Thanks for the jab,
How do you interpret the quote from Medler? My understanding is also that only one node can win... So it the same node would fire with the same input, no matter what. I then have no idea what Medler means.
Perhaps a nice ann_som feature would be to send out the BMU index and its weight.
.b.
Andy Farnell wrote:
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" bbogart@goto10.org wrote:
Hey all, Johannes in particular,
According to Medler: http://neuron-ai.tuke.sk/NCS/VOL1/P3_html/node28.html#SECTION000450000000000...
"...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.
PD-list@iem.at mailing list UNSUBSCRIBE and account-management -> http://lists.puredata.info/listinfo/pd-list