Learning permutations with exponential weights

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

Volumn 4539 LNAI, Issue , 2007, Pages 469-483

  Helmbold, David P ^a   Warmuth, Manfred K ^a  

^a University of California Santa Cruz   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BRANCH AND BOUND METHOD; MATRIX ALGEBRA; ONLINE SYSTEMS; RANDOM PROCESSES; UNCERTAINTY ANALYSIS;

LEARNING PERMUTATIONS; STOCHASTIC MATRIX; TARGET PERMUTATION;

LEARNING ALGORITHMS;

EID: 38049002079     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-72927-3_34     Document Type: Conference Paper

References (24)

1. Auer, P., Cesa-Bianchi, N., Freund, Y., Schapire, R.E.: The nonstochastic multiarmed bandit problem. SIAM Journal on Computing 32(1), 48-77 (2002)
2. Blum, A., Chawla, S., Kalai, A.: Static optimality and dynamic search-optimality in lists and trees. Algorithmica 36, 249-260 (2003)
3. Bhatia, R.: Matrix Analysis. Springer, Heidelberg (1997)
4. Balakrishnan, H., Hwang, I., Tomlin, C.: Polynomial approximation algorithms for belief matrix maintenance in identity management. In: 43rd IEEE Conference on Decision and Control, pp. 4874-4879 (December 2004)
5. Bousquet, O., Warmuth, M.K.: Tracking a small set of experts by mixing past posteriors. Journal of Machine Learning Research 3, 363-396 (2002)
6. Cesa-Bianchi, N., Lugosi, G.: Prediction, Learning, and Games. Cambridge University Press, Cambridge (2006)
7. Censor, Y., Lent, A.: An iterative row-action method for interval convex programming. Journal of Optimization Theory and Applications 34(3), 321-353 (1981)
8. Franklin, J., Lorenz, J.: On the scaling of multidimensional matrices. Linear Algebra and its applications, 114/115, 717-735 (1989)
9. 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)
10. Martin Furer. Quadratic convergence for scaling of matrices. In: Proceedings of ALENEX/ANALCO, pp. 216-223. SIAM (2004)
11. Helmbold, D.P., Schapire, R.E.: Predicting nearly as well as the best pruning of a decision tree. Machine Learning 27(01), 51-68 (1997)
12. Herbster, M., Warmuth, M.K.: Tracking the best linear predictor. Journal of Machine Learning Research 1, 281-309 (2001)
13. Kalai, A.: Simulating weighted majority with FPL. Private communication (2005)
14. Kalantari, B., Khachiyan, L.: On the complexity of nonnegative-matrix scaling. Linear Algebra and its applications 240, 87-103 (1996)
15. Kalai, A., Vempala, S.: Efficient algorithms for online decision problems (Special issue Learning Theory 2003). J. Comput. Syst. Sci. 71(3), 291-307 (2003)
16. Kivinen, J., Warmuth, M.K.: Additive versus exponentiated gradient updates for linear prediction. Information and Computation 132(1), 1-64 (1997)
17. Kivinen, J., Warmuth, M.K.: Averaging expert predictions. In: Fischer, P., Simon, H.U. (eds.) EuroCOLT 1999. LNCS (LNAI), vol. 1572, pp. 153-167. Springer, Heidelberg (1999)
18. Kuzmin, D., Warmuth, M.K.: Optimum follow the leader algorithm (Open problem). In: Auer, P., Meir, R. (eds.) COLT 2005. LNCS (LNAI), vol. 3559, pp. 684-686. Springer, Heidelberg (2005)
19. Linial, N., Samorodnitsky, A., Wigderson, A.: A deterministic strongly polynomial algorithm for matrix scaling and approximate permanents. Combinatorica 20(4), 545-568 (2000)
20. Littlestone, N., Warmuth, M.K.: The weighted majority algorithm. Inform. Comput. 108(2), 212-261 (1994)
21. McAllester, D.: PAC-Bayesian stochastic model selection. Machine Learning 51(1), 5-21 (2003)
22. Sinkhorn, R.: A relationship between arbitrary positive matrices and doubly stochastic matrices. The. Annals of Mathematical Staticstics 35(2), 876-879 (1964)
23. Takimoto, E., Warmuth, M.K.: Path kernels and multiplicative updates. Journal of Machine Learning Research 4, 773-818 (2003)
24. Warmuth, M.K., Kuzmin, D.: Randomized PCA algorithms with regret bounds that are logarithmic in the dimension. In: Advances in Neural Information Processing Systems 19 (NIPS 06), MIT Press, Cambridge (2006)