On Wed, Jul 23, 2003 at 05:52:17PM +0100, Theodore Hong wrote:
> Could we perhaps encode all of the samples as binary {0,1} data, but
> train the classifier to return a continuous result? That is, use
> samples like:
>
> ((time=17411,key=0x3a4b,htl=12), target=1) // success
> ((time=17480,key=0x3a4b,htl=15), target=1) // success
> ((time=17485,key=0x3a4c,htl=8), target=1) // success
> ((time=17487,key=0x3a4b,htl=9), target=0) // fail
>
> with the regression mode. The predictor will then return values
> between 0 and 1 which we can interpret as a probability of success.Perhaps, although it is starting to look really convoluted :-/ I would be interested to see a critique of what we have now and in what ways this approach is likely to be better. The main issue with the existing technique that I see is that the "forgetfulness" of the continuous averaging algorithms must be decided manually and arbitrarily, but if I understand this correctly I don't think this is addressing that issue - or is it? Ian. -- Ian Clarke [EMAIL PROTECTED] Coordinator, The Freenet Project http://freenetproject.org/ Founder, Locutus http://locut.us/ Personal Homepage http://locut.us/ian/
pgp00000.pgp
Description: PGP signature
