Geometric bounds for generalization in boosting

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

Volumn 2111, Issue , 2001, Pages 461-472

  Mannor, Shie ^a   Meir, Ron ^a  

^a TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); ERRORS; GEOMETRY;

BOOSTED CLASSIFIERS; BOOSTING ALGORITHM; EMPIRICAL ERRORS; GENERALIZATION ERROR; GEOMETRIC CONCEPTS; GEOMETRIC CONDITIONS; LINEAR CLASSIFIERS; MINIMAL VALUE;

COMPUTATION THEORY;

EID: 84943238425     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44581-1_30     Document Type: Conference Paper

References (13)

1. R. Alexander. Geometric methods in the study of irregularities of distribution. Combinatorica, 10(2):115–136, 1990.
2. R. Alexander.Principles of a new method in the study of irregularities of distribution. Invent. Math., 103:279–296, 1991.
3. L. Devroye and Gyorfi. Nonparametric Density Estimation: The L1 View. John Wiley, New York, 1985.
4. Y. Freund. Boosting a weak learning algorithm by majority. Information and Computation, 121:256–285, 1995.
5. Y.F reund and R.E. Sc hapire. Experiments with a new boosting algorithm. In Proceeding of the Thirteenth International Conference on Machine Learning, pages 148–156, 1996.
6. Y. Freund and R.E. Schapire. Game theory, on-line prediction and boosting. In Proceedings of the Ninth Annual Conference on Computational Learning Theory, 1996, pages 325–332, 1996.
7. J. Friedman, T. Hastie, and R. Tibshirani. Additive logistic regression: a statistical view of boosting. The Annals of Statistics, 38(2):337–374, 2000.
8. M. Goldman, L. Hastad, and A. Razborov. Majority gates vs.general weighted threshold gates. Jour. of Comput. Complexity, 1(4):277–300, 1992.
9. V. Koltchinskii, D. Panchenko, and F. Lozano. Some new bounds on the generlization error of combined classifiers. In T. Dietterich, editor, Advances in Neural Information Processing Systems 14, Boston, 2001.MIT Press.
10. S. Mannor and R. Meir. On the existence of weak learners and applications to boosting. Machine Learning, 2001.T o appear.
11. R.E. Schapire, Y. Freund, P. Bartlett, and W.S. Lee. Boosting the margin: a new explanation for the effectiveness of voting methods. The Annals of Statistics, 26(5):1651–1686, 1998.
12. R.E. Schapire and Y. Singer. Improved boosting algorithms using confidence-rated predictions. Machine Learning, 37(3):297–336, 1999.
13. J. Shaw-Taylor, P.L. Bartlett, R.C. Williamson, and M Anthony. Structural risk minimization over data-dependent hierarchies. IEEE Trans. Inf. Theory, 44(5):1926–1940, September 1998.