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During the last decade the number of spam messages sent has increased significantly. These undesired emails place a heavy burden on end users and email service providers. As a result, a tenacious struggle to outsmart each other exists between people who send spam and the spam filter providers. Constant innovation is therefore of vital importance to curb the rapid increase of spam. In this paper a Generalized Additive Neural Network (GANN) is harnessed to detect spam. This relative new type of neural network has a number of strengths that makes it a suitable classifier of spam. An automated GANN construction algorithm is applied to a spam data set.
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