Making large-scale support vector machine learning practical
Advances in kernel methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
A Comparative Study of Classification Based Personal E-mail Filtering
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Email classification with co-training
CASCON '01 Proceedings of the 2001 conference of the Centre for Advanced Studies on Collaborative research
Neural Computation
On effective e-mail classification via neural networks
DEXA'05 Proceedings of the 16th international conference on Database and Expert Systems Applications
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
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For addressing the growing problem of junk E-mail on the Internet, this paper proposes an effective E-mail classifying technique. Our work handles E-mail messages as semi-structured documents consisting of a set of fields with predefined semantics and a number of variable length free-text contents. The main contributions of this paper include the following: First, we present a Support Vector Machine (SVM) based model that incorporates the Principal Component Analysis (PCA) technique to reduce the data in terms of size and dimensionality of the input feature space. As a result, the input data become classifiable with fewer features, and the training process has faster convergence speed. Second, we build the classification model using both the $\mathcal{C}$-support vector machine and v-support vector machine algorithms. Various control parameters for performance tuning are studied in an extensive set of experiments. The results of our performance evaluation indicate that the proposed technique is effective in E-mail classification.