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。

An ameliorated algorithm that combined support vector machine (SVM) with k nearest neighbour (kNN) is presented and it comes into being as a new classifier,based on the research that SVM classifies some tested samples in error nearby the optimal super-plane.In the class phase,the algorithm computes the distance from the tested sample to the optimal super-plane of SVM in the feature space.If the distance is greater than the given threshold,the tested sample will be classified on SVM,otherwise,the kNN algorithm will be used based on the best distance measurement.The numerical experiments show that the mixed algorithm improve the accuracy compared to the sole SVM.