An innovative analyser for multi-classifier e-mail classification based on grey list analysis
Journal of Network and Computer Applications
Architecture of adaptive spam filtering based on machine learning algorithms
ICA3PP'07 Proceedings of the 7th international conference on Algorithms and architectures for parallel processing
An effective spam filter based on a combined support vector machine approach
International Journal of Internet Technology and Secured Transactions
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Spam is commonly defined as unsolicited email messages and the goal of spam categorization is to distinguish between spam and legitimate email messages. Many researchers have been trying to separate spam from legitimate emails using machine learning algorithms based on statistical learning methods. In this paper, an innovative and intelligent spam filtering model has been proposed based on support vector machine (SVM). This model combines both linear and nonlinear SVM techniques where linear SVM performs better for text based spam classification that share similar characteristics. The proposed model considers both text and image based email messages for classification by selecting an appropriate kernel function for information transformation.