Approximation stability and boosting

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

Volumn 6331 LNAI, Issue , 2010, Pages 59-73

  Gao, Wei ^a   Zhou, Zhi Hua ^a  

^a NANJING UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ADABOOST; BASE LEARNERS; CLASSIFICATION ALGORITHM; EXPONENTIAL BOUNDS; GENERAL LEARNING; LEARNABILITY; STABILITY ANALYSIS; UNIFORM STABILITY; UNSOLVED PROBLEMS;

ALGORITHMS; LEARNING SYSTEMS; STABILITY;

ADAPTIVE BOOSTING;

EID: 78249262911     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-16108-7_9     Document Type: Conference Paper

References (25)

1. Alon, N., Ben-David, S., Cesa-Bianchi, N., Haussler, D.: Scale-sensitive dimensions, uniform convergence, and learnablity. J. ACM 44(4), 615-631 (1997)
2. Bickel, J.P., Ritov, Y., Zakai, A.: Some theory for generalized boosting algorithms. J. Mach. Learn. Res. 7, 705-732 (2006)
3. Bousquet, O., Elisseeff, A.: Stability and generalization. J. Mach. Learn. Res. 2, 499-526 (2002)
4. Breiman, L.: Prediction games and arcing classifiers. Neural Comput. 11(7), 1493-1517 (1999)
5. Devroye, L.P., Wagner, T.J.: Distribution-free performance bounds for potential function rules. IEEE Trans. Inform. Theory 25, 601-604 (1979)
6. Freund, Y., Schapire, R.E.: Game theory, on-line prediction and boosting. In: Proc. of 9th COLT, Desenzano sul Garda, Italy, pp. 325-332 (1996)
7. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119-139 (1997)
8. Freund, Y., Schapire, R.E.: Response to "Evidence contrary to the statistical view of Boosting". J. Mach. Learn. Res. 9, 171-174 (2008)
9. Friedman, J., Hastie, T., Tibshirani, R.: Addtive logistic regression: A statistical view of boosting (with discussion). Ann. Statist. 28, 337-407 (2000) (Pubitemid 33227445)
10. Kearns, M., Ron, D.: Algorithmic stability and sanity-check bounds for leave-one-out cross-validation. Neural Comput. 11, 1427-1453 (1999)
11. Kutin, S., Niyogi, P.: The interaction of stability and weakness in Adaboost. Technical Report 30, Department of Computer Science, University of Chicago, Chicago, IL (2001)
12. Kutin, S., Niyogi, P.: Almost-everywhere algorithmic stability and generalization error. In: Proc. of 18th UAI, Edmonton, Canada, pp. 275-282 (2002)
13. Mason, L., Baxter, J., Bartlett, P.L., Freman, M.R.: Boosting algorithms as gradient descent. In: Solla, S.A., Leen, T.K., Müller, K.-R. (eds.) Advances in NIPS, vol. 12, pp. 512-518. MIT Press, Cambridge (1999)
14. McDiarmid, C.: On the method of bounded differences. In: Surveys in Combinatorics, pp. 148-188. Cambridge University Press, Cambridge (1989)
15. Mease, D., Wyner, A.: Evidence contrary to the statistical view of boosting with discussion. J. Mach. Learn. Res. 9, 131-201 (2008)
16. Mukherjee, S., Niyogi, P., Poggio, T., Rifkin, R.: Learning theory: Stability is sufficient for generalization and necessary and sufficient for consistency of empirical risk minimzation. Adv. Comput. Math. 25, 161-193 (2006)
17. Rakhlin, A., Mukherjee, S., Poggio, T.: Stability results in learning theory. Anal. Appl. 4, 397-417 (2005)
18. Rätsch, G., Onoda, T., Müller, K.R.: Soft margins for Adaboost. Mach. Learn. 42, 287-320 (2001)
19. Reyzin, L., Schapire, R.E.: How boosting the margin can also boost classifier complexity. In: Proc. of 23rd ICML, Pittsburgh, PA, pp. 753-760 (2006)
20. Rudin, C., Schapire, R.E., Daubechies, I.: Precise statements of convergence for adaboost and arc-gv. In: Proc. of AMS-IMS-SIAM Joint Summer Research Conference: Machine learning, Statistics and Discovery, Snowbird, Utah, pp. 131-145 (2007)
21. Schapire, R., Freund, Y., Bartlett, P.L., Lee, W.: Boosting the margin: A new explanation for the effectives of voting methods. Ann. Statist. 26, 1651-1686 (1998)
22. Shalev-Shwartz, S., Shamir, O., Srebro, N., Sridharan, K.: Learnability and stability in the general learning setting. In: Proc. of 22nd COLT, Montreal, Canada (2009)
23. Shalev-Shwartz, S., Shamir, O., Srebro, N., Sridharan, K.: Stochastic convex optimation. In: Proc. of 22nd COLT, Montreal, Canada (2009)
24. Vapnik, V.N.: Statistical Learning Theory. John Wiley & Sons, New York (1998)
25. Wang, L., Sugiyama, M., Yang, C., Zhou, Z.-H., Feng, J.: On the margin explanation of boosting algorithm. In: Proc. of 21st COLT, Helsinki, Finland, pp. 479-490 (2008)