Instance-Based Learning Algorithms
Machine Learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
An evaluation of statistical spam filtering techniques
ACM Transactions on Asian Language Information Processing (TALIP)
Data Mining
Spam Filtering Using Statistical Data Compression Models
The Journal of Machine Learning Research
Semi-supervised Text Classification Using RBF Networks
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
A neuro-computational intelligence analysis of the global consumer software piracy rates
Expert Systems with Applications: An International Journal
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Over the years, various spam email filtering technology and anti-spam software products have been developed and deployed. Some are designed to stop spam email at the server level, and others apply machine learning algorithms at the client level to identify spam email based on message content. In this paper, a new spam filtering model, RBF-SF, is proposed that detects and classifies email messages by a radial basis function (RBF) network. The model utilizes the valuable email discriminative information from training data and can incorporate additional background email in its learning process. The empirical results of RBF-SF on two benchmark spam testing corpora and a performance comparison with several other popular text classifiers have shown that the model is capable of filtering spam email effectively.