%0 Journal Article
%T Improved SMO algorithm with different error costs<br>基于不同惩罚系数的SMO改进算法
%A TIAN Da-dong
%A DENG Wei
%A <br>田大东
%A 邓伟
%J 计算机应用
%D 2008
%I 
%X When Keerthi's Sequential Minimal Optimization(SMO) algorithm is applied to the classification of unbalanced datasets,it not only leads to a poor classification performance but makes the result unstable.In order to overcome the difficulty,an improved SMO algorithm that used different error costs for different class was presented.Besides,the formula and the steps of the improved SMO algorithm were given.Experimental results show that our algorithm's ability of dealing with unbalanced datasets can be improved...
%K unbalanced datasets
%K error costs
%K Sequential Minimal Optimization(SMO)<br>非平衡数据集
%K 惩罚系数
%K 序贯最小优化
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=3568FEC3276C488188E39EA89CD19052&yid=67289AFF6305E306&vid=D3E34374A0D77D7F&iid=9CF7A0430CBB2DFD&sid=D0540156A5D11138&eid=4BC5DC3526DFBBB5&journal_id=1001-9081&journal_name=计算机应用&referenced_num=0&reference_num=6