Hi Guys, Sorry for reviving an old topic, but I would like to get more information about the geometric mean (GM) method devised by G. Robinson. The earliest reference I could find is his article:
http://radio.weblogs.com/0101454/stories/2002/09/16/spamDetection.html Where he mentions that the geometric mean is consistent with Fisher's method, but no more details are given: "The connection would take too much time to explain here, but it is a great justification for why this should work really well." I understant GM is now obsolete and has been replaced in favor of Fisher's, but I am interested in the derivation of the method (or at least the basic intuition behind it) and its relationship with Fisher's. I could not find any explanation in the archives of SpamBayes. The most similar thing to Robinson's GM I could find is "logarithmic opinion pool" with weights wi=1/n and constant k=1, but I have no idea whether the two things are related. Any explanation about the derivation of the GM method will be greatly appreciated. Thanks in advance P.S. I sent this same question to the [Spambayes - General] list and somebody suggested me to write here. -- View this message in context: http://www.nabble.com/Geometric-Mean-calculation-tp22319020p22319020.html Sent from the Spambayes - Development mailing list archive at Nabble.com. _______________________________________________ spambayes-dev mailing list spambayes-dev@python.org http://mail.python.org/mailman/listinfo/spambayes-dev
