The Strength of Weak Learnability
Machine Learning
The weighted majority algorithm
Information and Computation
Machine Learning
Boosting classifiers regionally
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Machine Learning
iBoost: Boosting Using an i nstance-Based Exponential Weighting Scheme
ECML '02 Proceedings of the 13th European Conference on Machine Learning
MadaBoost: A Modification of AdaBoost
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
An empirical comparison of three boosting algorithms on real data sets with artificial class noise
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Identifying and eliminating mislabeled training instances
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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The error reduction in generalization is one of the principal motivations of research in machine learning. Thus, a great number of work is carried out on the classifiers aggregation methods in order to improve generally, by voting techniques, the performance of a single classifier. Among these methods of aggregation, we find the Boosting which is most practical thanks to the adaptive update of the distribution of the examples aiming at increasing in an exponential way the weight of the badly classified examples. However, this method is blamed because of overfitting, and the convergence speed especially with noise. In this study, we propose a new approach and modifications carried out on the algorithm of AdaBoost. We will demonstrate that it is possible to improve the performance of the Boosting, by exploiting assumptions generated with the former iterations to correct the weights of the examples. An experimental study shows the interest of this new approach, called hybrid approach.