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From: smagt@fwi.uva.nl (Patrick van der Smagt)
Newsgroups: comp.ai.neural-nets
Subject: discussion: learn vs. store?
Keywords: learn, store, Hebb rule, two pennies
Message-ID: <1233@carol.fwi.uva.nl>
Date: 6 Sep 90 16:26:48 GMT
Sender: news@fwi.uva.nl
Lines: 35

I am not quite sure about this: learn vs. store.  I am inclined
to classify algorithms such as back-propagation (i.e., optimum-
SEEKING algorithms) as learning algorithms.  Whereas when one considers
the methods to "teach" a relaxation model such as what is
commonly called the Hopfield network (typically, the Hebb rule),
the term "learning rule" is too strong I think.  But what about
an iterative (though not really optimum-seeking) algorithm such as
Bruce et al.'s?  Is it generally agreed upon that this is STORE
as opposed to LEARN?

Reference:
A. D. Bruce,  A. Canning, B. Forrest, E. Gardner, D. J.  Wallace, 
"Learning and memory properties in fully connected networks",
AIP Conference Proceedings 151, Neural Networks for Computing,
J. S. Denker (ed.), Snowbird, Utah, 1986, pp. 65--70.


Patrick van der Smagt                                               /\/\
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