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Review and how it works
SpamBayes was thought as an accurate filtering instrument used to separate unwanted messages(spam) from the mail that user desires (ham).
Before to be a spam filter, the users must train the utility on representative samples of received messages. After it is been trained, the users may let this utility to classify the new mail that arrives. It is best to train its engine on recent messages, because the interests and the nature of what spam looks like change by time.
SpamBayes is working on developing a statistical antispam filter.
The major difference between SpamBayes and another similar projects is the emphasis on testing newer approaches to scoring messages. While most antispam projects are still working with the special Bayes algorithm, it utilizes a new algorithm which was conceived to be a combination of work from Gary Robinson and Tim Peters, and gives not only a 'spam' and 'good email' rating, but also an 'unsure email' rating, for those messages where it can't work out how to rate the message.
What is new?
- 1.0.4 is built with Python 2.3, as all previous versions were.
- If you have three various servers proxied, and they all fail, you will gain only three errors logged per hour, no matter how numerous times the connection fails.
- The connection to the server will now properly fail if the connection may not be established, and this error will be reported only once, rather than the partial success and continual warnings experienced with 1.0.3.
Brief details
The filter proxy will work with most POP3 or IMAP compatible clients.
Will classify incoming messages as 'spam', 'ham' (good, nonspam email) or 'unsure'.
For every message, it compares and computes probabilities to be an unwanted item.
How to install & uninstall SpamBayes - system requirements
Each Windows user utilizing Outlook or each other mail client may operate with this application setup.
Other users must run it from its source. For this they must have a recent edition of Python installed on the pc, edition 2.2 or later.
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