  We propose a general family of algorithms for regression estimation with
quadratic loss. Our algorithms are able to select relevant functions into a
large dictionary. We prove that a lot of algorithms that have already been
studied for this task (LASSO and Group LASSO, Dantzig selector, Iterative
Feature Selection, among others) belong to our family, and exhibit another
particular member of this family that we call Correlation Selector in this
paper. Using general properties of our family of algorithm we prove oracle
inequalities for IFS, for the LASSO and for the Correlation Selector, and
compare numerical performances of these estimators on a toy example.
