A Comparison of Support Vector Machines and Self-Organizing Maps for e-Mail Categorization

This paper reports on experiments in multi-class document categorization with support vector machines and self-organizing maps. A data set consisting of personal e-mail messages is used for the experiments. Two distinct document representation formalisms are employed to characterize these messages, namely a standard word-based approach and a character n-gram document representation. Based on these document representations, the categorization performance of both machine learning approaches is assessed and a comparison is given.

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