Descent Methods for Tuning Parameter Refinement

Alexander Lorbert, Peter Ramadge ; JMLR W&CP 9:469-476, 2010.

Abstract

This paper addresses multidimensional tuning parameter selection in the context of "train-validate-test" and K-fold cross validation. A coarse grid search over tuning parameter space is used to initialize a descent method which then jointly optimizes over variables and tuning parameters. We study four regularized regression methods and develop the update equations for the corresponding descent algorithms. Experiments on both simulated and real-world datasets show that the method results in significant tuning parameter refinement.



Home Page

Papers

Submissions

News

Scope

Editorial Board

Announcements

Proceedings

Open Source Software

Search

Login



RSS Feed

Page last modified on Wed Mar 24 15:36 GMT 2010.

Copyright @ JMLR 2000. All rights reserved.