Numerical simulations of an optimal algorithm for supervised learning

摘要

We study numerically an optimal learning algorithm for offline supervised noiseless training of a perception, that reaches bayesian generalization. We obtain the finite size corrections of the generalization error and its variance. The latter vanishes like 1/N, thus justifying the assumption of self-averaging done in analytical calculations. We also determined the stabilities distribution of the optimal student.