Text categorization is useful for indexing documents for information retrieval, filtering parts
for document understanding, and summarizing contents of documents of special interests …

Typical text classifiers learn from example and training documents that have been manually
categorized. In this research, our experiment dealt with the classification of news wire …

This paper reports a controlled study with statistical significance tests on five text
categorization methods: the Support Vector Machines (SVM), a k-Nearest Neighbor (kNN) …

Text categorization is the process of algorithmically analyzing an electronic document to
assign a set of categories (or index terms) that succinctly describe the content of the …

The information retrieval community is becoming increasingly interested in machine learning
techniques, of which text categorization is an application. This paper describes how we have …

A system that performs text categorization aims to assign appropriate categories from a
predefined classification scheme to incoming documents. These assignments might be used …

Text categorization is the task of discovering the category or class text documents belongs
to, or in other words spotting the correct topic for text documents. While there today exists …

Automatic text categorization is an important research area and has a potential for many text-
based applications including text routing and filtering. Typical text classifiers learn from …

In this paper, we describe an automated learning approach to text categorization based on
perception learning and a new feature selection metric, called correlation coefficient. Our …

This paper presents a method that exploits the hierarchical structure of an indexing
vocabulary to guide the development and training of machine learning methods for …