  In the context of Structural Risk Minimization, one is presented a sequence
of classes $\{\mathcal{G}_j\}$ from which, given a random sample $(X_i,Y_i)$
one wants to choose a strongly consistent estimator. For certain types of
classes of functions, we present a criterion to choose an estimator, based on
the minimization of the sum of empirical error and a complexity penalty
$r(n,j)$ over each class $\G_j$.
  We present also several other results together with important results found
on current literature on the subject, in an attempt to present and unify the
theory in the context of regression estimation. In particular we present a
generalization of the consistency of Structural Risk Minimization in the
context of regression estimation which are all new results found on Chapter 5.
