Adaptive sparse polynomial chaos expansions for global sensitivity analysis based on support vector regression
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[1] Seymour Geisser,et al. The Predictive Sample Reuse Method with Applications , 1975 .
[2] S. Sorooshian,et al. Evaluation of Maximum Likelihood Parameter estimation techniques for conceptual rainfall‐runoff models: Influence of calibration data variability and length on model credibility , 1983 .
[3] Ilya M. Sobol,et al. Sensitivity Estimates for Nonlinear Mathematical Models , 1993 .
[4] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[5] John C. Platt,et al. Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .
[6] I. Sobola,et al. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates , 2001 .
[7] Soroosh Sorooshian,et al. A framework for development and application of hydrological models , 2001, Hydrology and Earth System Sciences.
[8] A. Sarkar,et al. Mid-frequency structural dynamics with parameter uncertainty , 2001 .
[9] José Alí Moreno,et al. Fast Monte Carlo reliability evaluation using support vector machine , 2002, Reliab. Eng. Syst. Saf..
[10] Chih-Jen Lin,et al. Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel , 2003, Neural Computation.
[11] D. Xiu,et al. A new stochastic approach to transient heat conduction modeling with uncertainty , 2003 .
[12] Bernhard Schölkopf,et al. A tutorial on support vector regression , 2004, Stat. Comput..
[13] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[14] Yoshua Bengio,et al. Inference for the Generalization Error , 1999, Machine Learning.
[15] Yunqian Ma,et al. Practical selection of SVM parameters and noise estimation for SVM regression , 2004, Neural Networks.
[16] Ping-Feng Pai,et al. Software reliability forecasting by support vector machines with simulated annealing algorithms , 2006, J. Syst. Softw..
[17] O. L. Maître,et al. Uncertainty propagation in CFD using polynomial chaos decomposition , 2006 .
[18] Ping-Feng Pai,et al. System reliability forecasting by support vector machines with genetic algorithms , 2006, Math. Comput. Model..
[19] Shih-Wei Lin,et al. Particle swarm optimization for parameter determination and feature selection of support vector machines , 2008, Expert Syst. Appl..
[20] Chih-Jen Lin,et al. A Practical Guide to Support Vector Classication , 2008 .
[21] Bruno Sudret,et al. Global sensitivity analysis using polynomial chaos expansions , 2008, Reliab. Eng. Syst. Saf..
[22] Wei-Chiang Hong,et al. Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model , 2009 .
[23] M. Eldred. Recent Advances in Non-Intrusive Polynomial Chaos and Stochastic Collocation Methods for Uncertainty Analysis and Design , 2009 .
[24] Bruno Sudret,et al. Efficient computation of global sensitivity indices using sparse polynomial chaos expansions , 2010, Reliab. Eng. Syst. Saf..
[25] Paola Annoni,et al. Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index , 2010, Comput. Phys. Commun..
[26] B. Sudret,et al. An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis , 2010 .
[27] A. Basudhar,et al. An improved adaptive sampling scheme for the construction of explicit boundaries , 2010 .
[28] Bruno Sudret,et al. Adaptive sparse polynomial chaos expansion based on least angle regression , 2011, J. Comput. Phys..
[29] Maurice Lemaire,et al. Assessing small failure probabilities by combined subset simulation and Support Vector Machines , 2011 .
[30] Hakan A. Çirpan,et al. A set of new Chebyshev kernel functions for support vector machine pattern classification , 2011, Pattern Recognit..
[31] Patrick M. Reed,et al. When are multiobjective calibration trade‐offs in hydrologic models meaningful? , 2012 .
[32] Enrico Zio,et al. System reliability prediction by support vector regression with analytic selection and genetic algorithm parameters selection , 2015, Appl. Soft Comput..
[33] Mohammad Rajabi,et al. Polynomial chaos expansions for uncertainty propagation and moment independent sensitivity analysis of seawater intrusion simulations , 2015 .
[34] Francesca Pianosi,et al. A Matlab toolbox for Global Sensitivity Analysis , 2015, Environ. Model. Softw..
[35] Junjie Li,et al. System reliability analysis of slopes using least squares support vector machines with particle swarm optimization , 2016, Neurocomputing.
[36] Weihua Zhang,et al. Global sensitivity analysis using a Gaussian Radial Basis Function metamodel , 2016, Reliab. Eng. Syst. Saf..
[37] Bruno Sudret,et al. Polynomial meta-models with canonical low-rank approximations: Numerical insights and comparison to sparse polynomial chaos expansions , 2015, J. Comput. Phys..
[38] Francesco Montomoli,et al. SAMBA: Sparse Approximation of Moment-Based Arbitrary Polynomial Chaos , 2016, J. Comput. Phys..
[39] J.-M. Bourinet,et al. Rare-event probability estimation with adaptive support vector regression surrogates , 2016, Reliab. Eng. Syst. Saf..
[40] Zhenzhou Lu,et al. Multivariate sensitivity analysis based on the direction of eigen space through principal component analysis , 2017, Reliab. Eng. Syst. Saf..
[41] Francesco Contino,et al. A robust and efficient stepwise regression method for building sparse polynomial chaos expansions , 2017, J. Comput. Phys..