Ensemble methods in machine learning
Ensemble methods in machine learning
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DOI：
10.1007/3-540-45014-9_1
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发表时间：
2000-01-01
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影响因子：
--
通讯作者：
Dietterich, TG
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文献类型：
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作者：
Dietterich, TG
包围盒方法是一种学习算法，它构造一组分类器，然后通过对其预测进行（加权）投票来对新数据点进行分类。最初的集成方法是贝叶斯平均，但最近的算法包括纠错输出编码，Bagging和boosting。本文回顾了这些方法，并解释了为什么集成往往可以比任何单一的分类器更好地执行。回顾了一些比较集成方法的研究，并提出了一些新的实验来揭示Adaboost不能快速过拟合的原因。
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 experiments are presented to uncover the reasons that Adaboost does not overfit rapidly.