The determination of wt% alumina (wa) and pore volume fraction (pv) in alumina-based bioceramics is important in ceramic engineering. This article adopts support vector machine (SVM) and relevance vector machine (RVM) for prediction of wa and pv based on SiC. SVM is firmly based on theory of statistical learning. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. The developed SVM and RVM give equations for prediction of wa and pv. This article gives robust models based on SVM and RVM for prediction of wa and pv.
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