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From: saswss@hotellng.unx.sas.com (Warren Sarle)
Subject: Re: Haykin's RBF Chapter
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Date: Wed, 21 Aug 1996 20:59:07 GMT
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In article <Pine.SUN.3.91.960820132749.21955A-100000@soma.med.utah.edu>, Kai Kuck <kkuck@soma.med.utah.edu> writes:
|> 
|> I am planning to use an RBF network for a classification task and
|> currently read through Haykin's chapter on RBFs (Simon Haykin: Neural
|> Networks - A Comprehensive Foundation, Macmillan Publishing Company,
|> Englewood Cliffs, NJ, 1994: pp. 236ff.). 
|> 
|> For those of you who have read that chapter: Do you understand how this
|> Frechet differential works (pp. 247f.) ?  How about the differential
|> operator P and its adjoint P_star (p. 246, 249)? Do you understand why he
|> is using the Riesz representation (p. 248) ? I am working on Problem 7.10
|> (== derivation of equation (7.86)) and am lost, because I can't answer
|> these questions. 

My math is far too rusty to answer any of those questions either, but I
do not feel the least bit impaired on that account. Those mathematical
derivations obviously did not give Haykin any insight into the practical
use of RBF networks. For example, his section 7.9 comparing RBF networks
and MLPs is extremely shallow; see "How do MLPs compare with RBFs?" in
the Neural Network FAQ, part 2 of 7: Learning, at
ftp://ftp.sas.com/pub/neural/FAQ2.html for a much more thorough
discussion.


-- 

Warren S. Sarle       SAS Institute Inc.   The opinions expressed here
saswss@unx.sas.com    SAS Campus Drive     are mine and not necessarily
(919) 677-8000        Cary, NC 27513, USA  those of SAS Institute.
