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Comments on: On Active Learning Methods for Manifold Data

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  • Mostafa Reisi Gahrooei

    (University of Florida)

  • Hao Yan

    (Arizona State University)

  • Kamran Paynabar

    (Georgia Institute of Technology)

Abstract

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  • Mostafa Reisi Gahrooei & Hao Yan & Kamran Paynabar, 2020. "Comments on: On Active Learning Methods for Manifold Data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(1), pages 38-41, March.
    • Handle: RePEc:spr:testjl:v:29:y:2020:i:1:d:10.1007_s11749-019-00696-w
    DOI: 10.1007/s11749-019-00696-w
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    References listed on IDEAS

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    1. Gramacy, Robert B., 2016. "laGP: Large-Scale Spatial Modeling via Local Approximate Gaussian Processes in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 72(i01).
    2. Mostafa Reisi Gahrooei & Kamaran Paynabar & Massimo Pacella & Bianca Maria Colosimo, 2019. "An adaptive fused sampling approach of high-accuracy data in the presence of low-accuracy data," IISE Transactions, Taylor & Francis Journals, vol. 51(11), pages 1251-1264, November.
    3. Roustant, Olivier & Ginsbourger, David & Deville, Yves, 2012. "DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(i01).
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