%0 Journal Article
%T Identifying and Correcting Mislabled Training Instances Using Bayes<br>基于Bayes的有噪训练集去噪方法研究
%A LUO Jun-jie
%A SUN Jiang-wen
%A WANG Chong-jun
%A CHEN Shi-fu
%A <br>罗俊杰
%A 孙江文
%A 王崇骏
%A 陈世福
%J 计算机科学
%D 2008
%I 
%X De-noising is a basic pretreatment in the process of training a classifier.Most traditional de-noising approaches only delete instances tagged as noise which obviously also eliminates the useful information in these instances.A new approach is presented with which we can not only identify noise but also correct it,so that the useful information will be reserved.
%K Noise
%K Noise identifying
%K Noise correcting<br>噪声
%K 噪声辨别
%K 噪声纠正
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=DE58E277BEF7971ADF0FAC780E6A31F6&yid=67289AFF6305E306&vid=6209D9E8050195F5&iid=9CF7A0430CBB2DFD&sid=527AEE9F3446633A&eid=CEC789B3C68C3BB3&journal_id=1002-137X&journal_name=计算机科学&referenced_num=0&reference_num=15