Semi-supervised Text Classification Based on Self-training EM Algorithm
Jinshu Su · Journal of National University of Defense Technology · 2007
To improve computation efficiency,an enhanced EM algorithm based on self-training named STEM is proposed.In the E-step of each iteration,the unlabeled sample,whose class can be predicted by the current intermediate classifier with the most confidence,is moved to the labeled set and used in the M-step to train the next intermediate classifier.Therefore the mechanism of self-training by inter-result employing is introduced.Experimentation on text classification indicates that STEM outperforms EM in classification accuracy most of the time and improves the learning efficiency by reducing iterations.