Recently, spam has become an increasingly important problem. In this paper, a support vector machine (SVM) is used as the spam filter. Then a study is made of the effect of classification error rate when different subsets of corpora are used, and of the filter accuracy when SVM's with linear, polynomial, or RBF kernels is used. Also an investigation is made of the effect of the size of attribute sets. Based on the experimental results and analysis, it is concluded that SVM will be a very good alternative for building anti-spam classifiers, with consideration of a good combination of accuracy, consistency, and speed. © Springer-Verlag 2009.
CITATION STYLE
Xie, C., Ding, L., & Du, X. (2009). Anti-spam filters based on support vector machines. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5821 LNCS, pp. 349–357). https://doi.org/10.1007/978-3-642-04843-2_37
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