Improving Naive Bayes Text Classifier with Modified EM Algorithm

摘要

This paper presents the method of significantly improving conventional Bayesian statistical text classifier by incorporating accelerated EM (Expectation Maximization) algorithm. EM algorithm experiences a slow convergence and performance degrade in its iterative process, especially when real textual documents do not follow EM's assumptions. We propose a new accelerated EM algorithm that is simple yet has a fast convergence speed and allow to estimate a more accurate classification model on Bayesian text classifier.