%0 Journal Article
%T Microcalcification Detection Based on K-means Cluster and Multiple Kernel Support Vector Machine<br>基于K均值聚类和多核SVM的微钙化簇检测
%A CHANG Tian-tian
%A LIU Hong-wei
%A FENG Jun
%A <br>常甜甜
%A 刘红卫
%A 冯筠
%J 计算机科学
%D 2009
%I 
%X Considering the unbalanced distribution of the training samples and the multiformity of the features.A multiple kernel SVM based on K-means cluster algorithm was proposed.Firstly,training samples was clustered into K classes,different penalty factors were used for each class in order to balance the contributions of each class.Secondly,the multiple kernel support vector machine was proposed for diversity of the features.The stabilized training sample was obtained via active feedback learning.The result show ...
%K K-means cluster
%K Multiple kernel SVM
%K Microcalcification
%K Active feedback learning<br>K均值聚类
%K 多核支持向量机
%K 微钙化簇
%K 主动反馈学习
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=25B3DC7C4D0434E2D709BD840149CF0B&yid=DE12191FBD62783C&vid=933658645952ED9F&iid=5D311CA918CA9A03&sid=FA89360EB995A8AD&eid=FD7C952458BFB5D8&journal_id=1002-137X&journal_name=计算机科学&referenced_num=0&reference_num=13