资 源 简 介
Note: This project has been moved to benob/icsiboost on GitHub.
Boosting is a meta-learning approach that aims at combining an ensemble of weak classifiers to form a strong classifier. Adaptive Boosting (Adaboost) is 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. This approach is one of the 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 (or millions of sparse features) in a reasonable time/memory. It includes classification time code for c, python and java.
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文 件 列 表
icsiboost_tests
appos.data
20news.shyp
adult.data
20news.test
20news.data
adult.test
personref.test
appos.test.out
adult.names
adult.iter
README
personref.shyp
personref.data
20news.test.out
adult.shyp
personref.test.out
appos.test
appos.iter
personref.iter
20news.names
appos.names
appos.shyp
20news.iter
personref.names
adult.test.out
