Research on Relevance Vector Machine Model Based on Kernel Partial Least Square in Properties of Engineering Materials
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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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2013 ◽
Vol 765-767
◽
pp. 528-531
2014 ◽
Vol 496-500
◽
pp. 2256-2259
Keyword(s):