Bagging can stabilize without reducing variance

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

Volumn 2130, Issue , 2001, Pages 49-56

  Grandvalet, Yves ^a  

^a UNIVERSITÉ DE TECHNOLOGIE DE COMPIÈGNE   (France)

Author keywords

[No Author keywords available]

Indexed keywords

NEURAL NETWORKS; PERTURBATION TECHNIQUES;

EXPERIMENTAL EVIDENCE;

STATISTICAL METHODS;

EID: 84958949175     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44668-0_8     Document Type: Conference Paper

References (12)

1. L. Breiman. Bagging predictors. Machine learning, 24(2):123–140, 1996.
2. L. Breiman. Prediction games and arcing algorithms. Technical Report 504, Statistics Department, University of California at Berkeley, 1997.
3. P. Bühlmann and B. Yu. Explaining bagging. Technical Report 92, Seminar für Statistik, ETH, Zürich, 2000.
4. A. N. Burgess. Estimating equivalent kernels for neural networks: A data perturbation approach. In M.C. Mozer, M.I. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems 9, pages 382–388. MIT Press, 1997.
5. T.G. Dietterich. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting and randomization. Machine Learning, 40(2):1–19, 2000.
6. J. H. Friedman. On bias, variance, 0/1 loss, and the curse of dimensionality. Data Mining and Knowledge Discovery, 1(1):55–77, 1997.
7. J. H. Friedman and P. Hall. On bagging and non-linear estimation. Technical report, Stanford University, Stanford, CA., January 2000.
8. Y. Grandvalet. Bagging down-weights leverage points. In S.-I. Amari, C. Lee Giles, M. Gori, and V. Piuri, editors, IJCNN, volume 4, pages 505–510. IEEE, 2000.
9. T. J. Hastie and R. J. Tibshirani. Generalized Additive Models, volume 43 of Monographs on Statistics and Applied Probability. Chapman & Hall, 1990.
10. R. Maclin and D. Opitz. An empirical evaluation of bagging and boosting. In Proceedings of the Fourteenth National Conference on Artificial Intelligence, pages 546–551. AAAI Press, 1997.
11. R. 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. J. Tibshirani and K. Knight. The covariance inflation criterion for adaptive model selection. Journal of the Royal Statistical Society, B, 61(3):529–546, 1999.