Online learning of linear classifiers

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

Volumn 2600, Issue , 2003, Pages 235-257

  Kivinen, Jyrki ^a  

^a AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; LEARNING SYSTEMS;

BREGMAN DIVERGENCES; CONVERGENCE THEOREM; LINEAR CLASSIFICATION; LINEAR CLASSIFIERS; ON-LINE ALGORITHMS; ONLINE LEARNING; PERCEPTRON ALGORITHMS; TARGET MOVEMENTS;

E-LEARNING;

EID: 35248832956     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-36434-x_8     Document Type: Article

References (27)

1. P. Auer, N. Cesa-Bianchi and C. Gentile. Adaptive and self-confident on-line learning algorithms. Journal of Computer and System Sciences, 64:48-75, 2002.
2. B. E. Boser, I. M. Guyon and V. N. Vapnik. A training algorithm for optimal margin classifiers. In Proc. 5th Annual Workshop on Computational Learning Theory, pages 144-152. ACM Press, New York, NY, 1992.
3. L. M. Bregman. The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming. USSR Computational Mathematics and Physics, 7:200-217, 1967.
4. I. Csiszar. Why least squares and maximum entropy? An axiomatic approach for linear inverse problems. The Annals of Statistics, 19:2032-2066, 1991.
5. Y. Freund and R. E. Schapire. Large margin classification using the perception algorithm. Machine Learning, 37:277-296, 1999.
6. C. Gentile. A new approximate maximal margin classification algorithm. Journal of Machine Learning Research, 2:213-242, 2001.
7. C. Gentile and N. Littlestone. The robustness of the p-norm algorithms. In Proc. 12th Annual Conference on Computational Learning Theory, pages 1-11. ACM Press, New York, NY, 1999.
8. C. Gentile and M. K. Warmuth. Hinge loss and average margin. In M. S. Kearns, S. A. Solla and D. A. Cohn, editors, Advances in Neural Information Processing Systems 11, pages 225-231. MIT Press, Cambridge, MA, 1998.
9. A. J. Grove, N. Littlestone and D. Schuurmans. General convergence results for linear discriminant updates. Machine Learning, 43:173-210, 2001.
10. D. P. Helmbold, J. Kivinen and M. K. Warmuth. Relative loss bounds for single neurons. IEEE Transactions on Neural Networks, 10:1291-1304, 1999.
11. R. Herbrich. Learning Kernel Classifiers: Theory and Algorithms. MIT Press, Cambridge, MA, 2002.
12. M. Herbster and M. K. Warmuth. Tracking the best linear predictor. Journal of Machine Learning Research, 1:281-309, 2001.
13. K.-U. Hoffgen, H.-U. Simon and K. S. Van Horn. Robust trainability of single neurons. Journal of Computer and System Sciences, 50:114-125, 1995.
14. L. Jones and C. Byrne. General entropy criteria for inverse problems, with applications to data compression, pattern classification and cluster analysis. IEEE Transactions on Information Theory, 36:23-30, 1990.
15. J. Kivinen, A. J. Smola and R. C. Williamson. Online learning with kernels. In T. G. Dietterich, S. Becker and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14, pages 785-792. MIT Press, Cambridge, MA, 2002.
16. J. Kivinen and M. K. Warmuth. Additive versus exponentiated gradient updates for linear prediction. Information and Computation, 132:1-64, 1997.
17. J. Kivinen and M. K. Warmuth. Relative loss bounds for multidimensional regression problems. Machine Learning, 45:301-329, 2001.
18. J. Kivinen, M. K. Warmuth and P. Auer. The Perceptron algorithm vs. Winnow: linear vs. logarithmic mistake bounds when few input variables are relevant. Artificial Intelligence, 97:325-343, 1997.
19. J. Kivinen, A. J. Smola and R. C. Williamson. Large margin classification for moving targets. In N. Cesa-Bianchi, M. Numao and R. Reischuk, editors, Proc. 13th International Conference on Algorithmic Learning Theory. Springer, Berlin, November 2002.
20. N. Littlestone. Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm. Machine Learning, 2:285-318, 1988.
21. N. Littlestone. Mistake Bounds and Logarithmic Linear-threshold Learning Algorithms. PhD thesis, Technical Report UCSC-CRL-89-11, University of California Santa Cruz, 1989.
22. C. Mesterharm. Tracking linear-threshold concepts with Winnow. In J. Kivinen and B. Sloan, editors, Proc. 15th Annual Conference on Computational Learning Theory, pages 138-152. Springer, Berlin, 2002.
23. A. B. J. Novikoff. On convergence proofs on perceptions. In Proceedings of the Symposium on the Mathematical Theory of Automata, volume 12, pages 615-622. Polytechnic Institute of Brooklyn, 1962.
24. G. Ratsch and M. K. Warmuth. Maximizing the margin with boosting. In J. Kivinen and B. Sloan, editors, Proc. 15th Annual Conference on Computational Learning Theory, pages 334-350. Springer, Berlin, 2002.
25. R. Rockafellar. Convex Analysis. Princeton University Press, 1970.
26. V. N. Vapnik. Estimation of Dependences Based on Empirical Data. Springer, New York, NY, 1982.
27. ∼amai, 1998.