Chapter ViewAbstractPublication PagesstocConference Proceedingsconference-collections
Article
Free Access

How to use expert advice

Published:01 June 1993Publication History
First page image

References

  1. 1.T. M. Cover. Behaviour of sequential predictors of binary sequences. In Transactions of the Fourth Prague Conference on Information Theory, Statistical Decision Functions, Random Processes, pages 263-272. Publishing House of the Czechoslovak Academy of Sciences, 1965.Google ScholarGoogle Scholar
  2. 2.T. M. Cover and A. Shanhar. CompoundBayes predictors for sequences with apparent Markov structure. IEEE Transactzons on Systems, Man and Cybernetics, SMC-7(6):421-424, Jmm 1977.Google ScholarGoogle Scholar
  3. 3.A. Dawid. Prequential data analysis. Current Issues in Statistical Inference, to appear.Google ScholarGoogle Scholar
  4. 4.A. P. Dawid. Statistical theory: The prequential approach. Journal of the Royal Statistical Society, Series A, pages 278- 292, 1984.Google ScholarGoogle Scholar
  5. 5.A. P. Dawid. Prequential analysis, stochastic complexity and Bayesian inference. Bayesian Statistics 4, to appear.Google ScholarGoogle Scholar
  6. 6.A. DeSantis, G. Markowski, and M. N. Wegman. Learning probabilistic prediction functions. In Proceedings of the 1988 Workshop on Computational Learning Theory, pages 312- 328. Morgan Kaufmann, 1988. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. 7.M. Feder, N. Merhav, and M. Gutman. Universal prediction of individual sequences. IEEE Transactions on Information Theory, 38:1258-1270, 1992.Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. 8.A. Fiat, D. Foster, H. Karloff, Y. Rabani, Y. Ravid, and S. Vishwanathan. Competitive algorithms for layered graph traversal. In 32nd Annual Symposium on Foundations of Computer Science, pages 288-297, 1991. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. 9.A. Fiat, R. Karp, M. Luby, L. McGeoch, D. Sleator, aald N. Yomlg. Competitive paging algorithms. Journal of Algorithms, 12:685-699, 1991. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. 10.A. Fiat, Y. Rabani, and Y. Ravid. Competitive k-server algorithms. In 31st Annual Symposium on Foundations of Computer Science, pages 454-463, 1990.Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. 11.J. Galambos. The Asymptotic Theory of Extreme Oreder Stat2stics. R. E. Kreiger, second edition, 1987.Google ScholarGoogle Scholar
  12. 12.3. Hamlan. Approximation to Bayes risk in repeated play. ha Contributions to the theory of games, volume 3, pages 97-139. Princeton University Press, 1957.Google ScholarGoogle Scholar
  13. 13.D. Haussler and A. Barron. How well do Bayes methods work for on-line prediction of {+1, - 1 } values? In Proceedings of the Third NEC Symposium on Computation and Cognition. SIAM, to appear.Google ScholarGoogle Scholar
  14. 14.D. Haussler, M. Kearns, N. Littlestone, and M. K. Warmuth. Equivalence of models for polynomial learnability. Information and Computation, 95:129-161, 1991. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. 15.D. Haussler, M. Kearns, and R. Schapire. Bounds on the sample complexity of Bayesian learning using information theory and the VC dimension. Machine Learning, to appear. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. 16.D. Haussler, N. Littlestone, and M. Waxinuth. Fredicting {0,1}-fualctions on randomly drawn points. Technical Report UCSC-CRL-90-54, University of California Santa Cruz, Computer Research Laboratory, Dec. 1990. To appear, Information and Computation. Google ScholarGoogle Scholar
  17. 17.D. Helmbold and M. K. Warmuth. On weak learning. In Proceedings of the Third NEC Research Symposium on Co'rnpurational Learning and Cognition. SIAM, to appear.Google ScholarGoogle Scholar
