Citation Relationships



Blanchard G (2004) Different Paradigms for Choosing Sequential Reweighting Algorithms Neural Comput 16:811-836

References and models cited by this paper

References and models that cite this paper

Amit Y, Blanchard G (2001) Multiple randomized classifiers: MRCL Tech Rep (Available on-line: http:--galton.uchicago.edu-~amit-Papers-)

Amit Y, Geman D (1997) Shape quantization and recognition with randomized trees Neural Comput 9:1545-1588

Blackwell D (1956) An analog of the minmax theorem for vector payoffs Pacific Journal Of Mathematics 6:1-8

Blanchard G (2001) Mixture and aggregation of estimators for pattern recognition: Application to decision trees Unpublished doctoral dissertation (Available on-line: http:--www.math.u-psud.fr-~blanchard-publi-these.ps.gz)

Blanchard G (2003) Generalization error bounds for aggregate classifiers Nonlinear estimation and classification, Denison DD:Hansen MH:Holmes CC:Mallick B:Yu B, ed.

Breiman L (1998) Prediction games and arcing algorithms Tech Rep (Available on-line: ftp:--ftp.stat.berkeley.edu-pub-users-breiman-games.ps.Z)

Breiman L (1998) Arcing classifiers (with discussion) Ann Stat 26:801-849

Breiman L (2001) Random forests Mach Learn 45:5-32

Devroye L, Gyorfi L, Lugosi G (1996) A probabilistic theory of pattern recognition

Dietterich TG (2000) An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting and randomization Mach Learn 40:139-158

Frean M, Downs T (1998) A simple cost function for boosting Tech Rep (Available on-line: http:--www.boosting.org-papers-FreDow98.ps.gz)

Freund Y, Schapire R (1996) Experiment with a new boosting algorithm Proc. of the 13th International Conference on Machine Learning :148-156

Freund Y, Schapire RE (1996) Game theory, on-line prediction and boosting Proceedings of the Ninth Annual Conference on Computational Learning Theory :325-332

Friedman J, Hastie T, Tibshirani R (2000) Additive logistic regression: A statictical view of boosting Ann Stat 28:337-374

Grove AJ, Schuurmans D (1998) Boosting in the limit: Maximizing the margin of learned ensembles Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98)

Hart S, Mas-colell A (2001) A general class of adaptive strategies J Economic Theory 98:26-54

Koltchinkskii V, Panchenko D (2002) Empirical margin distributions and bounding the generalization error of combined classifiers Ann Stat 30:1-35

Meir R, Ratsch G (2003) An introduction to Boosting and leveraging Advanced lectures on machine learning (Available on-line: http:--www.boosting.org-papers-MeiRae03.ps.gz), Mendelson S:Smola A, ed. pp.119

Mika S (2002) Kernel fisher discriminants Unpublished doctoral dissertation

Onoda T, Ratsch G, Muller KR (1998) An asymptotic analysis of AdaBoost in the binary classification case Proc. of the Int. Conf. on Artificial Neural Networks (ICANN'98), Niklasson L:Boden M:Ziemke T, ed. pp.1956

Ratsch G, Mika S, Scholkopf B, Muller KR (2002) Constructing Boosting algorithms from SVMs: An application to one-class classification IEEE PAMI 24:1184-1199

Ratsch G, Onoda T, Muller KR (2001) Soft margins for AdaBoost Mach Learn 42:287-320

Ratsch G, Warmuth M (2001) Marginal boosting Tech Rep

Schapire RE, Freund Y, Bartlett P, Lee WS (1998) Boosting the margin: A new explanation for the effectiveness of voting methods Ann Stat 26:1651-1686

Smola AJ, Bartlett PL, Scholkopf B, Schuurmans D (2000) Advances in large margin classifiers, Smola AJ:Bartlett PL:Scholkopf B:Schuurmans D, ed.

Viola P, Jones MJ (2001) Robust real-time object detection Tech. Rep. CRL2001-01

(26 refs)