New analysis and algorithm for learning with drifting distributions

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7568 LNAI, Issue , 2012, Pages 124-138

  Mohri, Mehryar ^a,b   Muñoz Medina, Andres ^a  

^a NEW YORK UNIVERSITY   (United States)

^b GOOGLE INC   (United States)

Author keywords

domain adaptation; Drifting environment; generalization bound

Indexed keywords

CONVEX COMBINATIONS; DOMAIN ADAPTATION; DRIFTING ENVIRONMENT; GENERALIZATION BOUND; RADEMACHER COMPLEXITY;

ARTIFICIAL INTELLIGENCE;

LEARNING ALGORITHMS;

EID: 84867880979     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-34106-9_13     Document Type: Conference Paper

References (23)

1. Valiant, L.G.: A theory of the learnable. ACM Press, New York (1984)
2. Vapnik, V.N.: Statistical Learning Theory. J. Wiley & Sons (1998)
3. Cesa-Bianchi, N., Lugosi, G.: Prediction, learning, and games. Cambridge University Press (2006)
4. Herbster, M., Warmuth, M.: Tracking the best expert. Machine Learning 32(2), 151-178 (1998)
5. Herbster, M.,Warmuth,M.: Tracking the best linear predictor. Journal of Machine Learning Research 1, 281-309 (2001)
6. Cavallanti, G., Cesa-Bianchi, N., Gentile, C.: Tracking the best hyperplane with a simple budget perceptron. Machine Learning 69(2/3), 143-167 (2007)
7. Helmbold, D.P., Long, P.M.: Tracking drifting concepts by minimizing disagreements. Machine Learning 14(1), 27-46 (1994)
8. Bartlett, P.L.: Learning with a slowly changing distribution. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, COLT 1992, pp. 243-252. ACM, New York (1992)
9. Long, P.M.: The complexity of learning according to two models of a drifting environment. Machine Learning 37, 337-354 (1999)
10. Barve, R.D., Long, P.M.: On the complexity of learning from drifting distributions. Information and Computation 138(2), 101-123 (1997)
11. Freund, Y.,Mansour, Y.: Learning under Persistent Drift. In: Ben-David, S. (ed.) EuroCOLT 1997. LNCS, vol. 1208, pp. 109-118. Springer, Heidelberg (1997)
12. Bartlett, P.L., Ben-David, S., Kulkarni, S.: Learning changing concepts by exploiting the structure of change. Machine Learning 41, 153-174 (2000)
13. Crammer, K., Even-Dar, E.,Mansour, Y., Vaughan, J.W.: Regret minimization with concept drift. In: COLT, pp. 168-180 (2010)
14. Mansour, Y., Mohri, M., Rostamizadeh, A.: Domain adaptation: Learning bounds and algorithms. In: Proceedings of COLT. Omnipress, Montréal (2009)
15. Valiant, P.:Testing symmetric properties of distributions. SIAMJ.Comput. 40(6), 1927-1968 (2011)
16. Rakhlin, A., Sridharan, K., Tewari, A.: Online learning: Random averages, combinatorial parameters, and learnability (2010)
17. Cortes, C., Mohri, M.: Domain Adaptation in Regression. In: Kivinen, J., Szepesvári, C., Ukkonen, E., Zeugmann, T. (eds.) ALT 2011. LNCS, vol. 6925, pp. 308-323. Springer, Heidelberg (2011)
18. Dudley, R.M.: A course on empirical processes. Lecture Notes in Math., vol. 1097, pp. 2-142 (1984)
19. Pollard, D.: Convergence of Stochastic Processess. Springer, New York (1984)
20. Talagrand, M.: The Generic Chaining. Springer, New York (2005)
21. Littlestone, N.: From on-line to batch learning. In: Proceedings of the Second Annual Workshop on Computational Learning Theory, pp. 269-284. Morgan Kaufmann Publishers Inc. (1989)
22. Cesa-Bianchi, N., Conconi, A., Gentile, C.: On the generalization ability of on-line learning algorithms. In: NIPS, pp. 359-366 (2001)
23. Widrow, B., Hoff, M.E.: Adaptive switching circuits. Neurocomputing: Foundations of Research (1988)