pbarrett at hoganassessments.com
Wed Oct 31 10:39:37 EST 2007
> From: rasch-bounces at acer.edu.au
> [mailto:rasch-bounces at acer.edu.au] On Behalf Of Moritz Heene
> Sent: Wednesday, October 31, 2007 8:47 AM
> To: rasch at acer.edu.au
> Subject: Re:[Rasch] Fan
> Hello to all and hello especially to Paul,
> I am not expert in neural networks, I am just a beginner, but
> as far as I can see, neural networks, as efficient as they
> are, have still their problems.
That was some response!
I'm only replying to this bit quickly to note you missed my point!
Neural nets, like all Machine Learning algorithms, are prone to many
"threats to their validity" such as overlearning, lack of
cross-validation, and sometimes it's really difficult to track back as
to how they achieved their outputs (!) ..
But - I wanted to stress the impact this one innovation had in many
areas - unlike the impact of modern test theory which was developed
around the same time - and note why it was not taken up so quickly by
those who utilize test scores for explanatory or predictive purposes.
As to Andrew's point about back-propogation - again, it was the concept
of a network and how such a concept might be causal for human neural
development and activity which took several areas by storm.
Whether or not neural nets survive beyond the next developments in AI is
of course a moot point.
And thanks for the detailed response on the Lexile system Andrew - very
Regards .. Paul
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