Many machine learning algorithms have been applied to text classification tasks. In the
machine learning paradigm, a general inductive process automatically builds a text classifier …

A wide range of supervised learning algorithms has been applied to Text Categorization.
However, the supervised learning approaches have some problems. One of them is that …

The goal of text categorization is to classify documents into a certain number of predefined
categories. The previous works in this area have used a large number of labeled training …

Traditionally, text classifiers are built from labeled training examples. Labeling is usually
done manually by human experts (or the users), which is a labor intensive and time …

Text classification is the process of classifying documents into predefined categories based
on their content. Existing supervised learning algorithms to automatically classify text need …

Supervised learning algorithms usually require large amounts of training data to learn
reasonably accurate classifiers. Yet, in many text classification tasks, labeled training …

Text categorization, also known as text classification is a supervised classification problem. It
aims to assign a predefined class label or group to a new or unknown text document. Most of …

In traditional text classification, a classifier is built using labeled training documents of every
class. This paper studies a different problem. Given a set P of documents of a particular …

Text categorization–the assignment of natural language texts to one or more predefined
categories based on their content–is an important component in many information …

Text classification is the process of classifying documents into predefined categories based
on their content. It is the automated assignment of natural language texts to predefined …