  We study the typical learning properties of the recently proposed Support
Vectors Machines. The generalization error on linearly separable tasks, the
capacity, the typical number of Support Vectors, the margin, and the robustness
or noise tolerance of a class of Support Vector Machines are determined in the
framework of Statistical Mechanics. The robustness is shown to be closely
related to the generalization properties of these machines.
