基于C-SVM和KPCA的垃圾邮件检测研究

Spam detection approach based on Support Vector Machine and Kernel Principal-Component Analysis

现有的垃圾邮件检测算法存在小样本情况下泛化能力差的问题。提出了利用核主成分分析和支持向量机结合进行垃圾邮件检测的方法。与传统算法相比,该方法与邮件异构有很高的检测率、更强的泛化能力和更高的检测效率。实验证明了方法的实用性和高效性。

Current spam filtering has poor generalization ability as given less priority knowledge.The KPCA and SVM are adopted to implement spam filtering detection.Compared with traditional algorithms this method can achieve higher detection rate and better generalization,and decrease time of performance.The experiment on data set shows the effectiveness and excellent performance of the method.