스팸 필터

노트

위키데이터

• ID : Q2141106

말뭉치

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

메타데이터

위키데이터

• ID : Q2141106