Theoretical Views of Boosting and Applications [chapter]

Robert E. Schapire
1999 Lecture Notes in Computer Science  
Boosting is a general method for improving the accuracy of any given learning algorithm. Focusing primarily on the AdaBoost algorithm, we brie y survey theoretical work on boosting including analyses of AdaBoost's training error and generalization error, connections between boosting and game theory, methods of estimating probabilities using boosting, and extensions of AdaBoost for multiclass classi cation problems. Some empirical work and applications are also described.
doi:10.1007/3-540-46769-6_2 fatcat:jqgoqtfgovcdtm4lxzj36qf33u