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
Data Mining
A weighting approach for features based on real rough set
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 6
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Text classification is a problem applied to natural language texts that assigns a document into one or more predefined categories, based on its content. In this paper, we present an automatic text classification model that is based on the Radial Basis Function (RBF) networks. It utilizes valuable discriminative information in training data and incorporates background knowledge in model learning. This approach can be particularly advantageous for applications where labeled training data are in short supply. The proposed model has been applied for classifying spam email, and the experiments on some benchmark spam testing corpus have shown that the model is effective in learning to classify documents based on content and represents a competitive alternative to the well-known text classifiers such as naïve Bayes and SVM.