Schapire 所著的Boosting算法的教程。 对目前物体识别和机器学习领域中广泛应用的Boosting算法展开了深入浅出的描述，还有很多Toy Examples。
上传时间： 2017-06-06
上传用户：tonyshao
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 examples that are misclassified by each classifier. icsiboost implements Adaboost over stumps (one-level decision trees) on discrete and continuous attributes (words and real values). See http://en.wikipedia.org/wiki/AdaBoost and the papers by Y. Freund and R. Schapire for more details [1]. This approach is one of most efficient and simple to combine continuous and nominal values. Our implementation is aimed at allowing training from millions of examples by hundreds of features in a reasonable time/memory.
标签： meta-learning classifiers combining Boosting
上传时间： 2016-01-30
上传用户：songnanhua
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 learning (classification) algorithm with AdaBoost. A demo file (demo.m) is provided that demonstrates how these two files can be used with a classifier (basic threshold classifier) for two class classification problem.
标签： well-known algorithm AdaBoost Adaptive
上传时间： 2014-01-15
上传用户：qiaoyue
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
标签： well-known algorithm AdaBoost Adaptive
上传时间： 2013-12-31
上传用户：jiahao131
