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

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 …

Document classification is a growing interest in the research of text mining. Correctly
identifying the documents into particular category is still presenting challenge because of …

In this paper we study the use of a semi‐supervised agglomerative hierarchical clustering
(ssAHC) algorithm to text categorization, which consists of assigning text documents to …

The exponential growth of the internet has led to a great deal of interest in developing useful
and efficient tools and software to assist users in searching the Web. Document retrieval …

Most of the research on text categorization has focused on classifying text documents into a
set of categories with no structural relationships among them (flat classification). However, in …

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 …

This work deals with document classification. It is a supervised learning method (it needs a
labeled document set for training and a test set of documents to be classified). The …

The use of learning algorithms for text classification assumes the availability of a large
amount of documents which have been organized and labeled correctly by human experts …

Automatic organization of documents has become an important research issue since the
explosion of digital and online text information. There are mainly two machine learning …