Multi-feature fusion method based on support vector machine and k-nearest neighbor classifier
基于支持向量机和k-近邻分类器的多特征融合方法

The traditional classification methods only use one single classifier,which may lead to one-sidedness,low accuracy,and that the samples nearby the Support Vector Machine(SVM) hyperplanes are more easily misclassified.To solve these problems,the multi-feature fusion method based on SVM and K-Nearest Neighbor(KNN) classifiers was presented in this paper.Firstly,the features were divided into L groups and the SVM hyperplanes were constructed for each feature of training set.Secondly,the testing set was tested ...