[pdp-discuss] LEABRA++ ev_nm algorithm

[Randall C. O'Reilly]

I bet this is when there is no output activation -- they are all probably 
equally distant in that case and it just picks the first one..

- Randy

On Tuesday 24 October 2006 14:42, Pradeep Ramanathan wrote:
> Dear List,
>
> I am encountering a problem and couldn't find mention of it in the
> archives.
>
> I have two sets of input patterns which originally derive from picture
> stimuli but have undergone transformation.  Let's refer to these sets of
> events as A1 - A100, and B1 - B100.  I have only one set of output
> patterns, let's call them C1 - C100.  The network gets trained to map both
> A1 and B1 to the output pattern C1, both A2 and B2 to C2, and so on down
> the line.
>
> During testing, I have noticed that whenever the network makes an error in
> identifying an input pattern, for a large number of these errors, it
> reports A1 in the ev_nm field of ClosestEventStat.  By "large number" I
> mean sometimes as many as 60% of the erroneous ev_nm entries are listed as
> A1.  (By the way, most of the other errors are understandable errors, in
> that they can be confused with the correct output event.)
>
> The occurence of such a large number of this kind of error made me suspect
> that under certain circumstances, the network just "looks up" the first
> event in the list of input events and reports that in the ev_nm field,
> hence A1 since it is alphanumerically first in the list of input events.  I
> wondered if when several events are within the same, equally low, distance
> value the network just goes to the list of events in the environment and
> picks the first one, which would be A1.
>
> I tried downloading the source code files for ClosestEventStat, and perused
> the algorithm for reporting ev_nm.  It doesn't look fishy at all, and I
> don't see anything that appears like it might cause this problem.
> (Although, admittedly, I had some trouble following the coding language.)
> I also tried using different functions for calculating the distance,
> thinking that maybe using SSE was not a good idea.  However, I have tried
> all 7 distance functions, and they all demonstrate the same problem.
> (Incidentally, there is nothing unique about the picture that A1 represents
> that makes it highly confusable with a large number of other pictures.)
>
> Any ideas?
>
> Thanks,
> Pradeep
>
>
>
> Pradeep Ramanathan, M.S., M.A., CCC-SLP
> Speech-Language Pathologist/Doctoral Candidate
> University of Minnesota
>
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