From: lindahl@cse.arl.utexas.edu (Charlie Lindahl)
Subject: Demos for training & analysis of classification NNs (UTexas@Arlington)
Organization: Computer Science Engineering Univ. of Texas at Arlington
Distribution: comp
Date: Mon, 16 Nov 1992 04:57:47 GMT

All: 

I'm posting demos for an Electrical Engineering professor here 
at UTexas Arlington (Dr. Mike Manry). Below is the description for the
demos related to CLASSIFICATION problems. The ABSTRACT file appears below. 

I've posted on me.uta.edu (anon ftp) under /pub/neural/uta-nn-ee.tar
(this is a TAR file containing ZIP files, and hence should be transferred
IN BINARY MODE. Interested parties can EMAIL me as to why this file wouldn't
COMPRESS). 

Download procedure: 

1) Use ANONYMOUS FTP in BINARY mode. 
2) Use the UNIX TAR program to unpack into two ZIP files. 
3) Use the PKUNZIP PC program to unpack onto the PC. 

PLEASE don't send EMAIL directly to me, or post on this bboard (as 
I don't frequent this regularly); rather, send EMAIL directly to 
Dr. Manry at B496MTM@UTARLG.UTA.EDU. 

Charlie Lindahl
Electrical Engineering Dept
University of Texas at Arlington
EMAIL  :     lindahl@cse.uta.edu
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Neucls.zip  123,824 bytes

Four demo programs are included, for the training and analysis of 
multilayer perceptron (MLP) neural networks for classification. Such 
networks have sigmoidal hidden units sigmoidal output units. The 
enclosed programs are demos in that the training data file must 
have 4 or fewer inputs or 16 inputs, 800 or 16 or fewer training 
patterns, and 4 or fewer outputs. 

Program BP designs MLP classification networks using backpropagation
(BP). Nonstandard features in this program are (1) batching of weight 
changes is allowed, (2) The learning factor is adaptive, and 
(3) the importance of each input feature is calculated. The 
Mean-squared training error (MSE) and classification error percentage 
are calculated and printed for each iteration. 

Program In2 designs MLPs for classification using a fast technique. 
Networks are often better than those from BP and can be designed 
one to two orders of magnitude faster. The classification error 
percentage and MSE are calculated and printed for each iteration.

Program Mod analyzes MLP networks trained via BP or In2. Less 
useful units can be pruned under user control.

Program Watecon converts the weight file from Inmap or BPmap to 
an easily understood formatted form, or vice versa. This should 
allow users to process networks from other neural net design 
packages, and vice versa.
--
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"If Unix is the answer, surely    | Charlie S. Lindahl
we've forgotten the question." -  | lindahl@cse.uta.edu
Anonymous                         | Electrical Engineering Dept
                                  | University of Texas at Arlington
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Disclaimer: If my employer shares these views, I'd be most surprised.