Boosting a weak learning algorithm by majority
Information and Computation
Machine Learning
Game theory, on-line prediction and boosting
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Boosting in the limit: maximizing the margin of learned ensembles
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Boosting regression estimators
Neural Computation
Computational Statistics & Data Analysis - Nonlinear methods and data mining
The Consistency of Greedy Algorithms for Classification
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
A Consistent Strategy for Boosting Algorithms
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
On the rate of convergence of regularized boosting classifiers
The Journal of Machine Learning Research
Minimax nonparametric classification .I. Rates of convergence
IEEE Transactions on Information Theory
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This letter is a comprehensive account of some recent findings about AdaBoost in the presence of noisy data when approached from the perspective of statistical theory. We start from the basic assumption of weak hypotheses used in AdaBoost and study its validity and implications on generalization error. We recommend studying the generalization error and comparing it to the optimal Bayes error when data are noisy. Analytic examples are provided to show that running the unmodified AdaBoost forever will lead to overfit. On the other hand, there exist regularized versions of AdaBoost that are consistent, in the sense that the resulting prediction will approximately attain the optimal performance in the limit of large training samples.