Abstract
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.
This research was financially supported by Hansung University in the year of 2003.
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Kim, Hj., Chang, Jy. (2003). Improving Naïve Bayes Text Classifier with Modified EM Algorithm. In: Zhong, N., Raś, Z.W., Tsumoto, S., Suzuki, E. (eds) Foundations of Intelligent Systems. ISMIS 2003. Lecture Notes in Computer Science(), vol 2871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39592-8_45
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DOI: https://doi.org/10.1007/978-3-540-39592-8_45
Publisher Name: Springer, Berlin, Heidelberg
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