The boosting approach to machine learning: An overview

@misc{citeulike:411634, abstract = {Boosting is a general method for improving the accuracy of any given learning algorithm. Focusing primarily on the AdaBoost algorithm, this chapter overviews some of the recent work on boosting including analyses of AdaBoost's training error and generalization error; boosting's connection to game theory and linear programming; the relationship between boosting and logistic regression; extensions of AdaBoost for multiclass classification problems; methods of incorporating human knowledge...}, author = {Schapire, R. }, citeulike-article-id = {411634}, keywords = {algorithms, game, kdd, stochastic}, posted-at = {2008-05-09 20:56:28}, priority = {2}, title = {The boosting approach to machine learning: An overview}, url = {http://citeseer.ist.psu.edu/schapire02boosting.html}, year = {2001} }

TY - GEN ID - citeulike:411634 L3 - citeulike-article-id:411634 TI - The boosting approach to machine learning: An overview N2 - Boosting is a general method for improving the accuracy of any given learning algorithm. Focusing primarily on the AdaBoost algorithm, this chapter overviews some of the recent work on boosting including analyses of AdaBoost's training error and generalization error; boosting's connection to game theory and linear programming; the relationship between boosting and logistic regression; extensions of AdaBoost for multiclass classification problems; methods of incorporating human knowledge... KW - algorithms KW - game KW - kdd KW - stochastic AU - Schapire, R PY - 2001/// UR - http://citeseer.ist.psu.edu/schapire02boosting.html ER -