为了进一步提升支持向量机泛化性能，提出一种基于双重扰动的选择性支持向量机集成算法。
This paper proposed a selective Support Vector Machine (SVM) ensemble algorithm based on double disturbance to improve the generalization ability of SVM.
本文基于混合学习和集成学习的思想，将这两种方法应用于支持向量机建模技术中，主要解决预测分析问题。
This paper mainly focuses on the prediction problem by the application of hybrid and ensemble thinking into the modeling base on SVM.
对灰色预测GM(1,1)模型进行了分析，提出了集成灰色支持向量机的预测模型。
Based on grey prediction GM (1, 1) model, an integrated grey Support Vector Machine (SVM) model was presented.
我们将讨论拔靴集成法与多模激发法，以及这两个演算法是如何成功的被运用。我们也将介绍近来运用与拔靴集成法相似的方法，结合支持向量机所做的一些案例。
We discuss bagging and boosting and suggest some plausible justification for their success. We also describe some recent work about combining SVMs in a way similar to bagging.
我们将讨论拔靴集成法与多模激发法，以及这两个演算法是如何成功的被运用。我们也将介绍近来运用与拔靴集成法相似的方法，结合支持向量机所做的一些案例。
We discuss bagging and boosting and suggest some plausible justification for their success. We also describe some recent work about combining SVMs in a way similar to bagging.
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