Identifying junk electronic mail in Microsoft outlook with a support vector machine

In this paper, we utilize a simple support vector machine to identify commercial electronic mail. The use of a personalized dictionary for model training provided a classification accuracy of 96.69%, while a much larger system dictionary achieved 95.26%. The classification system was subsequently implemented as an add-in for Microsoft Outlook XP, providing sorting and grouping capabilities using Outlook's interface to the typical desktop e-mail user.