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Wow - I think I underestimated the complexity of what Google is working on here. I had always assumed it was a pretty normal (perhaps distributed) well trained
by barmstrong 16y ago
Wow - I think I underestimated the complexity of what Google is working on here. I had always assumed it was a pretty normal (perhaps distributed) well trained Bayesian algorithm. The article does a good job of highlighting some of the other issues they are facing. Makes me more tolerant of the few spam that do get through....
- larsberg 16y agoI understand the difficulty of the problem, but I still have trouble not getting irritated. Between 5 and 10 affiliate marketing messages make it through to my gmail inbox every day since I moved over in September, despite diligently marking them as spam each time. Are these guys really that smart? I don't read the contents of the body, but it's easy enough for a human to tell just from the sender, subject, and first line that it's spam...
- cdavid 16y agoThe problem of spam is that you do not want many false rejections (much better getting some spam through than losing some valid emails). In your example, you say it is easy to see from sender/subject/first line it is a spam, but only because you "know" it is a spam. Could you guarantee the email cannot be spam with 100 % confidence ?
- deleted 16y ago[deleted]
- bensummers 16y agoWhat's "between 5 and 10" as a percentage of the volume of attempts to send you email? If there were 20 attempts (spam and non-spam) a day, that's a problem, if 2000000, less so.
- ma2rten 16y agoI don't think logistic regression would necessarily be better than naive bayes for this. It is not you can only use naive bayes for only a bag-of-words model. Either are ML algorithms that you can use with any features you want. However I am sure they tested both. Also, I think they should have used a different algorithm for the global model, which gives a better performance at the expense of more training training.