Support Vector Machine and Relevance Vector Machine for Prediction of Alumina and Pore Volume Fraction in Bioceramics
Source abstract
Abstract
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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Research context
OpenAlex topics: Mineral Processing and Grinding · Industrial Vision Systems and Defect Detection · Face and Expression Recognition
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Mechanical Engineering Department, Jaya Suriya Engineering College Anna University Chennai 600055 India
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
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