Record link: 
http://hdl.handle.net/20.500.12708/53342
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Title: 
Support Vector Machines for Large Scale Text Mining in R
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Citation: 
Feinerer, I., & Karatzoglou, A. (2010). Support Vector Machines for Large Scale Text Mining in R. In Y. Lechevallier & G. Saporta (Eds.), Proceedings of COMPSTAT’2010 19th International Conference on Computational StatisticsParis France, August 22-27, 2010 Keynote, Invited and Contributed Papers (pp. 991–998). Physica. http://hdl.handle.net/20.500.12708/53342
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Publication Type: 
Inproceedings - Full-Paper Contribution
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Language: 
English
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Authors: 
Feinerer, Ingo 
Karatzoglou, Alexandros 
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Organisational Unit: 
E192-02 - Forschungsbereich Databases and Artificial Intelligence 
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Published in: 
Proceedings of COMPSTAT'2010 19th International Conference on Computational StatisticsParis France, August 22-27, 2010 Keynote, Invited and Contributed Papers 
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ISBN: 
978-3-7908-2604-3
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DOI of the book: 
10.1007/978-3-7908-2604-3
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Date (published): 
2010
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Event name: 
19th International Conference on Computational Statistics 
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Event date: 
22-Aug-2010 - 27-Aug-2010
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Event place: 
Paris, Frankreich, EU
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Number of Pages: 
8
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Publisher: 
Physica, Heidelberg, Deutschland
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Publisher: 
Physica, Heidelberg
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Peer reviewed: 
Yes
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Keywords: 
text mining; SVM; large scale
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Abstract: 
SVMs are an established tool in machine learning and data analysis. Though many implementations of SVM exist often specific applications require tailor made algorithms. In text mining in particular the data often comes in large sparse data matrices. Typical SVM algorithms like SMO do not take advantage of the sparsity, and do not scale well to data sets with millions of entries. In this paper we present an implementation of linear SVMs for R that address both of these issues.
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Research Areas: 
Logic and Computation: 100%
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Science Branch: 
Mathematik, Informatik
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Appears in Collections:
Conference Paper

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