Generalization bounds for voting classifiers based on sparsity and clustering

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 2777, Issue , 2003, Pages 492-505

  Koltchinskii, Vladimir ^a   Panchenko, Dmitry ^a   Andonova, Savina ^a  

^a NONE

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; DECISION MAKING; ERRORS; FUNCTIONS; PROBABILITY; COMPUTERS;

CLASSIFICATION ALGORITHMS; COMPLEXITY OF FUNCTIONS; CONVEX HULL; DETERMINISTIC FUNCTIONS; CONVEX COMBINATIONS; GENERALIZATION BOUND; GENERALIZATION ERROR; VOTING CLASSIFIERS; WEAK CLASSIFIERS;

SET THEORY; ARTIFICIAL INTELLIGENCE;

EID: 9444260333     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-45167-9_36     Document Type: Conference Paper

References (15)

1. Breiman, L. (1996) Bagging predictors. Machine Learning, 24, 2, 123-140.
2. Breiman, L. (1999) Prediction games and arcing algorithms. Neural Computation, 11, 7, 1493-1517.
3. Dudley, R.M. (1999) Uniform Central Limit Theorems. Cambridge University Press.
4. Friedman, J., Hastie, T. and Tibshirani, R. (2000) Additive logistic regression: a statistical view of boosting. Ann. Statist., 28, 337-374.
5. Haussler, D., (1995) Sphere packing numbers for subsets of the boolean n-cube with bounded Vapnik-Chervonenkis dimension, J. Combin. Theory Ser. A, 69, 217-232.
6. Antos, A., Kégl, B., Linder, T. and Lugosi, G., (2002), Data-dependent margin-based generalization bounds for classification, Journal of Machine Learning Research.
7. Koltchinskii, V. (2002) Bounds on margin distributions in learning problems, Annales de l'Institut Henri Poincaré, to appear.
8. Koltchinskii, V. and Panchenko, D. (2002) Empirical margin distributions and bounding the generalization error of combined classifiers. Ann. Statist, 30, 1.
9. Koltchinskii, V., Panchenko, D. and Lozano, F. (2003) Bounding the generalization error of convex combinations of classifiers: balancing the dimensionality and the margins, Ann. Appl. Probab., 13, 1.
10. Panchenko, D. (2002) Some extensions of an inequality of Vapnik and Chervonenkis, Elect. Comm. in Probab. 7, 55-65.
11. Panchenko, D. (2002) On the generalization ability of voting classifiers, submitted.
12. Schapire, R., Freund, Y., Bartlett, P. and Lee, W. S. (1998) Boosting the margin: A new explanation of effectiveness of voting methods. Ann. Statist., 26, 1651-1687.
13. van der Vaart, A.W. and Wellner, J.A. (1996) Weak convergence and Empirical Processes. With Applications to Statistics. Springer-Verlag, New York.
14. Vapnik, V.N. (1968) Chervonenkis, A.Ya., On the uniform convergence of relative frequencies of event to their probabilities, Soviet Math. Dokl.,9, 915-918.
15. Vapnik, V. (1998) Statistical Learning Theory. John Wiley & Sons, New York.