This work utilizes the Bayes formula to vectorize a document according to a probability
distribution based on keywords reflecting the probable categories that the document may …

This work implements an enhanced hybrid classification method through the utilization of the
naïve Bayes classifier and the Support Vector Machine (SVM). In this project, the Bayes …

At present several vectorization approaches are used to transform text documents into a
numerical format. A huge number of features converted from text data from a single …

An increasing number of computational and statistical approaches have been used for text
classification, including nearest-neighbor classification, naïve Bayes classification, support …

In the traditional setting, text categorization is formulated as a concept learning problem
where each instance is a single isolated document. However, this perspective is not …

The vector space model (VSM) is a textual representation method that is widely used in
documents classification. However, it remains to be a space-challenging problem. One …

This paper presents a probabilistic mixture modeling framework for the hierarchic
organisation of document collections. It is demonstrated that the probabilistic corpus model …

In this paper, we propose a new method of representing text documents based on feature
clustering approach. The proposed representation method is very powerful in reducing the …

Text Categorization is the process of assigning documents to a set of previously fixed
categories. A lot of research is going on with the goal of automating this time‐consuming …

Text categorization is the task of classifying natural language documents into a set of
predefine categories. Documents are typically represented by sparse vectors under the …