支持向量机集成(Support Vector Machine Ensemble,SVME)是指按照一定规则将有限个子SVM的结果结合起来,以便对新样本进行分类预测的学习算法。
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self-adaptive support vector machine ensemble 自适应增强支持向量机集成
ensemble support vector machine 集成支持向量机
Compared with the single suppo vector machine method, the support vector machine ensemble method has better classification accuracy.
模拟实验结果表明，该方法具有明显优于单一支持向量机的更高的分类准确率。
This paper proposed a selective Support Vector Machine (SVM) ensemble algorithm based on double disturbance to improve the generalization ability of SVM.
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。
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