Information Processing Letters
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
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
Prediction games and arcing algorithms
Neural Computation
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Maximizing the Margin with Boosting
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
An introduction to boosting and leveraging
Advanced lectures on machine learning
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Mutually beneficial learning with application to on-line news classification
Proceedings of the ACM first Ph.D. workshop in CIKM
Boosting recombined weak classifiers
Pattern Recognition Letters
iBoost: Boosting using an instance-based exponential weighting scheme
International Journal of Hybrid Intelligent Systems
Avoiding Boosting Overfitting by Removing Confusing Samples
ECML '07 Proceedings of the 18th European conference on Machine Learning
A Kernel Method for the Optimization of the Margin Distribution
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
A New Variant of the Optimum-Path Forest Classifier
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Compositional noisy-logical learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A Learning Algorithm for the Optimum-Path Forest Classifier
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
ODDboost: Incorporating Posterior Estimates into AdaBoost
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
A discrete approach for supervised pattern recognition
IWCIA'08 Proceedings of the 12th international conference on Combinatorial image analysis
Boosting through optimization of margin distributions
IEEE Transactions on Neural Networks
Approximation stability and boosting
ALT'10 Proceedings of the 21st international conference on Algorithmic learning theory
Engineering Applications of Artificial Intelligence
A Refined Margin Analysis for Boosting Algorithms via Equilibrium Margin
The Journal of Machine Learning Research
Efficient supervised optimum-path forest classification for large datasets
Pattern Recognition
Margin distribution based bagging pruning
Neurocomputing
Fast automatic microstructural segmentation of ferrous alloy samples using optimum-path forest
CompIMAGE'10 Proceedings of the Second international conference on Computational Modeling of Objects Represented in Images
Margin optimization based pruning for random forest
Neurocomputing
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Multi-Task boosting by exploiting task relationships
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Defect cluster recognition system for fabricated semiconductor wafers
Engineering Applications of Artificial Intelligence
On the doubt about margin explanation of boosting
Artificial Intelligence
Fully corrective boosting with arbitrary loss and regularization
Neural Networks
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Boosting methods are known not to usually overfit training data even as the size of the generated classifiers becomes large. Schapire et al. attempted to explain this phenomenon in terms of the margins the classifier achieves on training examples. Later, however, Breiman cast serious doubt on this explanation by introducing a boosting algorithm, arc-gv, that can generate a higher margins distribution than AdaBoost and yet performs worse. In this paper, we take a close look at Breiman's compelling but puzzling results. Although we can reproduce his main finding, we find that the poorer performance of arc-gv can be explained by the increased complexity of the base classifiers it uses, an explanation supported by our experiments and entirely consistent with the margins theory. Thus, we find maximizing the margins is desirable, but not necessarily at the expense of other factors, especially base-classifier complexity.