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.
Original language | English (US) |
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Pages (from-to) | 469-476 |
Number of pages | 8 |
Journal | Journal of Machine Learning Research |
Volume | 9 |
State | Published - 2010 |
Event | 13th International Conference on Artificial Intelligence and Statistics, AISTATS 2010 - Sardinia, Italy Duration: May 13 2010 → May 15 2010 |
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Software
- Statistics and Probability
- Artificial Intelligence