The Strength of Weak Learnability
Machine Learning
Boosting classifiers regionally
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Boosted mixture of experts: an ensemble learning scheme
Neural Computation
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Incorporating Prior Knowledge into Boosting
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Boosting the margin: A new explanation for the effectiveness of voting methods
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
An introduction to boosting and leveraging
Advanced lectures on machine learning
Ensemble selection from libraries of models
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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We present MBoost, a novel extension to AdaBoost that extends boosting to use multiple weak learners explicitly, and provides robustness to learning models that overfit or are poorly matched to data. We demonstrate MBoost on a variety of problems and compare it to cross validation for model selection.