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How to use expert advice

Published: 01 June 1993 Publication History
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References

[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.
[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.
[3]
A. Dawid. Prequential data analysis. Current Issues in Statistical Inference, to appear.
[4]
A. P. Dawid. Statistical theory: The prequential approach. Journal of the Royal Statistical Society, Series A, pages 278- 292, 1984.
[5]
A. P. Dawid. Prequential analysis, stochastic complexity and Bayesian inference. Bayesian Statistics 4, to appear.
[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.
[7]
M. Feder, N. Merhav, and M. Gutman. Universal prediction of individual sequences. IEEE Transactions on Information Theory, 38:1258-1270, 1992.
[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.
[9]
A. Fiat, R. Karp, M. Luby, L. McGeoch, D. Sleator, aald N. Yomlg. Competitive paging algorithms. Journal of Algorithms, 12:685-699, 1991.
[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.
[11]
J. Galambos. The Asymptotic Theory of Extreme Oreder Stat2stics. R. E. Kreiger, second edition, 1987.
[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.
[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.
[14]
D. Haussler, M. Kearns, N. Littlestone, and M. K. Warmuth. Equivalence of models for polynomial learnability. Information and Computation, 95:129-161, 1991.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[25]
J. Rissanen. Modeling by shortest data description. Automatica, 14:465-471, 1978.
[26]
J. Rissanen. Stochastic complexity and modeling. The Annals of Statistics, 14(3):1080-1100, 1986.
[27]
J. Rissanen and G. G. Langdon, Jr. Universal modeling and coding. IEEE Transactions on Information Theory, IT- 27(1):12-23, Jan. 1981.
[28]
H. S. Seung, H. Sompolinsky, and N. Tishby. Stati,#tical mechanics of learning from examples. Physical Review A, 45(8):6056-6091, 1992.
[29]
H. Sompolinsky, N. Tishby, and H. Seung. Learning from examples in large neural networks. Physical Review Led!ters, 65:1683-1686, 1990.
[30]
M. Talagrand. Sharper bounds for Gaussian and empirical processes. Annals of Probability, to appear.
[31]
L. G. Valiant. A theory of the learnable. Communications of the ACM, 27(11):1134-42, 1984.
[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.
[33]
V. N. Vapnik. Estimation of Dependences Based on Empirical Data. Springer-Verlag, 1982.
[34]
V. G. Vovk. Aggregating strategies. In Proceedings of the Third Annual Workshop on Computational Learning Theory, pages 371-383. Morgan Kaufmann, 1990.
[35]
V. G. Vovk. Prequential probability theory. Unpublished manuscript, 1990.
[36]
V. G. Vovk. Universal forcasting algorithms. Information and Computation, 96(2):245-277, Feb. 1992.
[37]
K. Yamanishi. A loss bound model for on-line stochastic prediction strategies. In Proceedings of the Fourth Annual 302. Morgan Kaufmann, 1991.

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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
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