  18. 18.D. P. Hehnbold and M. K. Warmuth. Some weak lealTSng results. In Proceedings of the Fifth Annual A CM Workshop on Computational Learning Theory, pages 399-412, 19!)2. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. 19.M. J. Kearns and R. E. Schapire. Efficient distributiolL-free learning of probabilistic concepts. In 31st Annual Symposium on Foundations of Computer Science, pages 382-391, 1990.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. 20.M. J. Kearns, R. E. Schapire, and L. M. Sellie. Toward efficient agnostic learning. In Proceedings of the Fifth Annual A CM Workshop on Computational Learning Theory, pages 341-352, 1992. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. 21.N. Littlestone. From on-fine to batch learning. In Proceedings of the Second Annual Workshop on Computational Learning Theory, pages 269-284. Morgan Kaufmann, 1989. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. 22.N. Littlestone, P. M. Long, and M. K. Warmuth. On-line learning of linear functions. In Proceedings of the Twenty Third Annual A CM Symposium on Theory of Computing, pages 465-475, 1991. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. 23.N. Littlestone and M. Warmuth. The weighted majority algorithm, in 30th Annual IEEE Symposium on Foundations of Computer Science, pages 256-261, 1989. Long version: UCSC tech. rep. UCSC-CRL-91-28.Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. 24.N. Merhav and M. Feder. Universal sequential learning and decision from individual data sequences. In Proceedings of the Fifth Annual A CM Workshop on Computational Learning Theory, pages 413-427, 1992. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. 25.J. Rissanen. Modeling by shortest data description. Automatica, 14:465-471, 1978.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. 26.J. Rissanen. Stochastic complexity and modeling. The Annals of Statistics, 14(3):1080-1100, 1986.Google ScholarGoogle Scholar
  27. 27.J. Rissanen and G. G. Langdon, Jr. Universal modeling and coding. IEEE Transactions on Information Theory, IT- 27(1):12-23, Jan. 1981.Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. 28.H. S. Seung, H. Sompolinsky, and N. Tishby. Stati,#tical mechanics of learning from examples. Physical Review A, 45(8):6056-6091, 1992.Google ScholarGoogle ScholarCross RefCross Ref
  29. 29.H. Sompolinsky, N. Tishby, and H. Seung. Learning from examples in large neural networks. Physical Review Led!ters, 65:1683-1686, 1990.Google ScholarGoogle Scholar
  30. 30.M. Talagrand. Sharper bounds for Gaussian and empirical processes. Annals of Probability, to appear.Google ScholarGoogle Scholar
  31. 31.L. G. Valiant. A theory of the learnable. Communications of the ACM, 27(11):1134-42, 1984. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. 32.V. Vapnik. Principles of risk minimization for learning theory. In J. E. Moody, S. J. Hanson, and R. P. Lippman, editors, Advances in Neural information Processing Systems 4. Morgan Kaufmann, 1992.Google ScholarGoogle Scholar
  33. 33.V. N. Vapnik. Estimation of Dependences Based on Empirical Data. Springer-Verlag, 1982. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. 34.V. G. Vovk. Aggregating strategies. In Proceedings of the Third Annual Workshop on Computational Learning Theory, pages 371-383. Morgan Kaufmann, 1990. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. 35.V. G. Vovk. Prequential probability theory. Unpublished manuscript, 1990.Google ScholarGoogle Scholar
  36. 36.V. G. Vovk. Universal forcasting algorithms. Information and Computation, 96(2):245-277, Feb. 1992. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. 37.K. Yamanishi. A loss bound model for on-line stochastic prediction strategies. In Proceedings of the Fourth Annual 302. Morgan Kaufmann, 1991. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. How to use expert advice

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        STOC '93: Proceedings of the twenty-fifth annual ACM symposium on Theory of Computing
        June 1993
        812 pages
        ISBN:0897915917
        DOI:10.1145/167088

        Copyright © 1993 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 1 June 1993

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • Article

        Acceptance Rates

        Overall Acceptance Rate1,469of4,586submissions,32%

        Upcoming Conference

        STOC '24
        56th Annual ACM Symposium on Theory of Computing (STOC 2024)
        June 24 - 28, 2024
        Vancouver , BC , Canada

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader