Hi John, HTM algorithms are really best suited for streaming, temporal data sequences. This is how humans operate as well.
You can sometimes contort HTM's to learn a static function but it's trying to fit a square peg into a round hole. In my experience it doesn't usually do any better (and often does worse) than simple conventional techniques. --Subutai On Wed, Oct 1, 2014 at 10:43 AM, John Blackburn <[email protected]> wrote: > Thanks, Subutai. Yes, this does help! One question: traditional neural > nets can be thought of as a function where you have input and output and > you train it (supervised learning) to give the right output for given > input. These NNs are not sequence based. I understand HTM is more advanced > than this, but can it behave like a function as well? Eg, if tilt(t) = > F[temp(t)] can HTM find this function? (current value from current value) > or can it only discover: > > tilt(t) = F[tilt(t-1), tilt(t-2),..., temp(t-1), temp(t-2)..] ? > (ie current value from past values) > > > On Tue, Sep 30, 2014 at 7:28 PM, Subutai Ahmad <[email protected]> > wrote: > >> On Tue, Sep 30, 2014 at 3:47 AM, John Blackburn < >> [email protected]> wrote: >> >>> What I want it to do is primarily predict tilt(t+1) from temperature(t), >>> I do not want it to predict temperature(t+1) from temperature(t) as this >>> will not work well with the data. (unlike Hotgym, the temperature cannot be >>> predicted from previous temperatures) >>> >> >> John, >> >> I'm not sure if this is a terminology thing, but I want to clarify the >> above statement to make sure we're on the same page. If tilt is the >> predicted field, NuPIC will always try to predict tilt(t+1) from tilt(t), >> tilt(t-1), title(t-2), ..., etc. It will only include temperature(t) if it >> helps. Note that if temperature(t) is completely correlated with tilt(t), >> adding temperature(t) will not help, because the information is already >> available in tilt(t). >> >> So, the swarm does not look for correlations between fields. It only >> looks to see which fields add value over and above the past values of the >> predicted field. >> >> My apologies if this was already obvious to you! This use of past values >> is a very big difference from traditional batch analytics, so just wanted >> to be clear. >> >> --Subutai >> >> >
