COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Support Vector Machine Active Learning with Application sto Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Active learning with statistical models
Journal of Artificial Intelligence Research
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Spam filtering is defined as a task trying to label emails with spam or ham in an online situation. The online feature requires the spam filter has a strong timely generalization and has a high processing speed. Machine learning can be employed to fulfill the two requirements. In this paper, we propose a SVMEL (SVM Ensemble Learning) method to combine five simple filters for higher accuracy and an active learning method to choose training emails for less training time. The experiments results show the filter applying active learning method can reduce requirements of labeled training emails and reach steady-state performance more quickly.