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NeuroCOLT
Technical Report NC-TR-95-041
A
General Feedforward Neural Network Model
Cedric
GEGOUT, Bernard GIRAU and Fabrice ROSSI
Ecole Normale Sup\'erieure de Lyon,
Ecole Normale Sup\'erieur de Paris
THOMSON-CSF/SDC/DPR/R4, Bagneux
France
Abstract
In this paper, we generalize a model proposed by L\'eon Bottou
and Patrick Gallinari. This model gives a general mathematical description
of feedforward neural networks, for which standard models, such as
Multi-Layer Perceptrons or Radial Basis Function based neural networks,
are only particular cases. A generalized back-propagation, which gives
an efficient way to compute the differential of the function computed
by the neural network, is introduced and carefully proved. We also
introduce an evaluation of the theoretical time needed to compute
the differential with the help of both direct algorithm and back-propagation.
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