  The learning properties of finite size polynomial Support Vector Machines are
analyzed in the case of realizable classification tasks. The normalization of
the high order features acts as a squeezing factor, introducing a strong
anisotropy in the patterns distribution in feature space. As a function of the
training set size, the corresponding generalization error presents a crossover,
more or less abrupt depending on the distribution's anisotropy and on the task
to be learned, between a fast-decreasing and a slowly decreasing regime. This
behaviour corresponds to the stepwise decrease found by Dietrich et al.[Phys.
Rev. Lett. 82 (1999) 2975-2978] in the thermodynamic limit. The theoretical
results are in excellent agreement with the numerical simulations.
