for what we are going to do. Roughly, the idea of boosting is to take a weak learning algorithm—any learning algorithm that gives a classifier that is slightly bet-ter than …

This short overview paper introduces the boosting algorithm AdaBoost, and explains the un-derlying theory of boosting, including an explanation of why boosting often does not suffer …

The boosting theorem says that if weak learning hypothesis is satis ed by some weak learning algorithm, then Adaboost algorithm will ensemble the weak hypothesis and produce a classi er …

Boosting can use any classifier as its weak learner (base classifier) but decision trees are by far the most popular. Boosting learns slowly, first using the samples that are easiest to predict, …

Boosting: Different Perspectives Boosting is a maximum-margin method (Schapire et al. 1998, Rosset et al. 2004) Trades lower margin on easy cases for higher margin on harder cases …

Boosting started with a question of Michael Kearns, about whether a “weak learning algorithm” can be made into a “strong learning algorithm.” Suppose a learning algorithm is only …

General boosting algorithm that works with a variety of different loss functions. Models include regression, outlier-resistant regression, K-class classification and risk modeling.