Online Algorithms pp 306-325 | Cite as

# On-Line Algorithms in Machine Learning

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## Keywords

Boolean Function Target Function Competitive Ratio Concept Class Target Concept
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## References

- 1.D. Angluin. Queries and concept learning.
*Machine Learning*, 2(4):319–342, 1988.Google Scholar - 2.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.Google Scholar - 3.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.Google Scholar - 4.D. Blackwell. An analog of the minimax theorem for vector payoffs.
*Pacific J. Math.*, 6:1–8, 1956.Google Scholar - 5.A. Blum. Learning boolean functions in an infinite attribute space.
*Machine Learning*, 9:373–386, 1992.Google Scholar - 6.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 - 7.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.Google Scholar - 8.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.Google Scholar - 9.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 - 10.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.Google Scholar - 11.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 - 12.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.Google Scholar - 13.T.M. Cover. Universal portfolios.
*Mathematical Finance*, 1(1):1–29, January 1991.Google Scholar - 14.T.M. Cover and E. Ordentlich. Universal portfolios with side information.
*IEEE Transactions on Information Theory*, 42(2), March 1996.Google Scholar - 15.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.Google Scholar - 16.M. Feder, N. Merhav, and M. Gutman. Universal prediction of individual sequences.
*IEEE Transactions on Information Theory*, 38:1258–1270, 1992.CrossRefGoogle Scholar - 17.D.P. Foster and R.V. Vohra. A randomization rule for selecting forecasts.
*Operations Research*, 41:704–709, 1993.Google Scholar - 18.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.Google Scholar - 19.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.Google Scholar - 20.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 - 21.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.Google Scholar - 22.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.Google Scholar - 23.M. Kearns, R. Schapire, and L. Sellie. Toward efficient agnostic learning.
*Machine Learning*, 17(2/3):115–142, 1994.Google Scholar - 24.N. Littlestone. Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm.
*Machine Learning*, 2:285–318, 1988.Google Scholar - 25.N. Littlestone. personal communication (a mistake-bound version of Rivest's decision-list algorithm), 1989.Google Scholar
- 26.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 - 27.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 - 28.N. Littlestone and M. K. Warmuth. The weighted majority algorithm.
*Information and Computation*, 108(2):212–261, 1994.Google Scholar - 29.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 - 30.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*.Google Scholar - 31.R.L. Rivest. Learning decision lists.
*Machine Learning*, 2(3):229–246, 1987.Google Scholar - 32.H. Robbins. Asymptotically subminimax solutions of compound statistical decision problems. In
*Proc. 2nd Berkeley Symp. Math. Statist. Prob.*, pages 131–148, 1951.Google Scholar - 33.J. Shtarkov. Universal sequential coding of single measures.
*Problems of Information Transmission*, pages 175–185, 1987.Google Scholar - 34.L.G. Valiant. A theory of the learnable.
*Comm. ACM*, 27(11):1134–1142, November 1984.CrossRefGoogle Scholar - 35.V. Vovk. Aggregating strategies. In
*Proceedings of the Third Annual Workshop on Computational Learning Theory*, pages 371–383. Morgan Kaufmann, 1990.Google Scholar - 36.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

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