For any set of weak-classifiers, this process is guaranteed to converge to a solution that classifies at least as well as the best classifier.
For any set of weak-classifiers, this process is guaranteed to converge to a solution that classifies at least as well as the best classifier. Developed by Yoav Freund and Robert Schapire, this meta-algorithm involves repeatedly reclassifying the training data with the best of the set of original classifiers while reweighting the training data so that the samples classified incorrectly in the previous round are given half the total weight. In the end, each classifier chosen as a top classifier is given a vote based on how well it performed, and the aggregate classifier is applied to the equally-weighted data set. FTP name this machine learning process, thought mistakenly to be named for Lady Lovelace but actually named for its adaptive nature.
For any set of weak-classifiers, this process is guaranteed to converge to a solution that classifies at least as well as the best classifier.
{AdaBoost} [accept {Adaptive Boosting}]