Abstract
This paper explores the use of Support Vector Machines (SVMs) for learning text classifiers from examples. It analyzes the particular properties of learning with text data and identifies why SVMs are appropriate for this task. Empirical results support the theoretical findings. SVMs achieve substantial improvements over the currently best performing methods and behave robustly over a variety of different learning tasks. Furthermore they are fully automatic, eliminating the need for manual parameter tuning.
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© 1998 Springer-Verlag Berlin Heidelberg
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Joachims, T. (1998). Text categorization with Support Vector Machines: Learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds) Machine Learning: ECML-98. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0026683
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DOI: https://doi.org/10.1007/BFb0026683
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64417-0
Online ISBN: 978-3-540-69781-7
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Keywords
- Support Vector Machine
- Radial Basic Function
- Text Categorization
- Irrelevant Feature
- Linear Threshold Function
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.