%0 Journal Article
%T Sample set shrinking strategy efficiently improving parameters seeking of support vector machines<br>有效提高SVM参数搜索效率的样本集缩减策略
%A DUAN Chong-wen
%A CHENG Li-zhi
%A <br>段崇雯
%A 成礼智
%J 计算机应用
%D 2007
%I 
%X The choice of kernel function and relative parameters plays an important role in Support Vector Machines (SVMs). It greatly influences the generalization performance of SVMs. It is time consuming to seek for optimal parameters when the training sample set is large. Concerning this problem, a sample set shrinking strategy was proposed. This method took some of the non-support-vector samples out of the training set; therefore efficiently reduced the set size. That is to say, with half the time consumed, a model can be constructed with testing accuracy just slightly changed.
%K support vector
%K sample set shrinking
%K grid searching
%K optimal parameters selection<br>支持向量
%K 样本集缩减
%K 网格搜索
%K 最优参数选取
%K 最优参数
%K 搜索效率
%K 训练样本集
%K 策略
%K support
%K vector
%K machines
%K seeking
%K parameters
%K improving
%K strategy
%K 搜索时间
%K 前提
%K 测试准确度
%K 样本点
%K 支持向量机
%K 消耗
%K 搜索算法
%K 网格
%K 影响
%K 能力
%K 模型
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=14E16CBD2743051224594C8963C26B31&yid=A732AF04DDA03BB3&vid=DB817633AA4F79B9&iid=0B39A22176CE99FB&sid=8C27CCA578E52082&eid=683005D16807E4FE&journal_id=1001-9081&journal_name=计算机应用&referenced_num=0&reference_num=12