Research on Relevance Vector Machine Model Based on Kernel Partial Least Square in Properties of Engineering Materials

Xu Sheng Gan; Can Yang; Hai Long Gao

doi:10.4028/www.scientific.net/AMR.952.311

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 952Research on Relevance Vector Machine Model Based...

Research on Relevance Vector Machine Model Based on Kernel Partial Least Square in Properties of Engineering Materials

Abstract:

To improve the prediction of properties of engineering materials, a Relevance Vector Machine (RVM) regression algorithm based on Kernel Partial Least Squares (KPLS) is proposed. In the algorithm, firstly execute the feature extraction from the original samples using KPLS, and then use obtained feature to realize RVM regression. The simulation shows that the hybrid regression algorithm can effectively reduce the difficulty on RVM modeling and has a wide application in prediction of properties of engineering materials.

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Periodical:

Advanced Materials Research (Volume 952)

Pages:

311-314

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.952.311

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Online since:

May 2014

Authors:

Xu Sheng Gan*, Can Yang, Hai Long Gao

Keywords:

Engineering Materials, Feature Extraction, Kernel Partial Least Square, RVM

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* - Corresponding Author

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