As the Open Source adage goes... you itch, you scratch <wink> Who itches enough to implement this? IIRC, we had a long discussion about this a long time ago, and decided that if enough image spam were being misclassified, we might take a look at it this idea. I don't have enough misclassifications to give me an irresistable itch....
Shawn K. Hall wrote: >>I gave this a whirl. Not so good for the samples I pulled >>out of my current spam database. >> >> > >Considering the way SpamBayes works, I think these results are very >acceptable. They're "unlikely" words, so will yield pretty good results >after a few of these ad-images' text are trained. Stuff like "EN_OY", >"_EXl", "blgge__", "_he", "vlbra_orl" will likely apear consistently for >all spam images that are converted, and unlikely to appear in legitimate >images. > >Regards, > >Shawn K. Hall >http://12PointDesign.com/ >http://ReliableAnswers.com/ > > >_______________________________________________ >[email protected] >http://mail.python.org/mailman/listinfo/spambayes >Check the FAQ before asking: http://spambayes.sf.net/faq.html > > > > > > _______________________________________________ [email protected] http://mail.python.org/mailman/listinfo/spambayes Check the FAQ before asking: http://spambayes.sf.net/faq.html
