Support Vector Machine and Relevance Vector Machine for Prediction of Alumina and Pore Volume Fraction in Bioceramics Kangeyanallore Govindaswamy Shanmugam Gopinath Mechanical Engineering Department, Jaya Suriya Engineering College, Anna University, Chennai 600055, India Soumen Pal,* Pijush Samui, and Bimal Kumar Sarkar Manufacturing Division, School of Mechanical and Building Sciences, VIT University, Vellore, 632014 Tamil Nadu, India Centre for Disaster Mitigation and Management, VIT University,Vellore, 632014 Tamil Nadu, India Nuclear and Medical Physics Division, School of Advanced Sciences, VIT University, Vellore, 632014 Tamil Nadu, India 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 equa- tions for prediction of wa and pv. This article gives robust models based on SVM and RVM for prediction of wa and pv. *soumenpal12@gmail.com © 2012 The American Ceramic Society Int. J. Appl. Ceram. Technol., 1–7 (2012) DOI:10.1111/j.1744-7402.2012.02810.x