Written by the developers of the first practical boosting algorithm, AdaBoost, this reference covers the background, theory, and advances in the formula. The first part of the book provides a general background of the subject. It is followed by an outline of the theory of boosting and the extensions to the AdaBoost algorithm that have been made since its inception. Chapters cover the mathematical study of machine learning, analysis of AdaBoost’s training error, the generalization error, and game theory. The authors also discuss specific applications, such as bioinformatics and computer vision, and provide examples to explain topics and ensure understanding.