Classification text with incomplete data based on Bernoulli mixture mode
一种基于Bernoulli混合模型的不完整数据文本分类方法

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.