Adaptive Importance Sampling Using Probabilistic Classification Vector Machines
نویسنده
چکیده
This abstract presents the basic idea of a new adaptive methodology for reliability assessment using probabilistic classification vector machines (PCVMs) [1], a variant of support vector machines (SVMs) [2, 3]. The proposed method is pivoted around two principal concepts definition of an explicit failure boundary and its variability using PCVMs, and importance sampling (IS) [4–6]. The proposed method puts particular emphasis on the presence of multiple design points [7–10].
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تاریخ انتشار 2012