SVM regression parameters optimization using parallel global search algorithm

Abstract

The problem of optimal parameters selection for the regression construction method using Support Vector Machine is stated. Cross validation error function is taken as the criterion. Arising bound constrained nonlinear optimization problem is solved using parallel global search algorithm by R. Strongin with a number of modifications. Efficiency of the proposed approach is demonstrated using model problems. A possibility of the algorithm usage on large-scale cluster systems is evaluated. Linear speed-up of combined parallel global search algorithm is demonstrated. © 2013 Springer-Verlag Berlin Heidelberg.