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From: smagt@fwi.uva.nl (Patrick van der Smagt)
Newsgroups: comp.ai.neural-nets
Subject: Re: Lippmann paper
Message-ID: <1126@carol.fwi.uva.nl>
Date: 3 Jul 90 09:14:40 GMT
References: <11441@rasp.eng.cam.ac.uk> <1085@carol.fwi.uva.nl>
Sender: news@fwi.uva.nl
Reply-To: smagt@fwi.uva.nl (Patrick van der Smagt)
Organization: FWI, University of Amsterdam
Lines: 59

In article <1085@carol.fwi.uva.nl> smagt@fwi.uva.nl (Patrick van der Smagt) writes:
>In article <11441@rasp.eng.cam.ac.uk> mww@uk.ac.cam.eng writes:
>>I am posting this for someone else so please reply directly to him
>>(mww@uk.ac.cam.eng) or to the network.
>>
>>A few weeks ago there was a summary of papers and books on neural
>>networks. Several people referred to Richard Lippman's well known
>>paper "An introduction to Computing with Neural Nets: IEEE ASSP
>>magazine April 1987"
>>
>>More than one person said that this paper contained errors.  As this
>>is such an influential and often read paper, particularly by those who
>>are new to the field, I wonder if anyone would care to say exactly
>>what these errors are.
>
>Here they are (or, at least, some of them):
>
[4 criticism deleted]
>
>	p. 19  box 7 (not major)
>		the issue of NORMALIZATION of the weights and inputs
>		is left out here.
>	

This one is correct after all, of course.  My previous confusion was
due to the fact that in his 1982 paper, Kohonen uses

                     m (t) + c.x(t)
                      i
	m (t+1) = -------------------
         i        ||m (t) + c.x(t) ||
                     i

whereas a better formula is (as he describes in his 1984 book)

        m (t+1) = m (t) + c(t).[x(t) - m (t)]
         i         i                    i
	
this one moves the weight vector more directly in the desired direction,
and removes the need of normalization.  Lippmann uses the latter method
(as does everyone).


Patrick van der Smagt                                               /\/\
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