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You can identify the spam because it's your inbox; you know what your goodmail is supposed to look like. For that reason you can use locally-trained naive Bayes
by denton-scratch 4y ago
You can identify the spam because it's your inbox; you know what your goodmail is supposed to look like. For that reason you can use locally-trained naive Bayes on the client, and get good recognition (you don't need ML).
Honestly, the best filter in my experience is Spamhaus Zen; it spots most spam with very few FPs. It's an IP blocklist.
- Avamander 4y agoIf you use something like rspamd, then doing ML is not terribly hard. Then it becomes a problem of providing a proper teaching set. Combining RBL's (be it URI, IP or domain WL's or BL's) will help with that. In the end it's still a hard problem, so takes a bunch of work.