Spam Filter Approach Based on Support Vector Machine
Wei Jiang · Jisuanji gongcheng · 2009
This paper presents a spam filter approach based on Support Vector Machine(SVM) to deal with cross language E-mail including Chinese and English, which provides the ability of integrating more statistical information.It optimizes the representation of linear kernel to improve time complexity and storage complexity, and adopts domain term extraction to improve the ability of semantic unit recognition and the performance of spam filter.Experiments on large-scale cross language corpora show that SVM-based approach increases the precision by 6.13% compared to Na?ve Bayes which is smoothed by Good-Turing, and increases classification accuracy by 8.18% compared to maximum entropy model.