Model Selection for Support Vector Classifiers via Direct Search Simplex Method

This paper addresses the problem of tuning hyperparameters in support vector machine modeling. A Direct Search Simplex (DSS) method, which seeks to evolve hyperparameter values using an empirical error estimate as steering criterion, is proposed and experimentally evaluated on real-world datasets. DSS is a robust hill climbing scheeme, a popular derivative-free optimization method, suitable for low-dimensional optimization problems for which the computation of the derivatives is impossible or difficult. This method produces satisfactory results.