D. Angluin. Queries and concept learning. Machine Learning
, 2(4):319–342, 1988.Google Scholar
R. Armstrong, D. Freitag, T. Joachims, and T. Mitchell. Webwatcher: A learning apprentice for the world wide web. In 1995 AAAI Spring Symposium on Information Gathering from Heterogeneous Distributed Environments, March 1995.
P. Auer and M.K. Warmuth. Tracking the best disjunction. In Proceedings of the 36th Annual Symposium on Foundations of Computer Science, pages 312–321, 1995.
D. Blackwell. An analog of the minimax theorem for vector payoffs. Pacific J. Math.
, 6:1–8, 1956.Google Scholar
A. Blum. Learning boolean functions in an infinite attribute space. Machine Learning
, 9:373–386, 1992.Google Scholar
A. Blum. Separating distribution-free and mistake-bound learning models over the boolean domain. SIAM J. Computing
, 23(5):990–1000, October 1994.CrossRefGoogle Scholar
A. Blum. Empirical support for winnow and weighted-majority based algorithms: results on a calendar scheduling domain. In Proceedings of the Twelflh International Conference on Machine Learning, pages 64–72, July 1995.
A. Blum and C. Burch. On-line learning and the metrical task system problem. In Proceedings of the 10th Annual Conference on Computational Learning Theory, pages 45–53, 1997.
A. Blum, L. Hellerstein, and N. Littlestone. Learning in the presence of finitely or infinitely many irrelevant attributes. J. Comp. Syst. Sci.
, 50(1):32–40, 1995.CrossRefGoogle Scholar
A. Blum and A. Kalai. Universal portfolios with and without transaction costs. In Proceedings of the 10th Annual Conference on Computational Learning Theory, pages 309–313, 1997.
N. Cesa-Bianchi, Y. Freund, D. P. Helmbold, and M. Warmuth. On-line prediction and conversion strategies. In Computational Learning Theory: Eurocolt '93
, volume New Series Number 53 of The Institute of Mathematics and its Applications Conference Series
, pages 205–216, Oxford, 1994. Oxford University Press.Google Scholar
N. Cesa-Bianchi, Y. Freund, D.P. Helmbold, D. Haussler, R.E. Schapire, and M.K. Warmuth. How to use expert advice. In Annual ACM Symposium on Theory of Computing, pages 382–391, 1993.
T.M. Cover. Universal portfolios. Mathematical Finance
, 1(1):1–29, January 1991.Google Scholar
T.M. Cover and E. Ordentlich. Universal portfolios with side information. IEEE Transactions on Information Theory, 42(2), March 1996.
A. DeSantis, G. Markowsky, and M. Wegman. Learning probabilistic prediction functions. In Proceedings of the 29th IEEE Symposium on Foundations of Computer Science, pages 110–119, Oct 1988.
M. Feder, N. Merhav, and M. Gutman. Universal prediction of individual sequences. IEEE Transactions on Information Theory
, 38:1258–1270, 1992.CrossRefGoogle Scholar
D.P. Foster and R.V. Vohra. A randomization rule for selecting forecasts. Operations Research
, 41:704–709, 1993.Google Scholar
Y. Freund. Predicting a binary sequence almost as well as the optimal biased coin. In Proceedings of the 9th Annual Conference on Computational Learning Theory, pages 89–98, 1996.
Y. Freund and R. Schapire. Game theory, on-line prediction and boosting. In Proceedings of the 9th Annual Conference on Computational Learning Theory, pages 325–332, 1996.
D. Helmbold, R. Sloan, and M. K. Warmuth. Learning nested differences of intersection closed concept classes. Machine Learning
, 5(2):165–196, 1990.Google Scholar
M. Kearns. Efficient noise-tolerant learning from statistical queries. In Proceedings of the Twenty-Fifth Annual ACM Symposium on Theory of Computing, pages 392–401, 1993.
M. Kearns, M. Li, L. Pitt, and L. Valiant. On the learnability of boolean formulae. In Proceedings of the Nineteenth Annual ACM Symposium on the Theory of Computing, pages 285–295, New York, New York, May 1987.
M. Kearns, R. Schapire, and L. Sellie. Toward efficient agnostic learning. Machine Learning
, 17(2/3):115–142, 1994.Google Scholar
N. Littlestone. Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm. Machine Learning
, 2:285–318, 1988.Google Scholar
N. Littlestone. personal communication (a mistake-bound version of Rivest's decision-list algorithm), 1989.
N. Littlestone. Redundant noisy attributes, attribute errors, and linear-threshold learning using winnow. In Proceedings of the Fourth Annual Workshop on Computational Learning Theory
, pages 147–156, Santa Cruz, California, 1991. Morgan Kaufmann.Google Scholar
N. Littlestone, P. M. Long, and M. K. Warmuth. On-line learning of linear functions. In Proc. of the 23rd Symposium on Theory of Computing
, pages 465–475. ACM Press, New York, NY, 1991. See also UCSC-CRL-91-29.Google Scholar
N. Littlestone and M. K. Warmuth. The weighted majority algorithm. Information and Computation
, 108(2):212–261, 1994.Google Scholar
N. Merhav and M. Feder. Universal sequential learning and decisions from individual data sequences. In Proc. 5th Annual Workshop on Comput. Learning Theory
, pages 413–427. ACM Press, New York, NY, 1992.Google Scholar
E. Ordentlich and T.M. Cover. On-line portfolio selection. In COLT 96, pages 310–313, 1996. A journal version is to be submitted to Mathematics of Operations Research.
R.L. Rivest. Learning decision lists. Machine Learning
, 2(3):229–246, 1987.Google Scholar
H. Robbins. Asymptotically subminimax solutions of compound statistical decision problems. In Proc. 2nd Berkeley Symp. Math. Statist. Prob., pages 131–148, 1951.
J. Shtarkov. Universal sequential coding of single measures. Problems of Information Transmission, pages 175–185, 1987.
L.G. Valiant. A theory of the learnable. Comm. ACM
, 27(11):1134–1142, November 1984.CrossRefGoogle Scholar
V. Vovk. Aggregating strategies. In Proceedings of the Third Annual Workshop on Computational Learning Theory, pages 371–383. Morgan Kaufmann, 1990.
V. G. Vovk. A game of prediction with expert advice. In Proceedings of the 8th Annual Conference on Computational Learning Theory
, pages 51–60. ACM Press, New York, NY, 1995.Google Scholar