%0 Journal Article
%T Positive Semidifinite Kernel Function of SVM Classification Machines under Finite-dimension Space Mapping<br>有限维空间映射下SVM分类机的半正定核函数
%A YUAN Feng-Shan
%A ZHU Si-Ming
%A <br>原峰山
%A 朱思铭
%J 计算机科学
%D 2006
%I 
%X With regard to feature of Support Vector Classification Machines,conditions and results to satisfy mapping of feature space in flnite-dimension are analyzed. The method to determine kernel function satisfied with positive semidefinite condition in mapping space of finite-dimension is put forward. The conclusion is that if Gram matrix which is formed by symmetrical function K is positive semidefinite, the original imported space is certainly mapped into a finitedimension feature space in which the inner product can be expressed by the kernel function K.
%K Finite-dimension space
%K Mapping space
%K Kernel function<br>有限维空间
%K 映射空间
%K 核函数
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=93A4408660584EA5&yid=37904DC365DD7266&vid=27746BCEEE58E9DC&iid=CA4FD0336C81A37A&sid=B78CD622C1934236&eid=E2E0FBFE4D7EFB94&journal_id=1002-137X&journal_name=计算机科学&referenced_num=0&reference_num=4