New Paper: A Fast and Robust Learning Algorithm for Feedforward NN

[WEYMAERE@lem.rug.ac.be]

The following paper has appeared in "Neural Networks", Vol. 4, No 3 (1991),
pp 361-369:
 
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                A Fast and Robust Learning Algorithm
                  for Feedforward Neural Networks
 
 
                Nico WEYMAERE and Jean-Pierre MARTENS
            Laboratorium voor Elektronika en Meettechniek
                    Rijksuniversiteit Gent
                        Gent, Belgium
 
 
 
                        ABSTRACT
 
The back-propagation algorithm caused a tremendous break-through in the
application of multilayer perceptrons. However, it has some important drawbacks:
long training times and sensitivity to the presence of local minima.
Another problem is the network topology: the exact number of units in a
particular hidden layer, as well as the number of hidden layers need to be
known in advance. A lot of time is often spent in finding the optimal topology.
In this paper, we consider multilayer networks with one hidden layer of Gaussian
units and an output layer of conventional units. We show that for this kind of
networks, it is possible to perform a fast dimensionality analysis, by analyzing
only a small fraction of the input patterns. Moreover, as a result of this
approach, it is possible to initialize the weights of the network before
starting the back-propagation training. Several classification problems are
taken as examples.
 
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Unfortunately, there is not an electronic version of this paper.
Reprint requests should be sent to :
 
Weymaere Nico
Laboratorium voor Elektronika en Meettechniek
St. Pietersnieuwstraat 41 - B9000 Gent