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Two Approaches on Implementation of CBR and CRM Technologies to the Spam Filtering Problem

机译:实现垃圾邮件过滤问题的CBR和CRM技术的两种方法

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摘要

Recently the number of undesirable messages coming to e-mail has strongly increased. As spam has changeable character the anti-spam systems should be trainable and dynamical. The machine learning technology is successfully applied in a filtration of e-mail from undesirable messages for a long time. In this paper it is offered to apply Case Based Reasoning technology to a spam filtering problem. The possibility of continuous updating of spam templates base on the bases of which new coming spam messages are compared, will raise efficiency of a filtration. Changing a combination of conditions it is possible to construct flexible filtration system adapted for different users or corporations. Also in this paper it is considered the second approach as implementation of CRM technology to spam filtration which is not applied to this area yet.
机译:最近,发送给电子邮件的不良消息的数量已大大增加。由于垃圾邮件具有可变的特征,因此反垃圾邮件系统应该是可训练的且动态的。长时间以来,机器学习技术已成功应用于从不良消息中过滤电子邮件。本文提出将基于案例的推理技术应用于垃圾邮件过滤问题。根据新来的垃圾邮件消息进行比较的基础上,连续更新垃圾邮件模板的可能性将提高过滤效率。通过改变条件的组合,可以构建适用于不同用户或公司的灵活的过滤系统。此外,在本文中,第二种方法被认为是CRM技术对垃圾邮件过滤的实施方法,目前尚未应用到该领域。

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