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From: guansy@cs.tamu.edu (Sheng-Yih Guan)
Newsgroups: comp.ai.neural-nets
Subject: Back-Propagation
Message-ID: <7010@helios.TAMU.EDU>
Date: 31 Jul 90 17:11:01 GMT
Sender: usenet@helios.TAMU.EDU
Distribution: usa
Organization: Computer Science Department, Texas A&M University
Lines: 37


In article <6985@helios.TAMU.EDU> vu2jok@cs.tamu.edu (Jogen K Pathak) writes:
>We are encountering problems while training the different paradigms , especially
>Back - Propagation paradigm. The training is very time consuming and tedious.
>Can anyone help to choose the training parameters' values that can
>reduce the training sessions. We are working in pattern classification of
>moderate size.e.g 100 input attributes.
>
>Any literature references also will be greatly appreciated.
>
>Jogen and Rajan.

In Fahlman and Lebiere's paper - The Cascade-Correlation Learning Architecture,
they have tried to analyze the resons why backprop learning is so slow and
they have identified two major problems:
	1. the step-size problem, and 
	2. the moving target problem.
In their references, there are several other articles on how to improve the
convergence of Back-Propagation.

Fahlman and Lebiere's paper is available via anonymous ftp.  The procedure
is as follows:
  >ftp cheops.cis.ohio-state.edu
  >cd /pub/neuroprose
  >bin
  >get fahlman.cascor-tr.ps.Z
  >quit

Hope this is helpful.

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