Path: utzoo!attcan!uunet!know!sdd.hp.com!uakari.primate.wisc.edu!aplcen!jhunix!ins_atge
From: ins_atge@jhunix.HCF.JHU.EDU (Thomas G Edwards)
Newsgroups: comp.ai.neural-nets
Subject: Re: Neural Nets with continuous valued outputs.
Summary: Localized Receptive Fields
Keywords: opt
Message-ID: <6705@jhunix.HCF.JHU.EDU>
Date: 25 Oct 90 03:18:09 GMT
References: <6327@rnd.GBA.NYU.EDU> <1669@exodus.Eng.Sun.COM>
Organization: The Johns Hopkins University - HCF
Lines: 27

In article <1669@exodus.Eng.Sun.COM> landman@hanami.Eng.Sun.COM (Howard A. Landman) writes:
>In article <6327@rnd.GBA.NYU.EDU> hjohar@rnd.GBA.NYU.EDU (unknown) writes:
>>Does anyone have any references on designing neural-nets that provide
>>continuous valued outputs? I've only seen papers on classifiers.

If you are looking for a network which learns quickly, but has a limited
number of inputs, then Localized Receptive Field Learning 
(Moody and Darken, Proceedings of 1988 Connectionist Models Summer School,
Morgan Kaufmann, 1988) might be for you.  It uses a single
layer of gaussian locally receptive fields.  Each receptive field
has a single output weighting.  These fields can self-organize
themselves across the input space by k-means clustering, and then
can be trained using the LMS rule for supervised learning.  

>Some data that may or may not be of interest.  I wanted to train a
>net to play the game of Go, using no particular assumptions on how
>to do that.  I have 300,000 some odd moves of pro games available,
>so my first thought was to have one training sample per move.

Gack!  That sounds like one big training test.  Go certainly is a game
which requires memory of Go problems as well as global strategies
for a human player to do well.  I still would like to see a large
population of recurrent Go networks playing against each other
using Schmidhuber's reinforcement learning if they loose. But it
would probably take a long, long time.  

-Thomas