  Bootstrap techniques (also called resampling computation techniques) have
introduced new advances in modeling and model evaluation. Using resampling
methods to construct a series of new samples which are based on the original
data set, allows to estimate the stability of the parameters. Properties such
as convergence and asymptotic normality can be checked for any particular
observed data set. In most cases, the statistics computed on the generated data
sets give a good idea of the confidence regions of the estimates. In this
paper, we debate on the contribution of such methods for model selection, in
the case of feedforward neural networks. The method is described and compared
with the leave-one-out resampling method. The effectiveness of the bootstrap
method, versus the leave-one-out methode, is checked through a number of
examples.
