Hi, I have unusual idea for different evaluation of scores or exactly another method of mail SPAMness rating.
Idea is based on using Bayesian theorem. Contrary to usual two-level score computation, where first level is Bayesian classification of word tokens from mail contents together with evaluation of other rules and second level is weighted combining (sum) of results, my idea consists on including matching rules as additional tokens into Bayesian evaluation of that mail. Decision if mail is or not SPAM will be made by testing of hypothesis "is SPAM" on selected level (e.g. 95%). Naturally this method is functional only when Bayes database is initially prefilled with some reasonable values for rule-tokens which is analogy to rule scores. I hope, this method have advantages in - adaptivity when learning is used - simplicity because of only single evaluating method - it is founded on regular statistical test not simplistic comparison of sum of weights Try to weight up my idea please. Thanks for your comments. M. Vancl
