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From: own@castle.ed.ac.uk (O Morgan)
Newsgroups: comp.ai.neural-nets
Subject: application feasibility question
Message-ID: <9659@castle.ed.ac.uk>
Date: 18 Apr 91 12:01:30 GMT
Organization: The Scottish Agricultural College
Lines: 70

Greetings,
I'm about to start work on a project involving neural networks,
and would like some indication of the feasibility of the project,
as I only have abstract notions of NN's rather than practical
experience.  This is therefore to see if I can attract any
helpful comments from this group.

The project involves trying to structure an existing database
into some sort of PDP system.  The reasons for this are to
provide the model with some of the qualities of NN, such as
tolerance to noisy input, associative memory and the ability to
induce the model from a data set.

The database is a socio-economic database of 200 farmers, with up
to 500 items of information relating to each farmer.  (NB: all
references below to fields refer to database fields 8-).

1) My first question is whether a problem of this scale is likely
to succeed or not.  In terms of current work, does this pass as a
small, medium or large application?

2) So far, having browsed a few books, I can see some
possibilities: One is the back propagation type (BP), another the
interactive competition (IAC) model, as they are described in the
Rumelhart and McClelland book.   Hopfield nets also look very
interesting.  As I see it, the relative merits/disadvantages of
each approach are:

The advantage of IAC over BP (as I see it) is that the model
would be a very flexible associative memory.  The user could
provide values for any set of fields, and the model would provide
a guess for all the missing values.  In the case of BP, the Input
values and the Output values are set prior to the learning stage,
so the model can only be used "in one direction".

The disadvantage of IAC (as I've understood it) is that every
database record is explicitly recorded in the model.  Ie: to
incorporate farm X in the model, a "variables" 'farm_X' and
'_farm_X' are created in the representation.  The number of these
ID variables will increase in proportion to the size of the
database.  On the other hand, in BP, the learning process
amalgamates all the records so that with the introduction of a
new record, the size of the model is constant, only the (some)
weights are changed to take account of the new data.

Here I would ask a question:  is there a representation of NN
that can combine both these advantages, namely flexibility at
run-time in the choice of inputs and outputs, and the compactness
of the BP model.  Hopfield nets look as if they might have this
quality, but none of the books I've seen (3) give any idea as to
how you train such networks.

3)  Currently I am reading up various references and intend using
the McClelland+Rumelhart software to experiment options.  Is this
a practical vehicle? Does anyone have any suggestion w.r.t. other
environments that might prove useful.  In this sort of project,
do you eventually end up writing your own code?

4)  The data base I am using contains large amounts of symbolic
information.  Is this sort of data readily processed by Neural
networks (assuming it is given a numeric representation).

Thanks in advance,

Olly  Morgan
--
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 Olly Morgan @ Scottish Agricultural College,  Edinburgh EH9 2HH, Scotland
               Tel: (+44 31) 662 4395          E.Mail: O.Morgan@ed.ac.uk
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