Ensemble Methods in Machine Learning
نویسنده
چکیده
Ensemble methods are learning algorithms that construct a set of classi ers and then classify new data points by taking a weighted vote of their predictions The original ensemble method is Bayesian aver aging 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 classi er Some previous studies comparing ensemble methods are reviewed and some new experiments are presented to uncover the reasons that Adaboost does not over t rapidly
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تاریخ انتشار 2000