马氏距离法在支持向量机拒识区域中的应用

Mahalanobis Distance Method for Unclassifiable Region of Support Vector Machine

  • 摘要: 为克服传统多分类支持向量机中存在的拒识区域,提出一种基于马氏距离的拒识区域解决方案.该方法首先计算落入拒识区域中的样本点到每类样本集的马氏距离,然后选择较小的马氏距离对应的类为样本的所属类.标准数据实验结果表明,马氏距离法在实验数据上消除拒识区域,有效提高了算法的分类性能和泛化能力.

     

    Abstract: To overcome unclassifiable region(UR) in conventional multi-classification support vector machine,Mahalanobis distance method(MDM) based solution for unclassifiable region is presented.MDM firstly computes the distance between the sample in UR and every dataset,then selects the class with the least Mahalanobis distance for the sample.Experimental results on benchmark datasets show that MDM eliminates UR in experiment data and improves the classification capacity and generalization ability of the algorithm effectively on experimental datasets.

     

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