AdaBoost is consistent

The risk, or probability of error, of the classifier produced by the AdaBoost algorithm is investigated. In particular, we consider the stopping strategy to be used in AdaBoost to achieve universal consistency. We show that provided AdaBoost is stopped after n1-ε iterations---for sample size n and ε ∈ (0,1)---the sequence of risks of the classifiers it produces approaches the Bayes risk

A low variance error boosting algorithm

This paper introduces a robust variant of AdaBoost, cw-AdaBoost, that uses weight perturbation to reduce variance error, and is particularly effective when dealing with data sets, such as microarray data, which have large numbers of features and small number of instances. The algorithm is compared with AdaBoost, Arcing and MultiBoost, using twelve gene expression datasets, using 10-fold cross validation. The new algorithm consistently achieves higher classification accuracy over all these datasets. In contrast to other AdaBoost variants, the algorithm is not susceptible to problems when a zero-error base classifier is encountered