Algorithmics: theory & practice
Algorithmics: theory & practice
The Strength of Weak Learnability
Machine Learning
Machine Learning
Game theory, on-line prediction and boosting
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Hi-index | 0.00 |
A new view of majority voting as a Monte Carlo stochastic algorithm is presented in this paper. Relation between the two approaches allows Adaboost's example weighting strategy to be compared with the greedy covering strategy used for a long time in Machine Learning. The greedy covering 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 to that exhibited by AdaBoost.