Ensemble Machine Learning pp 35-85
Boosting Algorithms: A Review of Methods, Theory, and Applications
Boosting is a class of machine learning methods based on the idea that a combination of simple classifiers (obtained by a weak learner) can perform better than any of the simple classifiers alone. A weak learner (WL) is a learning algorithm capable of producing classifiers with probability of error strictly (but only slightly) less than that of random guessing (0.5, in the binary case). On the other hand, a strong learner (SL) is able (given enough training data) to yield classifiers with arbitrarily small error probability.