Regularization in statistical models with multidimensional prediction functions

Matthias Schmid
Boosting is one of the most important methods for fitting regression models and building prediction rules from high-dimensional data. A notable feature of boosting is that the technique has a built-in mechanism for shrinking coefficient estimates and variable selection. This regularization mechanism[for full text, please go to the a.m. URL]