Abstract
We generalize on-line learning to handle delays in receiving labels for instances. After receiving an instance x, the algorithm may need to make predictions on several new instances before the label for x is returned by the environment. We give two simple techniques for converting a traditional on-line algorithm into an algorithm for solving a delayed on-line problem. One technique is for instances generated by an adversary; the other is for instances generated by a distribution. We show how these techniques effect the original on-line mistake bounds by giving upper-bounds and restricted lower-bounds on the number of mistakes.
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References
Littlestone, N.: Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm. Machine Learning 2, 285–318 (1988)
Androutsopoulos, I., Koutsias, J., Chandrinos, K., Paliouras, G., Spyropoulos, C.: An evaluation of naive bayesian anti-spam filtering (2000)
Padmanabhan, V.N., Mogul, J.C.: Using predictive prefetching to improve world wide web latency. ACM SIGCOMM Computer Communication Review 26, 22–36 (1996)
Littlestone, N.: Redundant noisy attributes, attribute errors, and linear-threshold learning using winnow. In: Proceedings of the Third Annual Conference on Computational Learning Theory, pp. 147–156 (1991)
Helmbold, D.P., Long, P.M.: Tracking drifting concepts using random examples. In: Proceedings of the Third Annual Conference on Computational Learning Theory, pp. 13–23 (1991)
Kuh, A., Petsche, T., Rivest, R.L.: Learning time-varying concepts. In: Neural Information Processing Systems Three, pp. 183–189. Morgan Kaufmann Publishers, Inc., San Francisco (1991)
Auer, P., Warmuth, M.K.: Tracking the best disjunction. In: Proceedings of the 36th annual symposium on foundations of computer science, pp. 312–321. IEEE Computer Society Press, Los Alamitos (1995)
Devroye, L., Györfi, L., Lugosi, G.: A Probabilistic Theory of Pattern Recognition. Springer, New York (1991)
Littlestone, N.: Comparing several linear-threshold learning algorithms on tasks involving superfluous attributes. In: Proceeding of the Twelve International Conference on Machine Learning, pp. 353–361 (1995)
Littlestone, N., Mesterharm, C.: An apobayesian relative of winnow. In: Neural Information Processing Systems Nine, pp. 204–210. MIT Press, Cambridge (1997)
Minsky, M.L., Papert, S.A.: Perceptrons. MIT Press, Cambridge (1969)
Li, Y., Long, P.: The relaxed online maximum margin algorithm. In: Neural Information Processing Systems Twelve, pp. 498–504. MIT Press, Cambridge (2000)
Mesterharm, C.: Tracking linear-threshold concepts with winnow. Journal of Machine Learning Research 4, 819–838 (2003)
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Mesterharm, C. (2005). On-line Learning with Delayed Label Feedback. In: Jain, S., Simon, H.U., Tomita, E. (eds) Algorithmic Learning Theory. ALT 2005. Lecture Notes in Computer Science(), vol 3734. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11564089_31
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DOI: https://doi.org/10.1007/11564089_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29242-5
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