A survey of learning-based techniques of email spam filtering

Author:

Blanzieri Enrico,Bryl Anton

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Linguistics and Language,Language and Linguistics

Reference108 articles.

1. Agrawal B, Kumar N, Molle M (2005) Controlling spam emails at the routers. In: Proceedings of the IEEE international conference on communications, ICC 2005, vol 3, pp 1588–1592

2. Albrecht K, Burri N, Wattenhofer R (2005) Spamato—an extendable spam filter system. In: Proceedings of second conference on email and anti-spam, CEAS’2005

3. Androutsopoulos I, Koutsias J, Chandrinos KV, Spyropoulos CD (2000a) An evaluation of naive bayesian anti-spam filtering. In: Potamias G, Moustakis V, van Someren M (eds) Proceedings of the workshop on machine learning in the new information age, 11th European conference on machine learning, ECML 2000, pp 9–17

4. Androutsopoulos I, Koutsias J, Chandrinos KV, Spyropoulos CD (2000b) An experimental comparison of naive bayesian and keyword-based anti-spam filtering with personal e-mail messages. In: Proceedings of the 23rd annual international ACM SIGIR conference on research and development in information retrieval, SIGIR ’00. ACM Press, New York, NY, USA, pp 160–167. ISBN 1-58113-226-3. http://doi.acm.org/10.1145/345508.345569

5. Androutsopoulos I, Paliouras G, Karkaletsis V, Sakkis G, Spyropoulos C, Stamatopoulos P (2000c) Learning to filter spam e-mail: a comparison of a naive bayesian and a memory-based approach. In: Zaragoza H, Gallinari P, Rajman M (eds) Proceedings of the workshop on machine learning and textual information access, 4th European conference on principles and practice of knowledge discovery in databases, PKDD 2000 pp 1–13

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