Hi,

I used this for mapping comments from testers to error causes
(soldering, supplier.....) in production of electronics. I achieved a
recognition rate 80% which was good because the computer looked at 100%
of the comments, humans (= me :-) at 1%.

It's shaky, and slow and depends a lot on deciding of the similarity
criterion and mapping the text input to floats. 1250 trainings samples,
1200 Neurons 280 inputs (known words) each take 15 minutes to train. The
code is from 2007 and I remember running it over night because hardware
was slower back when.

Also it was not pure SOFM. Lots of fun thou.

Anyway gettimothy if you want to give it a try, we can talk and I can
share code.

Cheers,

Herbert

Am 03.04.2020 um 23:23 schrieb gettimothy via Squeak-dev:
> That's interesting and I will read up on it as I get time.
>
> It is an interesting problem isn't it?
>
> What I might try is having each LatinRoot object visit each other one
> and by some heuristic, have them determine if they are "close" to each
> other.
>
> For giggles, I can randomly assign an integer weight to each one, and
> if the absolute value of their difference is within X, then they are
> close.
>
> That in itself is an interesting problem in itself. How to efficiently
> (or not)  have 800 objects visit the other 799 objects.
> How to store the set of "other close objects" in an object.
>
> Then, as other heuristics of "close" are developed, I can re-use that
>
>
> Thanks for your reply!
>
>
> ---- On Fri, 03 Apr 2020 17:12:31 -0400 *Stéphane Rollandin
> <lecteur at zogotounga.net>* wrote ----
>
>     Just a wild guess: self-organizing maps?
>
>     https://en.wikipedia.org/wiki/Self-organizing_map
>
>     Stef
>
>
>
>
>

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