Detecting Unwanted Email Using VAT

Author:

Islam Md Rafiqul,Chowdhury Morshed U.

Publisher

Springer Berlin Heidelberg

Reference32 articles.

1. Zhang, J., et al.: A Modified logistic regression: An approximation to SVM and its applications in large-scale text categorization. In: Proceedings of the 20th International Conference on Machine Learning, pp. 888–895. AAAI Press, Menlo Park (2003)

2. Sahami, M., Dumais, S., Heckerman, D., Horvitz, E.: A bayesian approach to filtering junk e-mail. In: Learning for Text Categorization: Papers from the Workshop, Madison, Wisconsin, AAAI Technical Report WS-98-05 (1998)

3. Androutsopoulos, I., et al.: Learning to filter spam e-mail: A comparison of a Naive Bayesian and a memory-based approach. In: Proceedings of the Workshop on Machine Learning and Textual Information Access, 4th European Conference on Principles and Practice of Knowledge Discovery in Databases, Lyon, France, pp. 1–13 (2000)

4. Drucker, H., Shahrary, B., Gibbon, D.C.: Support vector machines: relevance feedback and information retrieval. Inform. Process. Manag. 38(3), 305–323 (2003)

5. Islam, R., Chowdhury, M., Zhou, W.: An Innovative Spam Filtering Model Based on Support Vector Machine. In: Proceedings of the IEEE International Conference on Intelligent Agents, Web Technologies and Internet Commerce, vol. 2, pp. 348–353 (28-30, 2005)

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