Predictive modeling in a steelmaking process using optimized relevance vector regression and support vector regression

DOI10.1007/s10479-021-04053-9OpenAlexW3153198727MaRDI QIDQ2675703

Simone Massulini Acosta, Angelo Marcio Oliveira Santanna, Osiris Canciglieri Junior, Anderson Levati Amoroso

Publication date: 26 September 2022

Published in: Annals of Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10479-021-04053-9

zbMATH Keywords

differential evolutionsupport vector regressionprocess modelingsteelmaking processrelevance vector regression

Mathematics Subject Classification ID

Linear inference, regression (62Jxx) Mathematical programming (90Cxx) Artificial intelligence (68Txx)

Uses Software

• LIBSVM
• glmnet
• DeMat

Cites Work

• Parameter setting in evolutionary algorithms.
• Advances in differential evolution
• Differential evolution -- a simple and efficient heuristic for global optimization over continuous spaces
• Practical selection of SVM parameters and noise estimation for SVM regression
• Robust kernel distance multivariate control chart using support vector principles
• Ridge Regression in Practice
• 10.1162/15324430152748236
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