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Provide high blocking rate of over 95% of spam through the learning content
Spam filtering from server basis can be uncomfortable for the specific users. So that we offer the individual Spam DB besides Public Spam DB.
For those users who did not use of Spam DB learned individually, Public Spam DB shall be set specified by each company administrator
Believable users are always classified as normal mail
The user, used Web Mail, shall be received Daily Spam list to prevent of being mislabeled as Spam consistently.
When a mail has coming, analyze the contents of the mail to separate the word.
Mail content: Hi. My name is Hong Kil Dong's Hanbiro.
Separate words compared to the spam DB to check whether the user is received that contains a lot of spam word.
Below is a balance of equilibrium.
Let's consider separate contents and Spam words in the balance.
If 'Hong Kil Dong' is learned by Spam, the balance will be inclined to the left side.
Under this method, the Spam DB will classify as Spam if the rate of Spam possibility is higher than what user set. (.
(The above example is a simple explanation to help you understand but the actual process is made more complex.)
This does not mean the spam processing just includes only certain words. After checking several words, spam processing will enable a more accurate because Spam Possibility is estimated.
We can easily recognize that the effect of filter can be increased depending on the user proceeds normal mail, spam learning to some extent.