I tried Kohonen for classifying timbres once and
though I'm certainly not an expert on it Ben I seem to remember 
that it works within a threshold value. Input vector is just a _point_
in an n-dim space with a "sphere of influence" around it. If an output
vector is within this threshold area then the more input stimulus you
present that match a vector falling inside the threshold the more
that output is weighted. Eventually that output vector will fire with 
probability= 1 (absolutely certain). Further input vectors, no matter
how many you give, won't affect output vectors outside the threshold.
It's a long time ago, so excuse me if I'm talking rubbish.




On Fri, 12 Oct 2007 11:27:15 -0700
"B. Bogart" <[EMAIL PROTECTED]> wrote:

> Hey all, Johannes in particular,
> 
> According to Medler:
> http://neuron-ai.tuke.sk/NCS/VOL1/P3_html/node28.html#SECTION00045000000000000000
> 
> "...Consequently, more frequently occurring stimuli will be represented
> by larger areas in the map than infrequently occurring stimuli."
> 
> Which leads me to believe if you present a SOM with the same pattern,
> the number of nodes that fire should increase proportional to the
> frequency of the pattern.
> 
> Problem is I can make ann_som do that, not matter what rules I specify
> (using the regular decreasing learning rate).
> 
> If I send the same pattern to the som 10000 times, only one node will
> fire for that duration.
> 
> Am I misinterpreting the text?
> 
> Also by default does ann_som using time-seeded random numbers or regular
> random numbers? I ask because it appears the same node always wins at
> the start... (the biggest number).
> 
> Is there a FANN mailinglist for the discussion of such issues?
> 
> (http://leenissen.dk/fann/ is timing out for me.)
> 
> 
> Thanks,
> B.
> 
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Use the source

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