The Strength of Weak Learnability
Machine Learning
Boosting a weak learning algorithm by majority
Information and Computation
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
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Proceedings of the First International Workshop on Multiple Classifier Systems
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Proceedings of the Third International Workshop on Multiple Classifier Systems
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Using output codes to boost multiclass learning problems
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
Engineering multiversion neural-net systems
Neural Computation
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
A new HMM-based ensemble generation method for numeral recognition
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
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Methods that create several classifiers out of one base classifier, so-called ensemble creation methods, have been proposed and successfully applied to many classification problems recently. One category of such methods is Boosting with AdaBoost being the best known procedure belonging to this category. Boosting algorithms were first developed for two-class problems, but then extended to deal with multiple classes. Yet these extensions are not always suitable for problems with a large number of classes. In this paper we introduce some novel boosting algorithms which are designed for such problems, and we test their performance in a handwritten word recognition task.