Shock physics experiments are often complicated and expensive. As a result, researchers are unable to conduct as many experiments as they would like - leading to sparse data sets. In this paper, Support Vector Machines for regression are applied to velocimetry data sets for shock damaged and melted tin met al. Some success at interpolating between data sets is achieved. Implications for future work are discussed.
As noted above, SVM for regression (as opposed to SVM classification) is rarely applied in physics. There are, however, several successful examples of the support vector regression application. In @cite_3 introduced the regression type of the SVM technique to the civil engineering community and showed that SVM can be successfully applied to the problem of stream flow data estimation based on records of rainfall and other climatic data. By using three types of kernels, Polynomial, RBF, and Neural Network, and choosing the best values for SVM free parameters via trial and error, the authors point out that the SVM with the RBF kernel performs the best. Finally, this research is the first attempt to apply support vector regression in data analysis of VISAR measurements obtained from experiments on shock melted and damaged met al.
@cite_3
2126326837
Traditional classification approaches generalize poorly on image classification tasks, because of the high dimensionality of the feature space. This paper shows that support vector machines (SVM) can generalize well on difficult image classification problems where the only features are high dimensional histograms. Heavy-tailed RBF kernels of the form K(x, y)=e sup - spl rho spl Sigma sub i sup |xia-yia|b with a spl les 1 and b spl les 2 are evaluated on the classification of images extracted from the Corel stock photo collection and shown to far outperform traditional polynomial or Gaussian radial basis function (RBF) kernels. Moreover, we observed that a simple remapping of the input x sub i spl rarr x sub i sup a improves the performance of linear SVM to such an extend that it makes them, for this problem, a valid alternative to RBF kernels.