Path: utzoo!utgpu!jarvis.csri.toronto.edu!mailrus!csd4.milw.wisc.edu!uxc!iuvax!pur-ee!pc.ecn.purdue.edu!cb.ecn.purdue.edu!kavuri
From: kavuri@cb.ecn.purdue.edu (Surya N Kavuri )
Newsgroups: comp.ai.neural-nets
Subject: Re: S. Pinker / A. Prince
Message-ID: <772@cb.ecn.purdue.edu>
Date: 23 Mar 89 22:25:56 GMT
References: <2726@sun.soe.clarkson.edu> <1078@Portia.Stanford.EDU>
Organization: Purdue University Engineering Computer Network
Lines: 23

In article <1078@Portia.Stanford.EDU>, kortge@Portia.Stanford.EDU (Chris Kortge) writes:
> In article <2726@sun.soe.clarkson.edu> spam@clutx.clarkson.edu writes:
> >I just read a rather scathing article by Steven Pinker (MIT) and
> >Alan Prince (Brandeis) that tore PDP apart.  Has anyone seen any
> >responses to this article that defend Rumelhart and McClelland?
> 
> If you want to read an article which _attempts_ to tear PDP _in general_
> apart, read the one by Fodor and Pylyshyn in the same book.  It didn't
 ......
 There is another paper with a similar claim.
  "Gradient descent fails to separate" is its title.
            By : M. Brady and R.Raghavan
The paper shows the failure of BP in the case of examples where 
there are no local minima.  They assert (and they could be right as 
such cliams have been "romantic",as Minsky put it!) that least square
 solutons do not minimize the # of misclassifications. 
 They have examples where Perceptron does well while the gradient descent 
 with LSR fails. 
 They conclude that the failure of GD and LSE may be much more wide spread 
than presumed.
                                          SURYA
                                         (FIAT LUX)