Abstract
We study the problem of learning in the presence of a drifting target concept. Specifically, we provide bounds on the error rate at a given time, given a learner with access to a history of independent samples labeled according to a target concept that can change on each round. One of our main contributions is a refinement of the best previous results for polynomial-time algorithms for the space of linear separators under a uniform distribution. We also provide general results for an algorithm capable of adapting to a variable rate of drift of the target concept. Some of the results also describe an active learning variant of this setting, and provide bounds on the number of queries for the labels of points in the sequence sufficient to obtain the stated bounds on the error rates.
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Awasthi, P., Balcan, M.F., Long, P.M.: The power of localization for efficiently learning linear separators with noise. arXiv:1307.8371v2 (2013)
Balcan, M.F., Beygelzimer, A., Langford, J.: Agnostic active learning. In: Proceedings of the 23rd International Conference on Machine Learning (2006)
Balcan, M.-F., Broder, A., Zhang, T.: Margin based active learning. In: Bshouty, N.H., Gentile, C. (eds.) COLT. LNCS (LNAI), vol. 4539, pp. 35–50. Springer, Heidelberg (2007)
Balcan, M.F., Long, P.M.: Active and passive learning of linear separators under log-concave distributions. In: Proceedings of the 26th Conference on Learning Theory (2013)
Bartlett, P.L., Ben-David, S., Kulkarni, S.R.: Learning changing concepts by exploiting the structure of change. Machine Learning 41, 153–174 (2000)
Bartlett, P.L., Helmbold, D.P.: Learning changing problems (1996) (unpublished)
Barve, R.D., Long, P.M.: On the complexity of learning from drifting distributions. Information and Computation 138(2), 170–193 (1997)
Crammer, K., Mansour, Y., Even-Dar, E., Vaughan, J.W.: Regret minimization with concept drift. In: Proceedings of the 23rd Conference on Learning Theory, pp. 168–180 (2010)
Dasgupta, S., Kalai, A., Monteleoni, C.: Analysis of perceptron-based active learning. Journal of Machine Learning Research 10, 281–299 (2009)
El-Yaniv, R., Wiener, Y.: Active learning via perfect selective classification. Journal of Machine Learning Research 13, 255–279 (2012)
Hanneke, S.: A bound on the label complexity of agnostic active learning. In: Proceedings of the 24th International Conference on Machine Learning (2007)
Hanneke, S.: Activized learning: Transforming passive to active with improved label complexity. Journal of Machine Learning Research 13(5), 1469–1587 (2012)
Hanneke, S., Kanade, V., Yang, L.: Learning with a drifting target concept. arXiv:1505.05215 (2015)
Haussler, D., Littlestone, N., Warmuth, M.: Predicting \(\{0,1\}\)-functions on randomly drawn points. Information and Computation 115, 248–292 (1994)
Helmbold, D.P., Long, P.M.: Tracking drifting concepts by minimizing disagreements. Machine Learning 14(1), 27–45 (1994)
Long, P.M.: The complexity of learning according to two models of a drifting environment. Machine Learning 37(3), 337–354 (1999)
Vapnik, V.: Statistical Learning Theory. John Wiley & Sons Inc., New York (1998)
Vapnik, V., Chervonenkis, A.: On the uniform convergence of relative frequencies of events to their probabilities. Theory of Probability and its Applications 16, 264–280 (1971)
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Hanneke, S., Kanade, V., Yang, L. (2015). Learning with a Drifting Target Concept. In: Chaudhuri, K., GENTILE, C., Zilles, S. (eds) Algorithmic Learning Theory. ALT 2015. Lecture Notes in Computer Science(), vol 9355. Springer, Cham. https://doi.org/10.1007/978-3-319-24486-0_10
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DOI: https://doi.org/10.1007/978-3-319-24486-0_10
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