Abstract
We consider the problem of learning to predict as well as the best in a group of experts making continuous predictions. We assume the learning algorithm has prior knowledge of the maximum number of mistakes of the best expert. We propose a new master strategy that achieves the best known performance for online learning with continuous experts in the mistake bounded model. Our ideas are based on drifting games, a generalization of boosting and online learning algorithms. We also prove new lower bounds based on the drifting games framework which, though not as tight as previous bounds, have simpler proofs and do not require an enormous number of experts.
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References
Abernethy, J., Langford, J., Warmuth, M.K.: Continuous experts and the binning algorithm. In: Lugosi, G., Simon, H.U. (eds.) COLT 2006. LNCS (LNAI), vol. 4005, pp. 544–558. Springer, Heidelberg (2006)
Cesa-Bianchi, N., Freund, Y., Haussler, D., Helmbold, D.P., Schapire, R.E., Warmuth, M.K.: How to use expert advice. Journal of the Association for Computing Machinery 44(3), 427–485 (1997)
Cesa-Bianchi, N., Freund, Y., Helmbold, D.P., Warmuth, M.K.: On-line prediction and conversion strategies. Machine Learning 25, 71–110 (1996)
Freund, Y.: Boosting a weak learning algorithm by majority. Information and Computation 121(2), 256–285 (1995)
Freund, Y., Opper, M.: Continuous drifting games. Journal of Computer and System Sciences, 113–132 (2002)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1), 119–139 (1997)
Littlestone, N., Warmuth, M.K.: The weighted majority algorithm. Information and Computation 108, 212–261 (1994)
Rockafellar, R.T.: Convex Analysis. Princeton University Press, Princeton (1970)
Schapire, R.E.: Drifting games. Machine Learning 43(3), 265–291 (2001)
Spencer, J.: Ulam’s searching game with a fixed number of lies. Theoret. Comput. Sci. 95(2), 307–321 (1992)
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Mukherjee, I., Schapire, R.E. (2008). Learning with Continuous Experts Using Drifting Games. In: Freund, Y., Györfi, L., Turán, G., Zeugmann, T. (eds) Algorithmic Learning Theory. ALT 2008. Lecture Notes in Computer Science(), vol 5254. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87987-9_22
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DOI: https://doi.org/10.1007/978-3-540-87987-9_22
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