  In this article, we derive concentration inequalities for the
cross-validation estimate of the generalization error for subagged estimators,
both for classification and regressor. General loss functions and class of
predictors with both finite and infinite VC-dimension are considered. We
slightly generalize the formalism introduced by \cite{DUD03} to cover a large
variety of cross-validation procedures including leave-one-out
cross-validation, $k$-fold cross-validation, hold-out cross-validation (or
split sample), and the leave-$\upsilon$-out cross-validation.
  \bigskip
  \noindent An interesting consequence is that the probability upper bound is
bounded by the minimum of a Hoeffding-type bound and a Vapnik-type bounds, and
thus is smaller than 1 even for small learning set. Finally, we give a simple
rule on how to subbag the predictor. \bigskip
