SVM-KNN分类器——一种提高SVM分类精度的新方法

本文提出了一种将支持向量机分类和最近邻分类相结合的方法,形成了一种新的分类器.首先对支持向量机进行分析可以看出它作为分类器实际相当于每类只选一个代表点的最近邻分类器,同时在对支持向量机分类时出错样本点的分布进行研究的基础上,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直接应用支持向量机分类,否则代入以每类的所有的支持向量作为代表点的K近邻分类.数值实验证明了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,同时可以较好地解决应用支持向量机分类时核函数参数的选择问题.

SVM-KNN Classifier--A New Method of Improving the Accuracy of SVM Classifier

A new algorithm that combined Support Vector Machine (SVM) with K Nearest neighbour ( K NN) is presented and it comes into being a new classifier.The classifier based on taking SVM as a 1NN classifier in which only one representative point is selected for each class.In the class phase,the algorithm computes the distance from the test sample to the optimal super plane of SVM in feature space.If the distance is greater than the given threshold,the test sample would be classified on SVM;otherwise,the K NN algorithm will be used.In K NN algorithm,we select every support vector as representative point and compare the distance between the testing sample and every support vector.The testing sample can be classed by finding the k nearest neighbour of testing sample.The numerical experiments show that the mixed algorithm can not only improve the accuracy compared to sole SVM,but also better solve the problem of selecting the parameter of kernel function for SVM.