Communications of the ACM
Information Processing Letters
The Strength of Weak Learnability
Machine Learning
C4.5: programs for machine learning
C4.5: programs for 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
On the boosting ability of top-down decision tree learning algorithms
Journal of Computer and System Sciences
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
DS '98 Proceedings of the First International Conference on Discovery Science
MadaBoost: A Modification of AdaBoost
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
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We improve the analysis of the decision tree boosting algorithm proposed by Mansour and McAllester. For binary classification problems, the algorithm of Mansour and McAllester constructs a multiway branching decision tree using a set of multi-class hypotheses. Mansour and McAllester proved that it works under certain conditions. We give a much simpler analysis of the algorithm and simplify the conditions. From this simplification, we can provide a simpler algorithm, for which no prior knowledge on the quality of weak hypotheses is necessary.