  We present a new family of model selection algorithms based on the resampling
heuristics. It can be used in several frameworks, do not require any knowledge
about the unknown law of the data, and may be seen as a generalization of local
Rademacher complexities and $V$-fold cross-validation. In the case example of
least-square regression on histograms, we prove oracle inequalities, and that
these algorithms are naturally adaptive to both the smoothness of the
regression function and the variability of the noise level. Then,
interpretating $V$-fold cross-validation in terms of penalization, we enlighten
the question of choosing $V$. Finally, a simulation study illustrates the
strength of resampling penalization algorithms against some classical ones, in
particular with heteroscedastic data.
