A LVQ-based neural network anti-spam email approach
ACM SIGOPS Operating Systems Review
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InfoSecu '04 Proceedings of the 3rd international conference on Information security
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Mining spam email to identify common origins for forensic application
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A collaborative anti-spam system
Expert Systems with Applications: An International Journal
Behavior-based spam detection using a hybrid method of rule-based techniques and neural networks
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Revealing common sources of image spam by unsupervised clustering with visual features
Proceedings of the 2009 ACM symposium on Applied Computing
Review: A review of machine learning approaches to Spam filtering
Expert Systems with Applications: An International Journal
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Automatic thesaurus construction for spam filtering using revised back propagation neural network
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KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part I
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IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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WSEAS Transactions on Information Science and Applications
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Applied Soft Computing
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ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
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PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
A novel anti-spam email approach based on LVQ
PDCAT'04 Proceedings of the 5th international conference on Parallel and Distributed Computing: applications and Technologies
International Journal of Swarm Intelligence Research
Genetic optimized artificial immune system in spam detection: a review and a model
Artificial Intelligence Review
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In this paper we present a neural network based system for automated e-mail filing into folders and anti-spam filtering. The experiments show that it is more accurate than several other techniques. We also investigate the effects of various feature selection, weighting and normalization methods, and also the portability of the anti-spam filter across different users.