关键词: 支持向量机, 最近邻分类, 类代表点, 核函数, 特征空间, VC维

Abstract: A new algorithm that combined Support Vector Machine (SVM) with K Nearest neighbour (KNN) 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 KNN algorithm will be used.In KNN 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.

Key words: support vector machine, nearst neighbour algorithm, representative point, kernel function, feature space, VC Dimension