Ian Clarke <[EMAIL PROTECTED]> wrote:
> On Tue, Jul 22, 2003 at 04:50:48PM -0700, Rudi Cilibrasi wrote:
> > My proposal, here, is to train a pair of SVM's per remote host
> > with only the data relevant to that host. The first SVM will
> > predict if the request will succeed or fail. If the first
> > indicates success, then the second will be called upon to predict
> > how long the request will take.
>
> Hmmm, the reality is that I don't think a binary prediction will
> really be all that useful, since the sample data is likely to be
> quite widely distributed - is the algorithm not capable of providing
> the relative probabilities of success and failure?
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.
theo
_______________________________________________
devl mailing list
[EMAIL PROTECTED]
http://hawk.freenetproject.org:8080/cgi-bin/mailman/listinfo/devl
