<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"><html><head><meta content="text/html;charset=UTF-8" http-equiv="Content-Type"></head><body ><div style="font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 10pt;"><div>Hi Herbert,<br></div><div><br></div><div>I will get back to you after I read up on Neural Networks. <br></div><div><br></div><div>I found this on the web, and it looks interesting and challenging.<br></div><div><br></div><div><br></div><div><a href="https://dai.fmph.uniba.sk/courses/NN/haykin.neural-networks.3ed.2009.pdf" target="_blank">https://dai.fmph.uniba.sk/courses/NN/haykin.neural-networks.3ed.2009.pdf</a><br></div><div><br></div><div><br></div><div><br></div><div><br></div><div>cheers,<br></div><div><br></div><div>t <br></div><br><div data-zbluepencil-ignore="true" style="" class="zmail_extra"><br><div id="Zm-_Id_-Sgn1">---- On Sat, 04 Apr 2020 05:53:13 -0400 <b>Herbert König <herbertkoenig@gmx.net></b> wrote ----<br></div><br><blockquote style="border-left: 1px solid rgb(204, 204, 204); padding-left: 6px; margin: 0px 0px 0px 5px;"><div>Hi,<br> <br> 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%. <br> <br> 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. <br> <br> Also it was not pure SOFM. Lots of fun thou.<br> <br> Anyway gettimothy if you want to give it a try, we can talk and I
can share code.<br> <br> Cheers,<br> <br> Herbert<br> <br> <div class="x_1731772791moz-cite-prefix">Am 03.04.2020 um 23:23 schrieb
gettimothy via Squeak-dev:<br></div><div><br></div><br></div><br><blockquote><div style="font-family: Verdana, Arial, Helvetica, sans-serif;font-size: 10.0pt;"><div>That's interesting and I will read up on it as I get time.<br></div><div><br></div><div>It is an interesting problem isn't it?<br></div><div><br></div><div>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.<br></div><div><br></div><div>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.<br></div><div><br></div><div>That in itself is an interesting problem in itself. How to
efficiently (or not) have 800 objects visit the other 799
objects.<br></div><div>How to store the set of "other close objects" in an object.<br></div><div><br></div><div>Then, as other heuristics of "close" are developed, I can
re-use that <br></div><div><br></div><div><br></div><div>Thanks for your reply!<br></div><br><div class="x_1731772791zmail_extra"><br><div>---- On Fri, 03 Apr 2020 17:12:31 -0400 <b>Stéphane Rollandin <a target="_blank" href="mailto:lecteur@zogotounga.net" class="x_1731772791moz-txt-link-rfc2396E"><lecteur@zogotounga.net></a></b> wrote ----<br></div><br><blockquote style="border-left: 1.0px solid rgb(204,204,204);padding-left: 6.0px;margin: 0.0px 0.0px 0.0px 5.0px;"><div>Just a wild guess: self-organizing maps?<br> <br> <a href="https://en.wikipedia.org/wiki/Self-organizing_map" target="_blank">https://en.wikipedia.org/wiki/Self-organizing_map</a><br> <br> Stef<br> <br> <br></div></blockquote></div><div><br></div></div><br><br><pre class="x_1731772791moz-quote-pre"><br></pre></blockquote></blockquote></div><div><br></div></div><br></body></html>