A Bayesian Approach to Filtering Junk E-Mail¶
Sahami, M., Dumais, S., Heckerman, D., & Horvitz, E. (1998). A Bayesian Approach to Filtering Junk E-Mail.
Cited by¶
2 citations across 2 artifacts.
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Primes¶
- Conditional Probability
- The conditional-independence simplification is what makes this tractable: the naive-Bayes assumption declares the tokens conditionally independent given the class, factoring an otherwise exponential joint over word combinations into a product of per-word conditionals
This sourceThe naive-Bayes spam filter — assuming tokens conditionally independent given the class to factor the joint and invert via Bayes against the spam prior.
- The conditional-independence simplification is what makes this tractable: the naive-Bayes assumption declares the tokens conditionally independent given the class, factoring an otherwise exponential joint over word combinations into a product of per-word conditionals
Mechanisms¶
- Anti-Spam Rules
- Content is scored against a Bayesian filter trained on known-good and known-bad mail, so a message's features (link density, spoofed headers, sudden fan-out to hundreds of recipients) determine its fate rather than the identity of any one sender.
This sourceTrains a Bayesian junk-mail classifier on features extracted from labeled legitimate and junk email.
- Content is scored against a Bayesian filter trained on known-good and known-bad mail, so a message's features (link density, spoofed headers, sudden fan-out to hundreds of recipients) determine its fate rather than the identity of any one sender.
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