You will still need to train SpamSieve with your own good messages, as accuracy will be much better if you train SpamSieve with your own spam rather than the seed spam. This command will add about 1400 spam messages (taken from a public archive) to the corpus in order to jump-start spam recognition. If you don’t get that much spam but you still want to use SpamSieve, you can import Seed Spam. As the instructions say, “For best results, you should train it with at least 600 messages, with 65% of them being spam for the best results.†Now just consider how much email you get, how much is spam, and in all reality, it really won’t take that long for you to get the corpus up to that level. The more messages you train SpamSieve with, the better its accuracy will be. You do this by simply selecting some messages and telling SpamSieve to add them to its corpus. Once you’ve set up SpamSieve with your email program, you have to teach it what is good mail and what is spam. ![]() SpamSieve is designed to work with Mac OS 10.2.6 through 10.3.2 and works with Apple Mail, eMailer, Entourage 9.0.1 and later, Eudora 5.2 and later, Mailsmith 1.5 and later, and PowerMail 4.0 and later. Select the email program that you use and follow the instructions on how to set up SpamSieve for your email program. Once you’ve done that, you just follow the instructions in the SpamSieve Manual that is installed by just selecting it from the Help menu. Installing SpamSieve is fast and easy you just double-click on the SpamSieve.dmg file to mount the SpamSieve disk image, then you just simply move the SpamSieve application into your Applications folder. (False positives are good emails that wind up being mistakenly identified as spam) Because the Bayesian system builds up a list of what is good and bad based upon what you tell it and what it sees in your email program, it works better then the old content-based-scoring spam filtering systems. Bayesian filters learn to differentiate between good mail and spam that results in an adaptive process that produces very few false positives. ![]() Bayesian filters, without getting really technical as a lot of people will when talking Bayesian filters, means that they calculate the chances that a message is spam based upon the contents of the message. It checks your address book so that it won’t prohibit “good†mail from getting through to you and only marks “bad†mail as spam or places them in a Spam folder so that you can check and verify that the analysis was correct. SpamSieve, developed by Michael Tsai, utilizes Bayesian spam filters to learn what spam looks like and then block it from getting into your in-box. Into the picture came SpamSieve and how things have changed. When one has to set up multiple folders of spam addresses to keep them from inundating my mailboxes constantly, it’s time to find something that really works without constantly having to make adjustments and enter data. The one major problem that I’ve had with my email program, PowerMail, is that the spam filters just don’t do the job as well as I would like them to.
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