The Strength of Weak Learnability
Machine Learning
Randomized algorithms
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 Generalization Through Explicit Optimization of Margins
Machine Learning
Machine Learning
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Boosting in the presence of noise
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
On boosting with polynomially bounded distributions
The Journal of Machine Learning Research
Optimally-smooth adaptive boosting and application to agnostic learning
The Journal of Machine Learning Research
Smooth boosting and learning with malicious noise
The Journal of Machine Learning Research
An empirical evaluation of bagging and boosting
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Real-time ranking with concept drift using expert advice
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Parameterizing random test data according to equivalence classes
Proceedings of the 2nd international workshop on Random testing: co-located with the 22nd IEEE/ACM International Conference on Automated Software Engineering (ASE 2007)
Random classification noise defeats all convex potential boosters
Proceedings of the 25th international conference on Machine learning
Predicting electricity distribution feeder failures using machine learning susceptibility analysis
IAAI'06 Proceedings of the 18th conference on Innovative applications of artificial intelligence - Volume 2
Proceedings of the First International Workshop on Data Mining for Service and Maintenance
Algorithms and hardness results for parallel large margin learning
The Journal of Machine Learning Research
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Martingale boosting is a simple and easily understood technique with a simple and easily understood analysis. A slight variant of the approach provably achieves optimal accuracy in the presence of random misclassification noise.