preprint: noisy regression and classification

[Michael Biehl]

FTP-host:       ftp.physik.uni-wuerzburg.de 
FTP-filename:   /pub/preprint/1999/WUE-ITP-99-015.ps.gz

The following manuscript is now available via anonymous ftp,
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   http://theorie.physik.uni-wuerzburg.de/~biehl
   

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Ref.  WUE-ITP-99-015

Noisy regression and classification with continuous 
mulitlayer networks       
 
M. Ahr, M. Biehl, and R. Urbanczik

                     ABSTRACT

We investigate zero temperature Gibbs learning for two classes
of unrealizable rules which play an important role in practical
applications of multilayer neural networks with differentiable
activation functions: classification problems and noisy regression
problems. Considering one step of replica symmetry breaking, we
surprisingly find, that for sufficiently large training sets the 
stable state is replica symmetric even though the target rule
is unrealizable. The common practice to approximate a classifiction 
scheme by a continuous regression problem is demonstrated to 
increase the number of examples needed for succesful training
drastically. 
 

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Retrieval procedure via anonymous ftp: 

     unix> ftp  ftp.physik.uni-wuerzburg.de
     Name: anonymous  Password: {your e-mail address}
     ftp>  cd pub/preprint/1999
     ftp>  binary
     ftp>  get WUE-ITP-99-015-ps.gz               (*)   
     ftp>  quit
     unix> gunzip WUE-ITP-98-015.ps.gz
e.g. unix> lp -odouble WUE-ITP-99-015.ps                        

(*) can be replaced by "get WUE-ITP-99-015.ps". The file will then
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___________________________________________________________________ 

-- 

Michael Biehl     
Institut fuer Theoretische Physik              
Julius-Maximilians-Universitaet Wuerzburg
Am Hubland
D-97074 Wuerzburg 

email:     biehl at physik.uni-wuerzburg.de
www:       http://theorie.physik.uni-wuerzburg.de/~biehl 
Tel.:      (+49) (0)931 888 5865   
             "      "    "  5131
Fax :      (+49) (0)931 888 5141