[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.5391/IJFIS.2008.8.1.052

Modified Version of SVM for Text Categorization

Jo, Tae-Ho (School of Computer and Information Engineering Inha University)

Publication Information

International Journal of Fuzzy Logic and Intelligent Systems / v.8, no.1, 2008 , pp. 52-60 More about this Journal

Abstract

This research proposes a new strategy where documents are encoded into string vectors for text categorization and modified versions of SVM to be adaptable to string vectors. Traditionally, when the traditional version of SVM is used for pattern classification, raw data should be encoded into numerical vectors. This encoding may be difficult, depending on a given application area of pattern classification. For example, in text categorization, encoding full texts given as raw data into numerical vectors leads to two main problems: huge dimensionality and sparse distribution. In this research, we encode full texts into string vectors, and apply the modified version of SVM adaptable to string vectors for text categorization.

Keywords

String Vector; Text Categorization; Support Vector Machine;

Citations & Related Records

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