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https://link.springer.com/content/pdf/10.1007%2FBFb0026683.pdf
Text Categorization with Support Vector Machines: Learning with Many Relevant Features Thorsten Joachims Universit£t Dortmund lnformatik LS8, Baroper Str. 301 44221 Dortmund, Germany Abstract. This paper explores the use of Support Vector Machines (SVMs) for learning text classifiers from examples. It analyzes the par-Cited by: 10558
https://link.springer.com/chapter/10.1007%2FBFb0026683
Jun 16, 2005 · 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.Cited by: 10558
https://www.cs.cornell.edu/people/tj/publications/joachims_98a.pdf
tional text classi cation metho ds SVMs will pro etobe v ery robust, eliminating the need for exp ensiv e parameter tuning. 2 T ext Categorization The goal of text categorization is the classi cation of do cumen ts in to a xed n um b er of prede ned categories. Eac h do cumen t can b e in m ultiple, exactly one, or no category at all. Using mac
https://dl.acm.org/doi/10.1007/BFb0026683
Text categorization with support vector machines: Learning with many relevant features. Technical Report 23, Universität Dortmund, LS VIII, 1997. Google Scholar; J. Kivinen, M. Warmuth, and P. Auer. The perceptron algorithm vs. winnow: Linear vs. logarithmic mistake bounds when …
http://www.ist.temple.edu/~vucetic/cis525spring2004/meghneel.ppt
Text Categorization With Support Vector Machines: Learning With Many Relevant Features By Thornsten Joachims Presented By Meghneel Gore Goal of Text Categorization Classify documents into a number of pre-defined categories.
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