Post by Felipe Chegury Viana on Jun 3, 2009 7:52:38 GMT -5
Dear all,
Here it is a reference on conservative surrogates:
F. A. C. Viana, V. Picheny, and R.T. Haftka, "Safety Margins for Conservative Surrogates," in: 8th World Congress on Structural and Multidisciplinary Optimization, Lisbon, Portugal, June 1-5, 2009.
Using surrogate models for learning or optimization creates a risk associated to the fitting error that must be accounted for. Conservative surrogates are metamodels designed to safely estimate the actual response of the system. In this work we use safety margins to generate conservative surrogates. Given a desired level of conservativeness (percentage of safe predictions), we propose the use of cross-validation for estimating the required safety margin. We also explore how multiple surrogates and cross-validation can be used to minimize the loss of accuracy inherent in conservative surrogates. The approach was tested on two algebraic examples for ten basic surrogates including different instances of kriging, polynomial response surface, radial basis neural networks and support vector regression surrogates. For these examples we found that cross-validation (i) is effective for selecting the safety margin; and (ii) allows us to select a surrogate with the best compromise between conservativeness and loss of accuracy.
You can find more about it online:
fchegury.googlepages.com
All the best,
Felipe A.C. Viana
Here it is a reference on conservative surrogates:
F. A. C. Viana, V. Picheny, and R.T. Haftka, "Safety Margins for Conservative Surrogates," in: 8th World Congress on Structural and Multidisciplinary Optimization, Lisbon, Portugal, June 1-5, 2009.
Using surrogate models for learning or optimization creates a risk associated to the fitting error that must be accounted for. Conservative surrogates are metamodels designed to safely estimate the actual response of the system. In this work we use safety margins to generate conservative surrogates. Given a desired level of conservativeness (percentage of safe predictions), we propose the use of cross-validation for estimating the required safety margin. We also explore how multiple surrogates and cross-validation can be used to minimize the loss of accuracy inherent in conservative surrogates. The approach was tested on two algebraic examples for ten basic surrogates including different instances of kriging, polynomial response surface, radial basis neural networks and support vector regression surrogates. For these examples we found that cross-validation (i) is effective for selecting the safety margin; and (ii) allows us to select a surrogate with the best compromise between conservativeness and loss of accuracy.
You can find more about it online:
fchegury.googlepages.com
All the best,
Felipe A.C. Viana