Spam is a key problem in electronic communication. The increasing volume of spam has become a serious threat not only to the Internet, but also to society. Content-based filtering is one mainstream method of combating this threat in its various forms, but the previous Content-based filtering methods are hard to find a balance between efficiency and effectiveness. In this paper we intend to seek a linear solve for this problem, and two online linear classifiers: the Perceptron and Winnow are explored for this task in three benchmark corpora, which include English corpus PU1, Lingspam and Chinese corpus 2005-Jun, Our experiments conclude that both of these classifiers can filter spam emails effectively as well as efficiently. It is also show that they perform much better than a standard Naïve Bayes method. In fact, to the best of our knowledge, they have a state-of-the-art performance for filtering Chinese spam emails, at least on the above corpora. Furthermore, both of the two classifiers are easily adaptively updated, thus are suitable for real dynamic environment.
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