Algorithmics: theory & practice
Algorithmics: theory & practice
The Strength of Weak Learnability
Machine Learning
Machine Learning
A framework for structural risk minimisation
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
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
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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A new view of majority voting as a Monte Carlo stochastic algorithm is presented in this paper. The relation between the two approches allows Adaboost?s example weighting strategy to be compared with the greedy covering strategy used for a long time in Machine Learning. Even though one may expect that the greedy strategy is very much prone to overfitting, extensive experimental results do not support this guess. The greedy strategy does not clearly show overfitting, it runs in at least one order of magnitude less time, it reaches zero error on the training set in few trials, and the error on the test set is most of the time comparable, if not lower, than that exhibited by Adaboost.