Alternatives to the k-means algorithm that find better clusterings
Proceedings of the eleventh international conference on Information and knowledge management
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Design and Analysis of Experiments
Design and Analysis of Experiments
A survey on approaches for reliability-based optimization
Structural and Multidisciplinary Optimization
Structural and Multidisciplinary Optimization
Constrained efficient global optimization with support vector machines
Structural and Multidisciplinary Optimization
An adaptive hybrid surrogate model
Structural and Multidisciplinary Optimization
Adaptive virtual support vector machine for reliability analysis of high-dimensional problems
Structural and Multidisciplinary Optimization
A local adaptive sampling method for reliability-based design optimization using Kriging model
Structural and Multidisciplinary Optimization
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This article presents a methodology to generate explicit decision functions using support vector machines (SVM). A decision function is defined as the boundary between two regions of a design space (e.g., an optimization constraint or a limit-state function in reliability). The SVM-based decision function, which is initially constructed based on a design of experiments, depends on the amount and quality of the training data used. For this reason, an adaptive sampling scheme that updates the decision function is proposed. An accurate approximated explicit decision functions is obtained with a reduced number of function evaluations. Three problems are presented to demonstrate the efficiency of the update scheme to explicitly reconstruct known analytical decision functions. The chosen functions are the boundaries of disjoint regions of the design space. A convergence criterion and error measure are proposed. The scheme is also applied to the definition of an explicit failure region boundary in the case of the buckling of a geometrically nonlinear arch.