Metamodeling: a state of the art review
WSC '94 Proceedings of the 26th conference on Winter simulation
Mathematical Programming: Series A and B
The nature of statistical learning theory
The nature of statistical learning theory
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
Simulation and the Monte Carlo Method
Simulation and the Monte Carlo Method
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Pattern Search Algorithms for Bound Constrained Minimization
SIAM Journal on Optimization
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
An interior algorithm for nonlinear optimization that combines line search and trust region steps
Mathematical Programming: Series A and B
Sampling-based RBDO using the stochastic sensitivity analysis and Dynamic Kriging method
Structural and Multidisciplinary Optimization
Sequential sampling for contour estimation with concurrent function evaluations
Structural and Multidisciplinary Optimization
Sequential design of computer experiments for the estimation of a probability of failure
Statistics and Computing
Constrained efficient global optimization with support vector machines
Structural and Multidisciplinary Optimization
Detection and classification of defect patterns in optical inspection using support vector machines
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
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In this paper, an efficient classification methodology is developed for reliability analysis while maintaining an accuracy level similar to or better than existing response surface methods. The sampling-based reliability analysis requires only the classification information--a success or a failure--but the response surface methods provide function values on the domain as their output, which requires more computational effort. The problem is even more challenging when dealing with high-dimensional problems due to the curse of dimensionality. In the newly proposed virtual support vector machine (VSVM), virtual samples are generated near the limit state function by using an approximation method. The function values are used for approximations of virtual samples to improve accuracy of the resulting VSVM decision function. By introducing the virtual samples, VSVM can overcome the deficiency in existing classification methods where only classification values are used as their input. The universal Kriging method is used to obtain virtual samples to improve the accuracy of the decision function for highly nonlinear problems. A sequential sampling strategy that chooses new samples near the limit state function is integrated with VSVM to improve the accuracy. Examples show the proposed adaptive VSVM yields better efficiency in terms of modeling and response evaluation time and the number of required samples while maintaining similar level or better accuracy, especially for high-dimensional problems.