  We study the behavior of error bounds for multiclass classification under
suitable margin conditions. For a wide variety of methods we prove that the
classification error under a hard-margin condition decreases exponentially fast
without any bias-variance trade-off. Different convergence rates can be
obtained in correspondence of different margin assumptions. With a
self-contained and instructive analysis we are able to generalize known results
from the binary to the multiclass setting.
