  We study numerically the properties of the bayesian perceptron through a
gradient descent on the optimal cost function. The theoretical distribution of
stabilities is deduced. It predicts that the optimal generalizer lies close to
the boundary of the space of (error-free) solutions. The numerical simulations
are in good agreement with the theoretical distribution. The extrapolation of
the generalization error to infinite input space size agrees with the
theoretical results. Finite size corrections are negative and exhibit two
different scaling regimes, depending on the training set size. The variance of
the generalization error vanishes for $N \rightarrow \infty$ confirming the
property of self-averaging.
