new paper available "Combining Sigmoids and RBFs"

[rachida]

The following paper:

Combining Sigmoids and Radial Basis Functions in Evolutive Neural Architectures.

is available at:

    ftp://tirf.inpg.fr/pub/HTML/chentouf/esann96_chentouf.ps.gz

  ABSTRACT
    
    An incremental algorithm for supervised learning of noisy data using two
    layers neural networks with linear output units and a mixture of sigmoids
    and radial basis functions in the hidden layer (2-[S,RBF]NN) is proposed.
    Each time the network has to be extended, we compare different estimations
    of the residual error: the one provided by a sigmoidal unit responding to
    the overall input space, and those provided by a number of RBFs responding
    to localized regions. The unit which provides the best estimation is
    selected and installed in the existing network. The procedure is repeated
    until the error reduces to the noise in the data. Experimental results show
    that the incremental algorithm using 2-[S,RBF]NN is considerably faster
    than the one using only sigmoidal hidden units. It also leads to a less
    complex final network and avoids being trapped in spurious minima.
    
=========
This paper has been accepted for publication in the European Symposium on Artificial Neural Networks, Bruges, Belgium , April, 96. 

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||		Mrs Rachida CHENTOUF					     ||
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