When trained and evaluated on accurately labeled datasets, online email spam filters are remarkably effective, achieving error rates an order of magnitude better than classifie...
Signature-based collaborative spam detection (SCSD) systems provide a promising solution addressing many problems facing statistical spam filters, the most widely adopted technol...
Abstract. Spam is serious problem that affects email users (e.g. phishing attacks, viruses and time spent reading unwanted messages). We propose a novel spam email filtering appr...
We describe an in-depth analysis of spam-filtering performance of a simple Naive Bayes learner and two extended variants. A set of seven mailboxes comprising about 65,000 mails f...
Many of the first successful applications of statistical learning to anti-spam filtering were personalized classifiers that were trained on an individual user’s spam and ham ...