Machine Learning
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
Machine Learning
Logistic Regression, AdaBoost and Bregman Distances
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Boosted Classification Trees and Class Probability/Quantile Estimation
The Journal of Machine Learning Research
Boosting with incomplete information
Proceedings of the 25th international conference on Machine learning
Ensembles of Abstaining Classifiers Based on Rule Sets
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Topology modeling for Adaboost-cascade based object detection
Pattern Recognition Letters
Sparse approximation through boosting for learning large scale kernel machines
IEEE Transactions on Neural Networks
Approximation stability and boosting
ALT'10 Proceedings of the 21st international conference on Algorithmic learning theory
Stochastic boosting algorithms
Statistics and Computing
Temporal multi-hierarchy smoothing for estimating rates of rare events
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
A Refined Margin Analysis for Boosting Algorithms via Equilibrium Margin
The Journal of Machine Learning Research
A robust ranking methodology based on diverse calibration of AdaBoost
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Shedding light on the asymmetric learning capability of AdaBoost
Pattern Recognition Letters
Pattern Recognition Letters
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
Double-base asymmetric AdaBoost
Neurocomputing
On the doubt about margin explanation of boosting
Artificial Intelligence
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The statistical perspective on boosting algorithms focuses on optimization, drawing parallels with maximum likelihood estimation for logistic regression. In this paper we present empirical evidence that raises questions about this view. Although the statistical perspective provides a theoretical framework within which it is possible to derive theorems and create new algorithms in general contexts, we show that there remain many unanswered important questions. Furthermore, we provide examples that reveal crucial flaws in the many practical suggestions and new methods that are derived from the statistical view. We perform carefully designed experiments using simple simulation models to illustrate some of these flaws and their practical consequences.