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Abstract: High dimensionality of text can be a deterrent in applying complex learners such as Support Vector Machines to the task of text classification. Feature clustering is a powerful alternative to feature selection for reducing the dimensionality of text data. In this paper we propose a new informationtheoretic divisive algorithm for feature/word clustering and apply it to text classification. Existing techniques for such "distributional clustering" of words are agglomerative in nature and... (Update)
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BibTeX entry: (Update)
@misc{ text-journal,
author = "Algorithm For Text",
title = "Journal of Machine Learning Research 3 (2003) 1265-1287 Submitted 5/02;
Published 3/03 A Divisive Information-Theoretic Feature Clustering",
url = "citeseer.ist.psu.edu/766551.html" }
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