Wow Subutai, Thanks! I believe that was the first time swarming actually "clicked" for me...
David On Fri, Aug 22, 2014 at 11:27 AM, Subutai Ahmad <[email protected]> wrote: > On Fri, Aug 22, 2014 at 3:28 AM, Cavan Day-Lewis < > [email protected]> wrote: > >> Classification: NPL Management Ltd - Commercial >> >> Ø In fact, the more correlated a field is with the predicted field, the >> more likely that it is >> >> Ø unnecessary and will be left out. (This is the opposite of what >> happens in most machine >> >> Ø learning applications.) >> >> >> >> This is very interesting, why is this so? >> >> >> > It's because of the problem formulation with streaming data. Suppose you > have two variables, x and y and suppose x is the predicted field. In the > OPF, the HTM is solving the following problem. Given: > > : > x(t-2) y(t-2) > x(t-1) y(t-1) > x(t) y(t) > > Predict: x(t+1) > > Because of sequence learning, the HTM is good at exploiting information > from time t and past time stamps. And it has access to all that data. If > y(t) and x(t) are perfectly correlated, y(t) adds no additional value over > x(t). The important thing is to have temporal correlation between y(t) and > x(t+1) that is above and beyond the correlation between x(t) and x(t+1). > With fast moving data streams, I've found that temperature often changes so > slowly that the effects of including it are minimal because the effects are > already contained within x(t). > > In static machine learning problems, the problem formulation is usually: > given y(t) (and possibly other variables from time t), predict x(t). It's > a very different formulation. > > BTW, I agree with Matt's comment - you don't need to swarm over all the > data. Just swarm over a couple of thousand records, then use the resulting > parameters on the full dataset. > > --Subutai > > _______________________________________________ > nupic mailing list > [email protected] > http://lists.numenta.org/mailman/listinfo/nupic_lists.numenta.org > >
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