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Support Vector Machine and Relevance Vector Machine for Prediction of Alumina and Pore Volume Fraction in Bioceramics
Author(s) -
Gopinath Kangeyanallore Govindaswamy Shanmugam,
Pal Soumen,
Samui Pijush,
Sarkar Bimal Kumar
Publication year - 2012
Publication title -
international journal of applied ceramic technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.4
H-Index - 57
eISSN - 1744-7402
pISSN - 1546-542X
DOI - 10.1111/j.1744-7402.2012.02810.x
Subject(s) - relevance vector machine , support vector machine , relevance (law) , machine learning , artificial intelligence , volume fraction , materials science , computer science , representation (politics) , bayesian probability , pattern recognition (psychology) , composite material , politics , political science , law
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.