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| A. Basu, C. Watters, M. Shepherd, "Support Vector Machines for Text Categorization," 2013 46th Hawaii International Conference on System Sciences, vol. 4, pp. 103c, 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 4, 2003. | |||
| BibTex | x | ||
| @article{ 10.1109/HICSS.2003.1174243, author = {A. Basu and C. Watters and M. Shepherd}, title = {Support Vector Machines for Text Categorization}, journal ={2013 46th Hawaii International Conference on System Sciences}, volume = {4}, year = {2003}, isbn = {0-7695-1874-5}, pages = {103c}, doi = {http://doi.ieeecomputersociety.org/10.1109/HICSS.2003.1174243}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - 2013 46th Hawaii International Conference on System Sciences TI - Support Vector Machines for Text Categorization SN - 0-7695-1874-5 SP EP A1 - A. Basu, A1 - C. Watters, A1 - M. Shepherd, PY - 2003 KW - null VL - 4 JA - 2013 46th Hawaii International Conference on System Sciences ER - | |||
Text categorization is the process of sorting text documents into one or more predefined categories or classes of similar documents. Differences in the results of such categorization arise from the feature set chosen to base the association of a given document with a given category. Advocates of text categorization recognize that the sorting of text documents into categories of like documents reduces the overhead required for fast retrieval of such documents and provides smaller domains in which the users may explore similar documents.
In this paper we are interested in examining whether automatic classification of news texts can be improved by a prefiltering the vocabulary to reduce the feature set used in the computations. First we compare artificial neural network and support vector machine algorithms for use as text classifiers of news items. Secondly, we identify a reduction in feature set that provides improved results.