Bounds on the risk play a crucial role in statistical learning theory. They
usually involve as capacity measure of the model studied the VC dimension or
one of its extensions. In classification, such "VC dimensions" exist for models
taking values in {0, 1}, {1,..., Q} and R. We introduce the generalizations
appropriate for the missing case, the one of models with values in R^Q. This
provides us with a new guaranteed risk for M-SVMs which appears superior to the
existing one