Application of Support Vector Regression to Interpolation of Sparse Shock Physics Data Sets

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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 metal. Some success at interpolating between data sets is achieved. Implications for future work are discussed.
13 pages, 7 figures

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Artificial Intelligence, I.2; J.2

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http://salesiana.dossiersoluciones.com/handle/123456789/109825

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/cs/0603081
 http://arxiv.org/abs/cs/0603081

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arXiv