Boosting in the Limit: Maximizing the Margin of Learned Ensembles

The “minimum margin” of an ensemble classifier on a given training set is, roughly speaking, the smallest vote it give s to any correct training label. Recent work has shown that the Adaboost algorithm is particularly effective at producing ensembles with large minimum margins, and theory suggests that this may account for its success at reducing generaliza… CONTINUE READING