Regularization in linear regression and classi cation is viewed as a twostage process. First a set of candidate models is de ned by a path through the space of joint parameter values, and then a point on this path is chosen to be the nal model. Various path nding strategies for the rst stage of this process are examined, based on the notion of generalized gradient descent. Several of these strategies are seen to produce paths that closely correspond to those induced by commonly used… CONTINUE READING