Communications of the ACM
Crytographic limitations on learning Boolean formulae and finite automata
STOC '89 Proceedings of the twenty-first annual ACM symposium on Theory of computing
The Strength of Weak Learnability
Machine Learning
Boosting a weak learning algorithm by majority
Information and Computation
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
Using output codes to boost multiclass learning problems
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
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Boosting is a generic statistical process for generating accurate classifier ensembles from moderately accurate learning algorithm. This paper presents a new generic boosting style procedure, M-Boost , for learning multiclass concepts. M-Boost uses a global strategy for selecting the weak classifier, a global weight reassignment strategy, a vector valued weight for the selected classifiers, and an ensemble that outputs a probability distribution on classes.