%0 Journal Article
%T Classification text with incomplete data based on Bernoulli mixture mode<br>一种基于Bernoulli混合模型的不完整数据文本分类方法
%A CAI Chong-chao
%A WANG Shi-tong
%A <br>蔡崇超
%A 王士同
%J 计算机应用
%D 2007
%I 
%X It is an important issue to construct the text classification with incomplete data.An improved method that based on Bernoulli Mixture Model and Expectation Maximization(EM) algorithm was introduced.Based on Bernoulli Mixture Model and EM algorithm,by learning the labeled data,the initial value of likelihood function parameter was obtained first.Then the parameter estimate of prior probability model on the classifier with EM algorithm including weight was presented.Finally we got the improved classifier.The results show that our new method is better than the na've bayes text classification in the recall and precision.
%K incomplete data
%K text classification
%K naive bayes classification
%K Bernoulli mixture model
%K Expectation Maximization algorithm(EM)<br>不完整数据集
%K 文本分类
%K 朴素贝叶斯分类
%K Bernoulli混合模型
%K 期望最大化算法
%K Bernoulli
%K 混合模型
%K 不完整数据
%K 文本分类
%K 分类方法
%K mode
%K mixture
%K based
%K incomplete
%K data
%K text
%K 朴素贝叶斯算法
%K 查全率
%K 准确率
%K 结果
%K 实验
%K 先验概率模型
%K 分类器
%K 权值
%K 利用
%K 参数估计
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=361771BD5203F3A19FB9F7F385A1202C&yid=A732AF04DDA03BB3&vid=DB817633AA4F79B9&iid=94C357A881DFC066&sid=A31F2945A5873795&eid=72EB001A9B3C78CE&journal_id=1001-9081&journal_name=计算机应用&referenced_num=0&reference_num=9