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 …

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 …

This paper proposes a new approach for text categorization, based on a feature projection
technique. In our approach, training data are represented as the projections of training …

Automatic text categorization has always been an important application and research topic
since the inception of digital documents. Today, text categorization is a necessity due to the …

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

It is well known that the classification effectiveness of the text categorization system is not
simply a matter of learning algorithms. Text representation factors are also at work. This …

Supervised text categorization is a machine learning task where a predefined category label
is automatically assigned to a previously unlabelled document based upon characteristics of …

This paper proposes a new feature selection score for text classification. The value that this
score assigns to each feature has an appealing Bayesian interpretation, being the posterior …

We describe work on automatically assigning classification labels to books using the Library
of Congress Classification scheme. This task is non-trivial due to the volume and variety of …

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 …