Microwave Characterization Using Least-Square Support Vector Machines

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This paper presents the use of the least-square support vector machines (LS-SVM) technique, combined with the finite element method (FEM), to evaluate the microwave properties of dielectric materials. The LS-SVM is a statistical learning method that has good generalization capability and learning performance. The FEM is used to create the data set required to train the LS-SVM. The performance of LS-SVM model depends on a careful setting of its associated hyper-parameters. Different tuning techniques for optimizing the LS-SVM hyper-parameters are studied: cross validation (CV), genetic algorithms (GA), heuristic approach, and Bayesian regularization (BR). Results show that BR provides a good compromise between accuracy and computational cost.