The Strength of Weak Learnability
Machine Learning
Boosting a weak learning algorithm by majority
Information and Computation
Game theory, on-line prediction and boosting
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
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
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Machine Learning
An Adaptive Version of the Boost by Majority Algorithm
Machine Learning
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
Multiclass Boosting for Weak Classifiers
The Journal of Machine Learning Research
Efficient Margin Maximizing with Boosting
The Journal of Machine Learning Research
The Journal of Machine Learning Research
On the Consistency of Multiclass Classification Methods
The Journal of Machine Learning Research
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Random classification noise defeats all convex potential boosters
Machine Learning
Learning with continuous experts using drifting games
Theoretical Computer Science
ALT'09 Proceedings of the 20th international conference on Algorithmic learning theory
Boosting: Foundations and Algorithms
Boosting: Foundations and Algorithms
Boosting for multiclass semi-supervised learning
Pattern Recognition Letters
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Boosting combines weak classifiers to form highly accurate predictors. Although the case of binary classification is well understood, in the multiclass setting, the "correct" requirements on the weak classifier, or the notion of the most efficient boosting algorithms are missing. In this paper, we create a broad and general framework, within which we make precise and identify the optimal requirements on the weak-classifier, as well as design the most effective, in a certain sense, boosting algorithms that assume such requirements.