Yet faster method to optimize SVR hyperparameters based on minimizing cross-validation error

The performance of support vector (SV) regression deeply depends on its hyperparameters such as an insensitive zone thickness, a penalty factor, kernel function parameters. A method called MCV-SVR was once proposed, which optimizes SVR hyperparameters λ so that a cross-validation error is minimized. The method iterates two steps until convergence; step 1 optimizes parameters θ under given λ, while step 2 improves λ under given θ. Recently a faster version called the MCV-SVR-light was proposed, which accelerates step 2 by pruning. The present paper yet accelerates step 1 of the MCV-SVR-light by pruning without affecting solution quality. Here the pruning means confining the process to support vectors. Our experiments using three data sets show that the proposed method converged faster than the existing methods while the generalization performance remained comparable.