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[solla@homxb.att.com]

   The following preprint is available. If you want a copy, please 
   send your request to:

                      Sara A. Solla 
                      AT&T Bell Laboratories, Rm 4G-336
                      Crawfords Corner Road
                      Holmdel, NJ 07733

                      solla at homxb.att.com



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           ACCELERATED LEARNING IN LAYERED NEURAL NETWORKS        


                         Sara A. Solla  
            AT&T Bell Laboratories, Holmdel NJ 07733  

                Esther Levin and Michael Fleisher      
    Technion Israel Institute of Technology, Haifa 32000, Israel    



   
                            ABSTRACT 

Learning in layered neural networks is posed as the minimization of 
an error function defined over the training set. A probabilistic 
interpretation of the target activities suggests the use of relative  
entropy as an error measure. We investigate the merits of using this 
error function over the traditional quadratic function for gradient 
descent learning. Comparative numerical simulations for the contiguity 
problem show marked reductions in learning times. This improvement is 
explained in terms of the characteristic roughness of the landscape 
defined by the error function in configuration space. 



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