On Nov 22, 2013 4:15 PM, "Marek Otahal" <[email protected]> wrote:
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> On Sat, Nov 23, 2013 at 12:25 AM, Scott Purdy <[email protected]> wrote:
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>> For some reason having trouble wrapping my head around that but I
believe that all makes sense. I think you are saying that if your input
space was varied enough that you saturate the SP, then the number of
patterns you can represent is inversely proportional to the noise
tolerance. The caveat to this is that even if you end up with a different
SP representation because of a small amount of noise, the representation
will still be very semantically similar to the previous. In fact, you most
likely only have one or two columns that are different so the higher levels
will see a very similar pattern.
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> Yes, I wouldn't say it better ;) Thanks for rewording to make sense. (I
was just talking noise at the output layer, where 10% noise would make 100
from a 1000 bits, that could easily change meaning if you're running on 20
ON bits).
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> Btw, could this be an argument for reconstruction against classification?
When I mess with 10% of the SDR, you'll train on a wrong pattern, if we use
"back-propagation" the random-bits will have low permanences, so will
reduce on the way down.

That would require hierarchy and feedback. Reconstruction doesn't affect
active or predicted cells.

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>>>  PS:
>>> Is there a (lower bound) limit on the number of columns in SP? So would
a 20 col SP work? That way, I could achieve the (20 choose 3) and reach the
state of info-full SP.
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>> The theory relies on large numbers. Subutai's CLA quiz covers it very
thoroughly. In your 20 choose 3 example, you lose fault
tolerance/subsampling at higher levels, the ability to represent many
different patterns (only 1140), and the ability to represent many
simultaneous patterns.
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> I know, the fault tolerance would be a problem. Lost ability to represent
huge num. of patterns is what I want. Otherwise it's out of reach for
anybody to experiment with a SP in nearly saturated state (input pattern
wise).
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> --
> Marek Otahal :o)
>
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