A new technique for performing probabilistic analysis and optimization design using data classification methods is investigated. The approach is based on nonlinear decision boundaries constructed from data classification methods. A statistical learning tool known as support vector machine (SVM) is used to construct the boundaries. An adaptive sampling technique is used to generate samples and update the approximated decision function. The proposed approach is demonstrated with several benchmark and engineering problems.

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