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Spam filtering is a classical online learning problem. When the size of training sample set becomes larger and larger, the speed of Online SVM is becoming slower and slower. Therefore, we relax the constraints of Online SVM and get the Relaxed Online SVM (ROSVM) model, which can not only improve the speed, but also can ensure the performance. In this paper, we applied this model to Chinese spam filter...
In this paper, we propose a feature selection method to speed up online SVM based spam filter. Online SVM gives state-of-the-art classification performance on online spam filtering on large benchmark data sets. However, its computational cost is very expensive for large-scale applications. Feature Selection is a crucial step to online SVM classification. We use a feature selection method based on...
The logistic regression model has achieved success in spam filtering. But it is disadvantaged by the equal adjustment of the feature weights appeared in both spam messages and ham ones during training period. This paper presents an improved logistic regression model which reduces the impact of the features appearing in both spam messages and ham ones. Byte level n-grams are employed to extract the...
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