This paper deals with the application of the support vector machine (SVM) and the leastsquares SVM regressions to the uncertainty quantification of complex systems with a high-dimensional parameter space. The above regression techniques are used to build accurate and compact surrogate models of the system responses from a limited set of training samples. The accuracy and the feasibility of the proposed modeling techniques are then investigated by comparing their results with the ones predicted by a sparse polynomial chaos expansion by considering two real-life problems with 8 and 30 random variables, respectively. INDEX TERMS Machine learning, uncertainty quantification, parameterized modeling, surrogate models, SVM regression, LS-SVM regression, sparse PC expansion, integrated voltage regulator (IVR), wireless power transfer (WPT). I. INTRODUCTION