As the amount of online text increases, the demand for text classification to aid the analysis and management of text is increasing. Text is cheap, but information, in the form of...
Chowdhury Mofizur Rahman, Ferdous Ahmed Sohel, Par...
Bayesian text classifiers face a common issue which is referred to as data sparsity problem, especially when the size of training data is very small. The frequently used Laplacian...
In this paper, we propose a new context-sensitive Bayesian learning algorithm. By modeling the distributions of data locations by a mixture of Gaussians, the new algorithm can uti...
In this paper, we report our work on spam filtering with three novel bayesian classification methods: Aggregating One-Dependence Estimators (AODE), Hidden Naïve Bayes (HNB), Loca...
This paper presents novel methods for classifying images based on knowledge discovered from annotated images using WordNet. The novelty of this work is the automatic class discove...