Boosting is a meta-learning approach that aims at combining an ensemble of weak classifiers to form a strong classifier. Adaptive Boosting (Adaboost) implements this idea as a greedy search for a linear combination of classifiers by overweighting the

AdaBoost, Adaptive Boosting, is a well-known meta machine learning algorithm that was proposed by Yoav Freund and Robert Schapire. In this project there two main files ,AdaBoost, Adaptive Boosting, is a well-known meta machine learning algorithm t

AdaBoost, Adaptive Boosting, is a well-known meta machine learning algorithm that was proposed by Yoav Freund and Robert Schapire. In this project there two main files 1. ADABOOST_tr.m 2. ADABOOST_te.m to traing and test a user-coded learnin

AdaBoost, short for Adaptive Boosting, is a machine learning algorithm, formulated by Yoav Freund and Robert Schapire. It is a meta-algorithm, and can be used in conjunction with many other learning algorithms to improve their performance.

Yoav Freund and Robert E. Schapire于1996提出的adaboost经典算法-a paper about adaboost purposed by Yoav Freund and Robert E. Schapire in1996.