Entropy regularized LPBoost

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

Volumn 5254 LNAI, Issue , 2008, Pages 256-271

  Warmuth, Manfred K ^a   Glocer, Karen A ^a   Vishwanathan, S V N ^b  

^a University of California Santa Cruz   (United States)

^b CSIRO   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL GRAMMARS; ENTROPY; LINEARIZATION;

BOOSTING ALGORITHMS; GENERALIZATION ERRORS; ITERATION BOUNDS; LINEAR COMBINATIONS; LINEAR PROGRAMMING PROBLEMS; NUMBER OF ITERATIONS; RELATIVE ENTROPIES; SOFT MARGINS;

LINEAR PROGRAMMING;

EID: 56749106067     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-87987-9_23     Document Type: Conference Paper

References (24)

1. Abe, N., Takeuchi, J., Warmuth, M.K.: Polynomial learnability of stochastic rules with respect to the KL-divergence and quadratic distance. IEICE Transactions on Information and Systems E84-D(3), 299-316 (2001)
2. Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004)
3. Demiriz, A., Bennett, K.P., Shawe-Taylor, J.: Linear programming boosting via column generation. Mach. Learn. 46(1-3), 225-254 (2002)
4. Duffy, N., Helmbold, D.: Potential boosters? In: Solla, S., Leen, T., Müller, K.-R. (eds.) Advances in Neural Information Processing Systems 12, pp. 258-264. MIT Press, Cambridge (2000)
5. Freund, Y.: Boosting a weak learning algorithm by majority. Inform. Comput. 121(2), 256-285 (1995); In: COLT 1990
6. Freund, Y.: An adaptive version of the boost by majority algorithm. In: Proceedings of the 12th annual conference on Computational learning theory, pp. 102-113. ACM Press, New York (1999)
7. Freund, Y., Schapire, R.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1), 119-139 (1997)
8. Friedman, J., Hastie, T., Tibshirani, R.: Additive Logistic Regression: a Statistical View of Boosting. The Annals of Statistics 38(2) (2000)
9. Grove, A.J., Schuurmans, D.: Boosting in the limit: maximizing the margin of learned ensembles. In: AAAI 1998/IAAI 1998, Menlo Park, CA, USA, pp. 692-699 (1998)
10. Helmbold, D., Schapire, R.E., Singer, Y., Warmuth, M.K.: A comparison of new and old algorithms for a mixture estimation problem. Machine Learning 27(1), 97-119 (1997)
11. Kivinen, J., Warmuth, M.K.: Boosting as entropy projection. In: Proc. 12th Annu. Conf. on Comput. Learning Theory, pp. 134-144. ACM Press, New York (1999)
12. Lafferty, J.: Additive models, boosting, and inference for generalized divergences. In: Proceedings of the 12th Annual Conference on Computional Learning Theory, pp. 125-133. ACM Press, New York (1999)
13. Liao, J.: Totally Corrective Boosting Algorithms that Maximize the Margin. PhD thesis, University of California at Santa Cruz (December 2006)
14. Rätsch, G.: Robust Boosting via Convex Optimization: Theory and Applications. PhD thesis, University of Potsdam (2001)
15. Rätsch, G., Onoda, T., Müller, K.-R.: Soft margins for adaboost. Mach. Learn. 42(3), 287-320 (2001)
16. Rätsch, G., Schölkopf, B., Smola, A., Mika, S., Onoda, T., Müller, K.-R.: Robust ensemble learning. In: Smola, A., Bartlett, P., Schölkopf, B., Schuurmans, D. (eds.) Advances in Large Margin Classifiers, pp. 207-219. MIT Press, Cambridge, MA (2000)
17. Ratsch, G., Warmuth, M.: Efficient margin maximizing with boosting. Journal of Machine Learning Research 6, 2131-2152 (2005)
18. Rudin, C., Schapire, R., Daubechies, I.: Boosting based on a smooth margin. In: Proceedings of the 17th Annual Conference on Computational Learning Theory, pp. 502-517 (2004)
19. Rudin, C., Schapire, R., Daubechies, I.: Analysis of boosting algorithms using the smooth margin function. The Annals of Statistics 6(35), 2723-2768 (2007)
20. Schapire, R., Freund, Y., Bartlett, P., Lee, W.: Boosting the margin: A new explanation for the effectiveness of voting methods. The Annals of Statistics 26(5), 1651-1686 (1998)
21. Shalev-Shwartz, S., Singer, Y.: On the equivalence of weak learnability and linear separability: New relaxations and efficient boosting algorithms. In: Proceedings of the 21st annual conference on Computational learning theory, pp. 311-321. Omicron (2008)
22. Smola, A., Vishwanathan, S.V.N., Le, Q.: Bundle methods for machine learning. In: Piatt, J., Koller, D., Singer, Y., Roweis, S. (eds.) Advances in Neural Information Processing Systems 20, pp. 1377-1384. MIT Press, Cambridge (2008)
23. Warmuth, M., Liao, J., Rätsch, G.: Totally corrective boosting algorithms that maximize the margin. In: ICML 2006, pp. 1001-1008. ACM Press, New York (2006)
24. Warmuth, M.K., Glocer, K., Rätsch, G.: Boosting algorithms for maximizing the soft margin. In: Piatt, J., Koller, D., Singer, Y., Roweis, S. (eds.) Advances in Neural Information Processing Systems 20. MIT Press, Cambridge (2007)