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From: woody@eos.UUCP (Wayne Wood)
Newsgroups: comp.ai.neural-nets
Subject: expectancy
Message-ID: <7035@eos.UUCP>
Date: 14 Aug 90 00:07:17 GMT
Reply-To: woody@eos.UUCP (Wayne Wood)
Organization: NASA Ames Research Center, Calif.
Lines: 33

last resort, open to any suggestions, HELP!!!!

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.

this is vague, but it's all the brief i have.

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?

forgive a novice for his ignorance.

email answers okay if you don't want to clutter the net.

/***   woody   ****************************************************************
*** ...tongue tied and twisted, just an earth bound misfit, I...            ***
*** -- David Gilmour, Pink Floyd                                            ***
****** woody@eos.arc.nasa.gov *** my opinions, like my mind, are my own ******/