Text categorization with Support Vector Machines: Learning with many relevant features View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

1998

AUTHORS

Thorsten Joachims

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. More... »

PAGES

137-142

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bfb0026683

DOI

http://dx.doi.org/10.1007/bfb0026683

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1051853845


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