Communications of the ACM
The design and analysis of efficient learning algorithms
The design and analysis of efficient learning algorithms
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
Improved boosting algorithms using confidence-rated predictions
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Boosting in the limit: maximizing the margin of learned ensembles
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Boosting as entropy projection
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Prediction games and arcing algorithms
Neural Computation
Machine Learning
Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces
Machine Learning
ALT '96 Proceedings of the 7th International Workshop on Algorithmic Learning Theory
A Column Generation Algorithm For Boosting
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
An introduction to boosting and leveraging
Advanced lectures on machine learning
Online learning of linear classifiers
Advanced lectures on machine learning
How boosting the margin can also boost classifier complexity
ICML '06 Proceedings of the 23rd international conference on Machine learning
Efficient Margin Maximizing with Boosting
The Journal of Machine Learning Research
Observations on boosting feature selection
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Learning the bias of a classifier in a GA-Based inductive learning environment
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
A primal-dual convergence analysis of boosting
The Journal of Machine Learning Research
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AdaBoost produces a linear combination of weak hypotheses. It has been observed that the generalization error of the algorithm continues to improve even after all examples are classified correctly by the current linear combination, i.e. by a hyperplane in feature space spanned by the weak hypotheses. The improvement is attributed to the experimental observation that the distances (margins) of the examples to the separating hyperplane are increasing even when the training error is already zero, that is all examples are on the correct side of the hyperplane. We give an iterative version of AdaBoost that explicitly maximizes the minimum margin of the examples. We bound the number of iterations and the number of hypotheses used in the final linear combination which approximates the maximum margin hyperplane with a certain precision. Our modified algorithm essentially retains the exponential convergence properties of AdaBoost and our result does not depend on the size of the hypothesis class.