Path: utzoo!utgpu!news-server.csri.toronto.edu!cs.utexas.edu!yale!mintaka!chaos!rsun
From: rsun@chaos.cs.brandeis.edu (Ron Sun)
Newsgroups: comp.ai.neural-nets
Subject: Re: expectancy
Message-ID: <1990Aug14.195632.15922@chaos.cs.brandeis.edu>
Date: 14 Aug 90 19:56:32 GMT
References: <7035@eos.UUCP>
Organization: Brandeis University Computer Science Dept
Lines: 50

In article <7035@eos.UUCP> woody@eos.UUCP (Wayne Wood) writes:
>my boss wants me to implement a NN that models "expectancy"... i.e.
>when the network receives a given input, it recognizes the input
>and if it is contained in a given "expected set" the output nodes
>of the network receive an additional boost.
>

Based on what you said, it seems that you ought to look at
Steven Grossberg's work, especially his ART1 and ART2 architectures.
(see Carpenter and Grossberg, in Applied OPtics (?) 1986
and in COMPUTER 1988).
				
>this is vague, but it's all the brief i have.

If that is all you have to do, you may want to try some pattern
recognition techniques (more traditional one, some of which are
implementable in NN).

>ihave read Rummelhart et al. from cover(s) to cover(s) and haven't
>found a model that would appear to handle this.


>all input vectors are the same length with no 'flag' to indicate if the
>pattern is in the expected set or not.  he wants the expectancy effect
>to be determined during learning of the pattern set (entire set, not
>just expected set).
>
>it appears to me (in my ignorance) that he wants the weights to be
>selectively modified for patterns representing the expected set so
>they achieve higher calculated outputs.  i don't see how this can
>be done without disrupting previously learned patterns.
>
>can anybody direct me to articles or discussions of architectures that
>can handle this?
>
>
I don't understand what you are saying here --- you have to tell
the net what is your expected set.





Ron Sun
Brandeis University
Computer Science
Waltham, MA 02254
rsun@cs.brandeis.edu

.