%0 Journal Article
%T Study on the Fast Training Algorithm of Iteratively Re-weighted Least Squares Support Vector Machine<br>迭代重加权最小二乘支持向量机快速算法研究
%A WEN Wen
%A HAO Zhi-feng
%A SHAO Zhuang-feng
%A <br>温雯
%A 郝志峰
%A 邵壮丰
%J 计算机科学
%D 2010
%I 
%X Iteratively reweighted method is an important approach to improve the robustness of least sctuares support vector machine(LS-SVM). However, the reweighting and retraining procedure demands a lot of computational time, which makes it impossible for practical applications. In this paper, the iteratively reweighted least squares support vector machine (IRLS-SVM) was studied. An improved training algorithm of IRLS-SVM was proposed. It is based on novel numerical method, and can effectively reduce the computational complexity of IRIS-SVM. Three different weight funclions were implemented in the IRLS-SVM. Experiments on simulated instances and real-world datasets demonstrate the validity of this algorithm. Meanwhile, the results reveal that different weight function may require different computational time for the fast training algorithm of IRLS-SVM.
%K Support vector machines
%K Robustness
%K Outliers
%K Fast algorithm<br>支持向量机
%K 稳健性
%K 异常样本
%K 快速算法
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=B4F13BE8F2DA9D3547168C1B057453A3&yid=140ECF96957D60B2&vid=42425781F0B1C26E&iid=5D311CA918CA9A03&sid=A1266CF37D675CF1&eid=CA5852BD1A173B3A&journal_id=1002-137X&journal_name=计算机科学&referenced_num=0&reference_num=20