但真正的垃圾邮件过滤，不仅出。
概述了垃圾邮件过滤研究的现状。
一种有效的垃圾邮件过滤新方法。
安装一个垃圾邮件过滤器。
先进的垃圾邮件过滤，加强防晒最垃圾早日停止。
Advanced spam filtering, enhanced SPF stops most spam early.
由此一来，垃圾邮件过滤软件的生产商也会削弱这方面的业务。
So the companies doing spam filters would be killing that part of their own business.
在如今的电子邮件时代中，垃圾邮件过滤解决方案是非常重要的。
Spam filtering solutions are a necessary evil in today's e-mail climate.
在垃圾邮件过滤中使用的白名单和黑名单的基本原理是非常简单的。
The fundamentals of the white and blacklists used in spam filtering are very simple.
它可以用于垃圾邮件过滤，或在你自己的外壳脚本中使用。
It can be used in spam filtering, or in your own shell SCR ipts to use.
胶订先生曾不经意“泛滥”垃圾邮件过滤的余心控制面板。
Bookbinder had unwittingly "unchecked" the spam filter in the MS Mind control panel.
通过为垃圾邮件过滤方法的不同部分给出不同的得分，您最终可以提供一个有效的得分。
By giving different scores to different parts of the spam filtering methods, you can ultimately provide an effective score.
由于缺乏标准的中文邮件样本集，无法评测不同垃圾邮件过滤系统的性能。
Lack of standard Chinese mail dataset, the performance of various Spam-filter systems can't be evaluated.
使用垃圾邮件过滤解决方案可以捕获大量的垃圾邮件，但是很难实现100%的可靠性。
Using a spam filtering solution catches a significant amount of spam, but many fail to reach 100 percent reliability.
文本倾向性识别在垃圾邮件过滤、信息安全和自动文摘等领域都有广泛的应用。
At the fields of spam filtering, information security and Automatic Abstracting, text orientation identification is used widely.
文本倾向性识别在垃圾邮件过滤、信息安全和自动文摘等领域都有广泛的应用。
At the fields of spam filtering, information security and automatic summarizations, text orientation identification is used widely.
为了更好地过滤垃圾邮件，我们展开了中文反垃圾邮件过滤系统的开发与研究。
The the Chinese anti-spam filter system is developed and researched for the sake of filtering the spam effectively.
本论文课题的主要目标是探索一种具体的垃圾邮件过滤模型，实现并测试该模型。
This main goal of this paper is to explore a specific spam filtering model, implement and test it.
所以，语义网格在垃圾邮件过滤领域的研究应用具有很强的理论意义和应用价值。
Therefore, the research on spam filtering based on semantic grid has a strong theoretical significance and application value.
说明：是一个很好的邮件过滤器，希望对研究垃圾邮件过滤方面的朋友有所帮助。
It is a good spam filter, and I hope that it is helpful to you who is studying the spam filter.
在分析传统垃圾邮件过滤技术的基础上，提出了一种基于用户反馈的反垃圾邮件技术。
On the basis of analyzing the traditional spam filtering techniques, this paper presents a novel anti-spam technique based on the users feedback.
主要介绍如何建立最大熵模型以及应用最大熵模型实现垃圾邮件过滤的基本原理和方法。
Mainly introduce how to create the Maximum Entropy Modeling and the principle and method of realizing Spam Email Filtering based on the Maximum Entropy Modeling.
主要介绍如何建立最大熵模型以及应用最大熵模型实现垃圾邮件过滤的基本原理和方法。
Mainly introduced how to Create the Maximum Entropy Modeling and the principle and method of Realizing Spam Email Filtering based the Maximum Entropy Modeling.
然而，ICF继续说，尽管垃圾邮件过滤有效的减少能源浪费，但直接打击它的源头要好更多。
However, the ICF goes on to say that while spam filtering is effective in reducing energy waste, fighting it at the source is far better.
Bayesian垃圾邮件过滤技术通过将“垃圾邮件”单词与“正常”单词进行比较来实现过滤任务。
The Bayesian spam filtering technique works by comparing "spam" words with "normal" words.
然后从内容过滤，接入过滤，行为过滤这三方面对垃圾邮件过滤技术的研究现状进行全面综述；
Secondly, all kinds of Anti-Spam technology including content filtering access-filtering behavior-filtering are introduced.
本文以增强语义信息，提高搜索的查全率和查准率为目标，设计了基于语义网格的垃圾邮件过滤模块。
To enhance recall ratio and precision ratio of information retrieval, the paper designs the model of spam filtering based on semantic grid.
最后请记住，即使在现有的垃圾邮件过滤解决方案中组合使用了所有的这些解决方案，可能仍然无法彻底解决问题。
Finally, bear in mind that even employing all these solutions in combination with an existing spam filtering solution might not resolve the problem entirely.
大多数垃圾邮件过滤自动地集成到一个单独的文件夹涉嫌滥发电子邮件给你就你筹委会稍后检讨或删除。
Most integrated spam filters automatically place suspected junk email into a separate folder on your PC for you to review or delete later on.
您也想避免用留言：你好，喜、帮助新增或收件人的姓名或电子邮件地址作为这样才能引发垃圾邮件过滤。
You also want to avoid using the words: hello, hi, help, new or the recipient's name ore-mail address as doing so can trigger spam filters.
多数反垃圾邮件过滤要训练，但是所以你要偶尔告诉垃圾邮件过滤，这并不是一件不经意放进垃圾邮件夹。
Most anti-spam filters need to be trained, however, so you'll have to occasionally tell the filter that something is NOT spam that it inadvertently put into the Junk Mail folder.
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