Ken Seehof wrote: > Excellent idea, Dan. That's conveniently sidesteps the most difficult > issue: getting the neural network to actually come up with linguistic > rules. Once an intelligent human specifies the set of rules, the neural > net should have no difficulty coming up with an optimal non-linear > function of pre-processed features (i.e. the "rules") to identify spam. > Analysis of the weights after training will help remove rules that turn > out to be irrelevant. Wouldn't decision tree or other rule inference algorithms be more accurate than neural networks for that kind of machine learning ? Moreover, neural nets are "black box" : you do not get a logical rule set (that may be edited by humans or exchanged) but an ugly matrix of floats... -- Romuald Texier
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