%0 Journal Article
%T Experimental Comparison of Support Vector Machine Training Algorithms<br>支持向量机训练算法的实验比较
%A JI Shui-wang
%A JI Wang-tian
%A <br>姬水旺
%A 姬旺田
%J 计算机应用研究
%D 2004
%I 
%X Support vector learning algorithm is based on structural risk minimization principle.It combines two remarkable ideas: maximum margin classifiers and implicit feature spaces defined by kernel function.Presents a comprehensive comparison of three mainstream learning algorithms: SVM~(light),Bsvm,and SvmFu using face detection,MNIST,and USPS hand-written digit recognition applications.
%K Statistical Learning Theory
%K Support Vector Machine
%K Training Algorithms<br>统计学习理论
%K 支持向量机
%K 训练算法
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=F28905D55920C2AD&yid=D0E58B75BFD8E51C&vid=659D3B06EBF534A7&iid=708DD6B15D2464E8&sid=13553B2D12F347E8&eid=A04140E723CB732E&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=4&reference_num=33