Aleks points me to this paper by Jerry Friedman on non-Bayesian regularization methods. I’d also recommend our Bayesian approach (see this Annals of Applied Statistics paper). Once you’re going to assume a probability model for the data (a likelihood), it’s a pretty small step to include prior information as well. But read Friedman’s paper in any case. He focuses more on computational issues than we do. There are really two parallel literatures.