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  1. So lets get started in building a spam filter on a publicly available mail corpus.[1]
  2. Bayesian algorithms were used for email filtering as early as 1996.[2]
  3. Users can also install separate email filtering programs.[2]
  4. The spam filter is usually unable to analyze this picture, which would contain the sensitive words like «Viagra».[2]
  5. The spam filter design uses a single hidden layer with many fewer units than the input layer.[3]
  6. The SpamAssassin tool is a freely-available Perl-based spam filter that combines hand-crafted features using a perceptron.[3]
  7. In this article, we’re going to develop a simple spam filter in node.js using a machine learning technique named “Naive Bayes”.[4]
  8. We’ll now write the spam filter.[4]
  9. If you’ve made it this far: congratulations on building your first machine learning based spam filter![4]
  10. An ML-based spam filter can learn in several ways, but it has to be trained by using a large amount of data from already recognised spam emails and identifying patterns.[5]
  11. The best possible spam filter at the moment still relies on human beings and machines working together, rather than in isolation.[5]
  12. We commonly use the rule-based approach when we share the same spam filter between multiple users.[6]
  13. Enhancing the Naive Bayes Spam Filter Through Intelligent Text Modification Detection.[7]
  14. Thus, a spam filter could be trained to check that some other features are consistent with the IP address feature.[8]
  15. A spam filter can scan the headers to see what the HELO command 200 said.[8]
  16. Hence, features relating to at least a portion of the tag pattern can be used in training a spam filter.[8]

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