%0 Journal Article
%T Multi-feature fusion method based on support vector machine and k-nearest neighbor classifier<br>基于支持向量机和k-近邻分类器的多特征融合方法
%A CHEN Li
%A CHEN Jing
%A <br>陈丽
%A 陈静
%J 计算机应用
%D 2009
%I 
%X 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 ...
%K Support Vector Machine (SVM)
%K K-Nearest Neighbor (KNN)
%K multi-feature fusion
%K inverse probability<br>支持向量机
%K k-近邻
%K 多特征融合
%K 后验概率
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=B444F7E6EDA8D3A4C656F1F3F85E698B&yid=DE12191FBD62783C&vid=771469D9D58C34FF&iid=38B194292C032A66&sid=07034C6B9EA4A53C&eid=9EB9AF946ABE60ED&journal_id=1001-9081&journal_name=计算机应用&referenced_num=0&reference_num=12