On the trade-off between number of examples and precision of supervision in machine learning problems

DOI10.1007/s11590-019-01486-xzbMath1472.62109OpenAlexW2978657651MaRDI QIDQ2037897

Giorgio Gnecco, Federico Nutarelli

Publication date: 8 July 2021

Published in: Optimization Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11590-019-01486-x

zbMATH Keywords

linear regressionordinary least squaresvariance controllarge-sample approximationoptimal supervision time

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Linear regression; mixed models (62J05) Learning and adaptive systems in artificial intelligence (68T05)

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Optimal trade-off between sample size, precision of supervision, and selection probabilities for the unbalanced fixed effects panel data model ⋮ Computational design of innovative mechanical metafilters via adaptive surrogate-based optimization ⋮ Optimal data collection design in machine learning: the case of the fixed effects generalized least squares panel data model ⋮ On the optimal generalization error for weighted least squares under variable individual supervision times

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• LQG Online Learning

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