%0 Journal Article
%T SMO algorithm based on reserve working set strategy<br>基于预备工作集的最小序列优化算法
%A CHEN Wei-min
%A SONG Xiao-feng
%A JIANG Bin
%A <br>陈卫民
%A 宋晓峰
%A 姜斌
%J 计算机应用研究
%D 2007
%I 
%X In order to improve the training speed for large-scale problem, proposed a new strategy for the working set selection in SMO algorithm based on the tradeoff between the cost on working set selection and cache performance. This new strategy selected several maximal violating samples from cache as the reserve working set which would provide iterative working sets for the next several optimizing steps. The new strategy could improve the efficiency of the kernel cache and reduce the computational cost related to the working set selection. The results of theories and experiments demonstrate that the proposed method can reduce the training time, especially for large datasets.
%K support vector machine(SVM)
%K reserve working set(RWS)
%K working set selection
%K kernel cache<br>支持向量机
%K 预备工作集
%K 工作集选择
%K 核缓存
%K 工作集
%K 最小
%K 序列优化算法
%K strategy
%K working
%K reserve
%K based
%K 结果
%K 实验
%K 分析
%K 理论
%K 选择策略
%K 命中率
%K 方法
%K 迭代优化
%K 组成
%K 样本
%K 程度
%K 条件
%K cache
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=E65CA079C899DCEF&yid=A732AF04DDA03BB3&vid=B91E8C6D6FE990DB&iid=F3090AE9B60B7ED1&sid=42425781F0B1C26E&eid=1371F55DA51B6E64&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=10