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Inductive Learning Algorithms and
Representations for Text Categorization
January 1, 1998
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Download Document
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BibTex
Authors

Susan Dumais

John Platt

David Heckerman

Mehran Sahami
Published In

7th International Conference on Information and Knowledge Management
Publication Type
Inproceedings
Book Title
7th International Conference on Information and Knowledge Management
Pages
148-152

Abstract

Related Info
Abstract
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 organization
and management tasks. We compare the effectiveness of five different automatic learning
algorithms for text categorization in terms of learning speed, realtime classification speed, and
classification accuracy. We also examine training set size, and alternative document
representations. Very accurate text classifiers can be learned automatically from training
examples. Linear Support Vector Machines (SVMS) are particularly promising because they are
very accurate, quick to train, and quick to evaluate.
Related Info
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 Support Vector Machines
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 Algorithms
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 Microsoft Research Lab - Redmond
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