%0 Journal Article
%T Semi-supervised multi-label Boosting algorithm<br>一种半监督的多标签Boosting分类算法
%A ZHAO Chen-yang
%A SI Jie
%A <br>赵晨阳
%A 佀　洁
%J 计算机应用研究
%D 2012
%I 
%X For multi-label classification problem without enough labeled data, this paper proposed a new semi-supervised Boosting algorithm. It provided a semi-supervised general multi-label Boosting framework by using functional gradient descent method. It also used the conditional entropy as a regularization term on unlabeled data in classification model. Experimental result shows that the performance of the new semi-supervised Boosting algorithm can be improved by increasing unlabeled data; it also has a better result than traditional supervised Boosting algorithm by different measures.
%K Boosting algorithm
%K semi-supervised learning
%K multi-label classification<br>Boosting算法
%K 半监督学习
%K 多标签分类
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=9B3C227A81E2A28C587652A8762BDBD4&yid=99E9153A83D4CB11&vid=771469D9D58C34FF&iid=9CF7A0430CBB2DFD&sid=E40E84C3E1578338&eid=426A159B2664DD34&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=11