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RESEARCH ON ADVANCED FILTERING ALGORITHM FOR ANTI-SPAM BASED ON A BAYESIAN CLASSIFICATION MODEL

机译:基于贝叶斯分类模型的反垃圾邮件高级过滤算法研究

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Currently, naive Bayesian algorithm for Email filtering has been accepted widely and been applied in many commercial Email systems. However, facing with the still severe situation of spam Email, people are realizing gradually that depending on such a simple algorithm can't meet the practical needs for anti-spam. On the other hand, Bayesian network, as an important branch in the knowledge discovery area, has been researched and explored for a long period. The question of Email filter can be projected to a Bayesian network model. By doing so and utilizing Bayesian parameter estimation for some key nodes in the model, we can accomplish Email classification and discrimination based on probabilistic conditions. According to the experimental results, new algorithm has much higher rate to converge and more stable than the naive Bayesian
机译:目前,用于电子邮件过滤的天真贝叶斯算法已被广泛接受并应用于许多商业电子邮件系统中。然而,面对垃圾邮件的仍然是严峻的情况,人们逐渐意识到,根据这样的简单算法无法满足反垃圾邮件的实用需求。另一方面,贝叶斯网络是知识发现地区的重要分支,已经在很长一段时间内得到了研究和探索。电子邮件过滤器的问题可以投影到贝叶斯网络模型。通过这样做并利用模型中某些关键节点的贝叶斯参数估计,我们可以根据概率条件完成电子邮件分类和歧视。根据实验结果,新算法的收敛速度要较高,比天真贝叶斯更稳定

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