%0 Journal Article
%T Algorithm for learning centre of single class based on k-means and semi-supervised mechanism<br>基于k-means和半监督机制的单类中心学习算法
%A LI Zhi-sheng
%A SUN Yue-heng
%A HE Pi-lian
%A HOU Yue-xian
%A <br>李志圣
%A 孙越恒
%A 何丕廉
%A 侯越先
%J 计算机应用
%D 2008
%I 
%X A new algorithm named "single-means" was presented to improve the centre estimation of the object class when a hybrid data set had unknown k value and feature of accumulating to centre. Based on that k-means algorithm was equivalent to Expectation Maximum (EM) algorithm on a special hybrid Gaussian model, it was proved that given a data set generated by the above Gaussian model, the true centre of the object Gaussian distribution could be converged by a new algorithm. The new algorithm was applied in learning the centre of single text class. The experiment shows that given a small labeled text set, the new algorithm can get a better centre, and is robust on sparse data set and that with great variance.
%K k-means
%K single-means<br>单类学习
%K 半监督学习
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=658C8BDC182EB5F8119E926054CDA2E5&yid=67289AFF6305E306&vid=D3E34374A0D77D7F&iid=F3090AE9B60B7ED1&sid=75359DB37CA6AEF5&eid=71E51BD3D819EC5A&journal_id=1001-9081&journal_name=计算机应用&referenced_num=2&reference_num=7