Talks and Poster Presentations (with Proceedings-Entry):

I. Feinerer, A. Karatzoglou:
"Support Vector Machines for Large Scale Text Mining in R";
Talk: 19th International Conference on Computational Statistics, Paris, Frankreich; 08-22-2010 - 08-27-2010; in: "COMPSTAT 2010 --- Proceedings in Computational Statistics", Y. Lechevallier, G. Saporta (ed.); Physica, Heidelberg, Deutschland (2010), ISBN: 978-3-7908-2603-6; 999 - 1006.

English 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.

SVM, text mining, large scale

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

Created from the Publication Database of the Vienna University of Technology.