%0 Journal Article
%T New sparse least squares support vector machine algorithm<br>一种新的最小二乘支持向量机稀疏化算法
%A WU Zong-liang
%A DOU Heng
%A <br>吴宗亮
%A 窦衡
%J 计算机应用
%D 2009
%I 
%X The recognition rate of Least Squares Support Vector Machine (LS-SVM) sparse algorithm rapidly decreases with the reduction of training samples in dealing with some pattern recognition issues, and the sparsification can not be achieved. To overcome such a shortage, a new sparse algorithm was proposed. The method was applied to radar range profile's recognition and the experimental results show its validity in recognition.
%K 最小二乘支持向量机
%K 稀疏化
%K 雷达一维距离像
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=858069862C8066B899C7B4F73BFFD3E5&yid=DE12191FBD62783C&vid=771469D9D58C34FF&iid=B31275AF3241DB2D&sid=9B1BEE8CA21457F1&eid=1593278DEEDA4D8F&journal_id=1001-9081&journal_name=计算机应用&referenced_num=0&reference_num=7