ph.d. thesis available: NN for Signal Processing

[Claus Svarer]

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The following ph.d. thesis is now available by anonymous ftp:


               Neural Networks for Signal Processing
         
                            Claus Svarer
                  CONNECT, Electronics Institute B349
                   Technical University of Denmark
                      DK-2800 Lyngby Denmark
                         csvarer at ei.dtu.dk
  

The main themes of the thesis are:

     Optimization of neural network architectures by
     pruning of parameters.  Pruning is based on Optimal Brain Damage 
     estimates of which parameters induce the least increase 
     the cost-function when removed.
      
     Selection of the optimal architecture in a family of pruned networks
     based on a generalization error estimate (Akaike's Final Prediction 
     Error estimate). 

     Methods for on-line tuning of the different parameters of the
     network optimization algorithms.  The gradient-descent parameter is
     tuned using a second order Gauss-Newton method, while the weight-decay
     parameters are tuned to minimize the generalization ability 
     (FPE estimate) of the network.

The methods proposed for network optimization are all examined by 
experiments. Examples are selected from the areas of classification,
time-series prediction and non-linear modeling.



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 | Claus Svarer                          | e-mail : csvarer at ei.dtu.dk         |
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