Abstract
Many solutions have been deployed to prevent harmful effects from spam mail. Typical methods are either pattern matching using the keyword or method using the probability such as naive Bayesian method. In this paper, we proposed a classification method of spam mail from normal mail using support vector machine, which has excellent performance in binary pattern classification problems. Especially, the proposed method efficiently practices a learning procedure with a word dictionary by the n-gram. In the conclusion, we showed our proposed method being superior to others in the aspect of comparing performance.
This work was supported (in part) by the Ministry of Information&Communications, Korea, under the Information Technology Research Center (ITRC) Support program.
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© 2004 Springer-Verlag Berlin Heidelberg
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Moon, J., Shon, T., Seo, J., Kim, J., Seo, J. (2004). An Approach for Spam E-mail Detection with Support Vector Machine and n-Gram Indexing. In: Aykanat, C., Dayar, T., Körpeoğlu, İ. (eds) Computer and Information Sciences - ISCIS 2004. ISCIS 2004. Lecture Notes in Computer Science, vol 3280. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30182-0_36
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DOI: https://doi.org/10.1007/978-3-540-30182-0_36
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