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Comments on: On Active Learning Methods for Manifold Data

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References

  • Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition, pp 248–255

  • Gahrooei MR, Paynabar K, Pacella M, Colosimo BM (2019) An adaptive fused sampling approach of high-accuracy data in the presence of low-accuracy data. IISE Trans 51(11):1–14

    Article  Google Scholar 

  • Gal Y, Islam R, Ghahramani Z (2017) Deep Bayesian active learning with image data. In: Proceedings of the 34th international conference on machine learning, vol 70, pp 1183–1192

  • Gramacy RB (2016) laGP: large-scale spatial modeling via local approximate Gaussian processes in R. J Stat Softw 72(1):1–46

    Article  MathSciNet  Google Scholar 

  • Mesnil O, Yan H, Ruzzene M, Paynabar K, Shi J (2014) Frequency domain instantaneous wavenumber estimation for damage quantification in layered plate structures. In: EWSHM-7th European workshop on structural health monitoring, 2014

  • Roustant O, Ginsbourger D, Deville Y (2012) DiceKriging, DiceOptim: two R packages for the analysis of computer experiments by kriging-based metamodeling and optimization

  • Simpson JA (1992) Mechanical measurement and manufacturing. Control Dyn Syst Adv Theory Appl 45:17

    Google Scholar 

  • Smailagic A, Costa P, Noh HY, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A. MedAL (2018) Accurate and robust deep active learning for medical image analysis. In: 17th IEEE international conference on machine learning and applications (ICMLA) 2018, pp 481–488

  • Snelson E, Ghahramani Z (2006) Sparse Gaussian processes using pseudo-inputs. In: Advances in neural information processing systems, pp 1257–1264

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Authors and Affiliations

  1. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Mostafa Reisi Gahrooei

  2. School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA

    Hao Yan

  3. H. Milton School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA

    Kamran Paynabar

Authors
  1. Mostafa Reisi Gahrooei
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  2. Hao Yan
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  3. Kamran Paynabar
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Correspondence to Kamran Paynabar.

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This comment refers to the invited paper available at: https://doi.org/10.1007/s11749-019-00694-y.

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Reisi Gahrooei, M., Yan, H. & Paynabar, K. Comments on: On Active Learning Methods for Manifold Data. TEST 29, 38–41 (2020). https://doi.org/10.1007/s11749-019-00696-w

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