Machine Learning
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
Machine Learning
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Linear Programming Boosting via Column Generation
Machine Learning
Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces
Machine Learning
MadaBoost: A Modification of AdaBoost
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Boosting in the presence of noise
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
Smooth boosting and learning with malicious noise
The Journal of Machine Learning Research
Information geometry of U-Boost and Bregman divergence
Neural Computation
Robust boosting and its relation to bagging
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Robustifying AdaBoost by Adding the Naive Error Rate
Neural Computation
Edited AdaBoost by weighted kNN
Neurocomputing
Improving Logitboost with prior knowledge
Information Fusion
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Boosting is known as a gradient descent algorithm over loss functions. It is often pointed out that the typical boosting algorithm, Adaboost, is highly affected by outliers. In this letter, loss functions for robust boosting are studied. Based on the concept of robust statistics, we propose a transformation of loss functions that makes boosting algorithms robust against extreme outliers. Next, the truncation of loss functions is applied to contamination models that describe the occurrence of mislabels near decision boundaries. Numerical experiments illustrate that the proposed loss functions derived from the contamination models are useful for handling highly noisy data in comparison with other loss functions.