Path: utzoo!utgpu!news-server.csri.toronto.edu!rpi!usc!jarthur!nntp-server.caltech.edu!tylerh
From: tylerh@nntp-server.caltech.edu (Tyler R. Holcomb)
Newsgroups: comp.ai.neural-nets
Subject: Re: Radial basis functions
Message-ID: <1991May15.194433.10566@nntp-server.caltech.edu>
Date: 15 May 91 19:44:33 GMT
References: <1991May15.000449.7396@noose.ecn.purdue.edu>
Organization: California Institute of Technology, Pasadena
Lines: 39

kavuri@lips.ecn.purdue.edu (Surya N Kavuri ) writes:


> Radial basis functions are nonmonotonic and are not 
> used for nets using backprop.  How can then one determine
> the centers and radii of these functions ?
> 
>					SURYA KAVURI

1.  See the comprehensive papers by Poggio and Girosi.

 M.I.T  A.I Lab :  A.I. Memo no 1140,  July 1989
                   A.I. Memo no 1167,  April 1990
A note in Science about 1 1/2 years ago (sorry - don't
have reference with me).

2.  The seminal paper by Moody & Darken  "Fast learning in
Networks of Locally-Tuned Processing Units."  Vol 1, pp.
281-294 Neural Computation, 1989.

Remember - backprop just means "gradient descent", which
is exactly how Poggio goes about training his RBF networks.
However, M&D's method, while appearing very simplistic, is
orders of magnitude faster and every bit as effective as "backprop".

I have just completed a comparitive study of three  current
training methods, an extension to M&D, and a new method
I have come up with.  Unfortunatley, this work will not be
ready in printed form until late fall (there is some other
business I need to attend to before I can sit down and do the writing).
Without knowing more about your intended application, I would recommend
the K-means approach in M&D.


-- 
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