Universal approximation using radial-basis-function networks
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates
Mathematics and Computers in Simulation - IMACS sponsored Special issue on the second IMACS seminar on Monte Carlo methods
Support vector machines: hype or hallelujah?
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Machine Learning
An effective screening design for sensitivity analysis of large models
Environmental Modelling & Software
Hi-index | 0.00 |
New classification based methods for global sensitivity analysis of structural models are presented which do not require the full approximation of the model response for qualitatively good sensitivity measures. Instead, only the level sets of the model response are identified by partitioning it into a number of classes with a few available sample points. The average change in class memberships of simulated points on the model domain is considered as sensitivity measure. The new methods are realized using Support Vector Machines and their results are compared with existing methods by using analytical as well as practical industry examples.