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 examines the use of inductive learning to categorize natural language
documents into prede ned content categories. Categorization of text is of increasing …

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) …

This paper focuses on a comparative evaluation of a wide-range of text categorization
methods, including previously published results on the Reuters corpus and new results of …

Text categorization canbeviewed asaprocessof catego~ search, in which one or more
categories for a testdocument are searchedfor by using given training documents with …

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 …

This paper presents the design and evaluation of a text categorization method based on the
Hierarchical Mixture of Experts model. This model uses a divide and conquer principle to …

Most previous works of feature selection emphasized only the reduction of high
dimensionality of the feature space. But in cases where many features are highly redundant …

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 …

We tackle two different problems of text categorization (TC), namely feature selection and
classifier induction. Feature selection (FS) refers to the activity of selecting, from the set of r …