Application of Linear Classifier on Chinese Spam Filtering

Spam is a key problem in electronic communication. Especially in large-scale email systems. Content-based filtering is one mainstream method of combating this threat in its forms, an e-mail filtering system can learn directly from a user's mail set, but the previous Content-based filtering methods are hard to find a balance between efficiency and effectiveness. Such algorithms of text categorization as Naive Bayes, kNN, Decision Tree and Boosting can be applied in spam filtering. However, the effectiveness of Naive Bayes is limited and it is not fit for instant feedback learning.