Received at my Penn e-mail address today: spam with the subject line "Increase girth and inches in one easy step!"
Presumably "inches" means "inches of length". (I'll leave it to you to figure out what length they're trying to increase.)
But I couldn't help but notice that they really should have said "girth" and "length", naming both the quantities. (Or "inches" and "inches", naming both the dimensions, but that would be silly and redundant.)
Showing posts with label spam. Show all posts
Showing posts with label spam. Show all posts
10 April 2008
29 October 2007
Bayesian gender spam
A Bayesian explanation of how to determine the gender of a person on the street (from observable cues), by Meep.
It's rather similar to Bayesian spam filtering (Paul Graham, see also here). The major difference is that one can generally assume that most e-mail is spam, whereas one cannot assume that most people are of one or the other of the two canonical genders.
In the spam filtering case, though, it doesn't seem that the prior probability that a message is spam matters; Graham claims that most e-mails are either very likely or very unlikely to be spam. But there are probably more words in an e-mail than there are easily observable cues to a person's gender; it seems much more likely to get, say, that a person has a 60% probability of being male than that an e-mail has a 60% probability of being spam.
Also, it's a lot easier to collect the necessary for spam filtering than for gender determination.
It's rather similar to Bayesian spam filtering (Paul Graham, see also here). The major difference is that one can generally assume that most e-mail is spam, whereas one cannot assume that most people are of one or the other of the two canonical genders.
In the spam filtering case, though, it doesn't seem that the prior probability that a message is spam matters; Graham claims that most e-mails are either very likely or very unlikely to be spam. But there are probably more words in an e-mail than there are easily observable cues to a person's gender; it seems much more likely to get, say, that a person has a 60% probability of being male than that an e-mail has a 60% probability of being spam.
Also, it's a lot easier to collect the necessary for spam filtering than for gender determination.
Subscribe to:
Posts (Atom)
