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Learning Conceptual Descriptions of Categories

By Giuseppe Attardi, Maria Simi, Filippo Tanganelli, Alessandro Tommasi, Maria Simi and Ro Tommasi and Ro Tommasi

Abstract

In this work we propose a model to learn conceptual descriptions of categories from precategorized texts. The model is general and parametric, and it captures most of the statistical approaches to classication as well as allowing the denition of more symbolic learning schemes. The algorithm scheme has been instantiated into three dierent algorithms, which have been implemented and tested on a collection of documents obtained from the Web. As a possible application of the descriptions obtained, classication was done on a test set. Results are somewhat surprising, and stand in contrast with most experiments done in literature, possibly giving hints about a dierent research direction. 1 Introduction 1.1 Motivation The use of statistical analysis often allows the treatment of phenomena whose complexity goes beyond our modeling capabilities. As an example, the theory of chaotic systems shows how some complex phenomenon, such as the meteorological condition, is not describable..

Year: 1999
OAI identifier: oai:CiteSeerX.psu:10.1.1.36.3934
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