Machine Learning
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
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
The strength of weak learnability
SFCS '89 Proceedings of the 30th Annual Symposium on Foundations of Computer Science
Fast classification with neural networks via confidence rating
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
A Dynamically Adjusted Mixed Emphasis Method for Building Boosting Ensembles
IEEE Transactions on Neural Networks
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Multi-Net systems in general, and the Real Adaboost algorithm in particular, offer a very interesting way of designing very powerful classifiers. However, one inconvenient of this schemes is the large computational burden required for their construction. In this paper, we propose a new Boosting scheme which incorporates subsampling mechanisms to speed up the training of base learners and, therefore, the setup of the ensemble network. Furthermore, subsampling the training data provides additional diversity among the constituent learners, according to the some principles exploited by Bagging approaches. Experimental results show that our method is in fact able to improve both Boosting and Bagging schemes in terms of recognition rates, while allowing significant training time reductions.