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Showing 121 - 140 results of 13,198 for search '(((( algorithm loss function ) OR ( algorithm i function ))) OR ( algorithm python function ))*', query time: 0.76s Refine Results
  1. 121
    APSO–CNN and FNN algorithms’ loss function validation sets.

    APSO–CNN and FNN algorithms’ loss function validation sets. by Ahmed Bahaa (12887063)

    Published 2022
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  2. 122
    Loss function values of target detection algorithms during training.

    Loss function values of target detection algorithms during training. by Guodong Su (7876934)

    Published 2024
    “…<p>Loss function values of target detection algorithms during training.…”
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  3. 123
    SwinT-YOLOv4 loss function curve.

    SwinT-YOLOv4 loss function curve. by Kewei Lu (138871)

    Published 2023
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  4. 124
    Strahler number (<i>H</i>), branching ratio () and length ratio () as functions of relative age ().

    Strahler number (<i>H</i>), branching ratio () and length ratio () as functions of relative age (). by Jonathan E Kings (20818367)

    Published 2025
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  5. 125
    Comparison results of the loss functions.

    Comparison results of the loss functions. by Jia Li (160557)

    Published 2025
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  6. 126
    Automated correction of fluorescence bleedthrough in the larval eye.

    Automated correction of fluorescence bleedthrough in the larval eye. by Sebastian M. Bernasek (8525829)

    Published 2020
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  7. 127
    Automated unsupervised annotation of clones in the larval eye.

    Automated unsupervised annotation of clones in the larval eye. by Sebastian M. Bernasek (8525829)

    Published 2020
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  8. 128
    Perturbing gene expression via mitotic recombination.

    Perturbing gene expression via mitotic recombination. by Sebastian M. Bernasek (8525829)

    Published 2020
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  9. 129
    APSO–WOA–CNN and FNN algorithms’ loss function validation sets.

    APSO–WOA–CNN and FNN algorithms’ loss function validation sets. by Ahmed Bahaa (12887063)

    Published 2022
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  10. 130
    APSO–WOA–CNN and FNN algorithms’ loss function training sets.

    APSO–WOA–CNN and FNN algorithms’ loss function training sets. by Ahmed Bahaa (12887063)

    Published 2022
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  11. 131
    MSE of MOP parameters with various schemes with <i>n</i> = 50 and <i>α</i> = 2, <i>θ</i> = 1.5, <i>β</i> = 0.5.

    MSE of MOP parameters with various schemes with <i>n</i> = 50 and <i>α</i> = 2, <i>θ</i> = 1.5, <i>β</i> = 0.5. by R. Alshenawy (13162866)

    Published 2022
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  12. 132
    Data Sheet 1_Machine learning algorithm based on combined clinical indicators for the prediction of infertility and pregnancy loss.docx

    Data Sheet 1_Machine learning algorithm based on combined clinical indicators for the prediction of infertility and pregnancy loss.docx by Rui Zhang (13940)

    Published 2025
    “…The model for potential pregnancy loss was also developed using five machine learning algorithms and was based on 7 indicators. …”
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  13. 133
    MSE of MOP parameters with various schemes with <i>n</i> = 50 and <i>α</i> = 2.5, <i>θ</i> = 1.5, <i>β</i> = 0.5.

    MSE of MOP parameters with various schemes with <i>n</i> = 50 and <i>α</i> = 2.5, <i>θ</i> = 1.5, <i>β</i> = 0.5. by R. Alshenawy (13162866)

    Published 2022
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  14. 134
    Changes in loss values for different algorithms.

    Changes in loss values for different algorithms. by Xiaojun Wang (50332)

    Published 2025
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  15. 135
    MSEs and biases for MLEs and bayes estimators of <i>α</i> = 2, <i>θ</i> = 1.5, <i>β</i> = 0.5.

    MSEs and biases for MLEs and bayes estimators of <i>α</i> = 2, <i>θ</i> = 1.5, <i>β</i> = 0.5. by R. Alshenawy (13162866)

    Published 2022
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  16. 136
    Image_1_Identification and verification of diagnostic biomarkers in recurrent pregnancy loss via machine learning algorithm and WGCNA.tif

    Image_1_Identification and verification of diagnostic biomarkers in recurrent pregnancy loss via machine learning algorithm and WGCNA.tif by Changqiang Wei (11454415)

    Published 2023
    “…This profile underwent differential expression analysis, WGCNA, functional enrichment, and subsequent analysis of RPL gene expression using LASSO regression, SVM-RFE, and RandomForest algorithms for hub gene screening. …”
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  17. 137
    MSEs and biases for MLEs and bayes estimators of <i>α</i> = 2.5, <i>θ</i> = 1.5, <i>β</i> = 0.5.

    MSEs and biases for MLEs and bayes estimators of <i>α</i> = 2.5, <i>θ</i> = 1.5, <i>β</i> = 0.5. by R. Alshenawy (13162866)

    Published 2022
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  18. 138
    Point and interval prediction of the unobserved order statistics for scheme 3, with <i>α</i> = 0.5, <i>β</i> = 0.5, <i>θ</i> = 0.5.

    Point and interval prediction of the unobserved order statistics for scheme 3, with <i>α</i> = 0.5, <i>β</i> = 0.5, <i>θ</i> = 0.5. by R. Alshenawy (13162866)

    Published 2022
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  19. 139
    Point and interval prediction of the unobserved order statistics for scheme 2, with <i>α</i> = 0.5, <i>β</i> = 0.5, <i>θ</i> = 0.5.

    Point and interval prediction of the unobserved order statistics for scheme 2, with <i>α</i> = 0.5, <i>β</i> = 0.5, <i>θ</i> = 0.5. by R. Alshenawy (13162866)

    Published 2022
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  20. 140
    Point and interval prediction of the unobserved order statistics for scheme 1, with <i>α</i> = 0.5, <i>β</i> = 0.5, <i>θ</i> = 0.5.

    Point and interval prediction of the unobserved order statistics for scheme 1, with <i>α</i> = 0.5, <i>β</i> = 0.5, <i>θ</i> = 0.5. by R. Alshenawy (13162866)

    Published 2022
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