![]() If you don’t have spam around, you could either train SpamSieve as you receive it (probably with lower accuracy at first) or wait briefly until you’ve collected a representative sample. I initially trained SpamSieve with about 600 spam messages from my disgustingly large collection of spam and 600 good messages from my In box (yes, it has been that full, though I’ve beaten it back down into the 300s). Even while I’m waiting for the next version of Eudora to bring SpamSieve’s capabilities to messages I filter out of my In box, I’ve found it extremely worthwhile. Since the communication happens via AppleScript, you can edit the included scripts to customize them further. The integration is relatively seamless, except in Eudora, the current version of which has limitations that restrict SpamSieve to filtering mail that ends up in the In box (not in any other folder). SpamSieve accomplishes this by using the AppleScript capabilities of these email programs to pass information to and from SpamSieve itself. Once it has identified messages as spam, it can mark or move them, and in some of the email programs, your filters can continue to work on the marked messages. SpamSieve works with any number of accounts and filters mail from any source your email program supports. I’m not interested in using Mail, and other spam utilities (such as Matterform Media’s points-based Spamfire utility, which also has many proponents) work outside of your email program, forcing you to scan for false positives in a separate interface). Although it’s not available for Mac OS 9, it does also work with Emailer running in Classic mode. SpamSieve - Along with its implementation of Bayesian filter, I especially appreciate the fact that SpamSieve works inside Eudora, and also inside a number of other email programs, including Entourage, Mailsmith, and PowerMail. There are two main implementations of statistical Bayesian filtering for Mac OS X: Apple’s Mail and Michael Tsai’s SpamSieve, the latter of which I’ve been testing with Eudora 5.2 for some months now. On the positive side, it’s possible that improved algorithms can address these problems. It’s also possible for spammers to pollute your corpus of good and bad words by including lots of good words in a spam message, thus reducing the accuracy of the filter over time. Spam may get through when it’s sufficiently related to your profession for instance, I get spam advertising translation services because of the TidBITS translations. Legitimate mail, such as promotional mailings from companies you’ve bought from in the past, can look a lot like spam at first, and it’s also hard to identify spam messages with minimal text accurately. That’s because you must train a Bayesian filter with a sample of both spam and legitimate messages, and because the Bayesian filter continually examines new messages, it can adapt to the kind of mail you receive, both good and bad.īayesian filters aren’t perfect. Statistical (or Bayesian) filters, which were most popularly described in relation to spam in August of 2002 (and refined last month) by Paul Graham, use a statistical approach that combines the probability that any given word or phrase (implementations vary) to decide if the message is spam.īayesian Filters - The beauty of Bayesian filtering is that it works on the contents of your email, which is probably rather different from mine and anyone else’s. Points-based filters refine that approach, assigning (or removing) points for each criteria matched by a given message they decide if a message is spam or not by how many points that message accumulates. ![]() Put simply, a Boolean filter looks for string of text, and if it’s found, considers the message spam. You can reduce the flow, though, with one of three basic approaches to filtering spam out of your email stream: Boolean filters, points-based filters, and so-called "Bayesian" statistical filters. Having to sort through the increasingly repulsive spam that’s rushing into our electronic mailboxes is becoming more unpleasant than ever. #1648: iPhone passcode thefts, Center Cam improves webcam eye contact, APFS Uncertainty Principle.#1649: More LastPass breach details and 1Password switch, macOS screen saver problem, tvOS 16.3.3 fixes Siri Remote bug.#1650: Cloud storage changes for Box, Dropbox, Google Drive, and OneDrive quirky printing problem.#1651: Dealing with leading zeroes in spreadsheet data, removing ad tracking from ckbk.#1652: OS updates, DPReview shuttered, LucidLink cloud storage.
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