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一种改进的结合K近邻法的SVM分类算法

DOI: 10.11834/jig.20091117

Keywords: 支持向量机,k近邻法,泛化错误,最佳距离度量

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Abstract:

在对支持向量机在超平面附近容易对测试样本造成错分进行研究的基础上,改进了将支持向量机分类和k近邻分类相结合的方法,形成了一种新的分类器。在分类阶段计算待识别样本和最优分类超平面的距离,如果距离差大于给定阈值可直接应用支持向量机分类,否则用最佳距离k近邻分类。数值实验表明,使用支持向量机结合最近邻分类的分类器分类比单独使用支持向量机分类具有更高的分类准确率。

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