Ensemble Methods in Machine Learning

@article{Dietterich:00, abstract = {. Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include error-correcting output coding, Bagging, and boosting. This paper reviews these methods and explains why ensembles can often perform better than any single classifier. Some previous studies comparing ensemble methods are reviewed, and some new...}, author = {Dietterich, Thomas G. }, citeulike-article-id = {263075}, journal = {Lecture Notes in Computer Science}, keywords = {machine-learning}, pages = {1--15}, posted-at = {2008-06-24 13:07:08}, priority = {2}, title = {Ensemble Methods in Machine Learning}, url = {http://citeseer.ist.psu.edu/dietterich00ensemble.html}, volume = {1857}, year = {2000} }

TY - JOUR ID - Dietterich:00 L3 - citeulike-article-id:263075 TI - Ensemble Methods in Machine Learning JF - Lecture Notes in Computer Science VL - 1857 SP - 1 EP - 15 N2 - . Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include error-correcting output coding, Bagging, and boosting. This paper reviews these methods and explains why ensembles can often perform better than any single classifier. Some previous studies comparing ensemble methods are reviewed, and some new... KW - machine-learning AU - Dietterich, Thomas PY - 2000/// UR - http://citeseer.ist.psu.edu/dietterich00ensemble.html ER -