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The Role of Diversity in Ensemble Learning
Ensemble learning is a machine learning paradigm that achieves the state-of-the-art performance. Diversity was believed to be a key to a good performance of an ensemble approach, which, however, previously served only as a heuristic idea. We show that diversity can play the role of regularization. '''Papers''': * <b>Diversity regularized machine:</b> In the [^http://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/ijcai11drm.pdf|IJCAI'11 (PDF)] paper, we showed that diversity plays a role of regularization as in popular statistical learning approaches. * <b>Diversity regularized ensemble pruning:</b> In the [^{UP}papers/ecml12-divprune.pdf|ECML'12 (PDF)] paper, we proved that diversity defined on hypothesis output space plays a role of regularization, and use this principle to prune Bagging classifiers. '''Codes''': * Diversity-regularized SVM [^http://lamda.nju.edu.cn/code_DRM.ashx|(codes in Matlab)] * Diversity-regularized ensemble pruning [^http://lamda.nju.edu.cn/code_DREP.ashx|(codes in Matlab)]
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