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Showing 61 - 80 results of 15,205 for search '(( element method algorithm ) OR ((( data code algorithm ) OR ( based based algorithm ))))', query time: 0.44s Refine Results
  1. 61
    Computational cost of anchor-based algorithms.

    Computational cost of anchor-based algorithms. by Olarewaju Mubashiru Lawal (14710251)

    Published 2025
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  2. 62
    Computation time as a function of the sample size on the chain graph dataset.

    Computation time as a function of the sample size on the chain graph dataset. by Tomokaze Shiratori (9635271)

    Published 2024
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  3. 63
    Log-likelihood as a function of the number of selected edges on the chain graph dataset (black dashed line: The true number of edges).

    Log-likelihood as a function of the number of selected edges on the chain graph dataset (black dashed line: The true number of edges). by Tomokaze Shiratori (9635271)

    Published 2024
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  4. 64
    Computation time as a function of the sample size on the random graph dataset.

    Computation time as a function of the sample size on the random graph dataset. by Tomokaze Shiratori (9635271)

    Published 2024
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  5. 65
    F1 score of edges selected through cross-validation on the chain graph dataset.

    F1 score of edges selected through cross-validation on the chain graph dataset. by Tomokaze Shiratori (9635271)

    Published 2024
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  6. 66
    Log-likelihood as a function of the number of selected edges on the random graph dataset (black dashed line: The true number of edges).

    Log-likelihood as a function of the number of selected edges on the random graph dataset (black dashed line: The true number of edges). by Tomokaze Shiratori (9635271)

    Published 2024
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  7. 67
    Examples of ground-truth graph structures with (<i>p</i>, <i>n</i><sub>≠0</sub>) = (10, 10).

    Examples of ground-truth graph structures with (<i>p</i>, <i>n</i><sub>≠0</sub>) = (10, 10). by Tomokaze Shiratori (9635271)

    Published 2024
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  8. 68
    Number of edges selected through cross-validation on the chain graph dataset.

    Number of edges selected through cross-validation on the chain graph dataset. by Tomokaze Shiratori (9635271)

    Published 2024
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  9. 69
    Number of edges selected through cross-validation on the random graph dataset.

    Number of edges selected through cross-validation on the random graph dataset. by Tomokaze Shiratori (9635271)

    Published 2024
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  10. 70
    Graphs of the regularization terms.

    Graphs of the regularization terms. by Tomokaze Shiratori (9635271)

    Published 2024
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  11. 71
    F1 score of a given number of selected edges on the chain graph dataset.

    F1 score of a given number of selected edges on the chain graph dataset. by Tomokaze Shiratori (9635271)

    Published 2024
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  12. 72
    Computation time as a function of the number of variables on the chain graph dataset.

    Computation time as a function of the number of variables on the chain graph dataset. by Tomokaze Shiratori (9635271)

    Published 2024
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  13. 73
    Computation time as a function of the number of variables on the random graph dataset.

    Computation time as a function of the number of variables on the random graph dataset. by Tomokaze Shiratori (9635271)

    Published 2024
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  14. 74
    F1 score of a given number of selected edges on the random graph dataset.

    F1 score of a given number of selected edges on the random graph dataset. by Tomokaze Shiratori (9635271)

    Published 2024
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  15. 75
    F1 score of edges selected through cross-validation on the random graph dataset.

    F1 score of edges selected through cross-validation on the random graph dataset. by Tomokaze Shiratori (9635271)

    Published 2024
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  16. 76
    Code used for data analysis in this study.

    Code used for data analysis in this study. by Nourin Sultana (22339463)

    Published 2025
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  17. 77
    Code for MR-based diagnosis of PCNSL

    Code for MR-based diagnosis of PCNSL by Paul Naser (17776497)

    Published 2025
    “…We aimed to develop an easy-to-use algorithm, based on a convolutional neural network (CNN) to preoperatively discern PCNSL from GBM and systematically compare its performance to experienced neurosurgeons and radiologists. …”
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  18. 78
    The metro zone algorithm pseudo-code.

    The metro zone algorithm pseudo-code. by Sarah Alhumoud (22258183)

    Published 2025
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  19. 79
    Data and code.

    Data and code. by Hao Cao (2248066)

    Published 2025
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  20. 80
    Data and code used in this article.

    Data and code used in this article. by Feng Shen (151470)

    Published 2024
    “…The innovative use of the PSO algorithm addresses the limitations of traditional lag structure selection methods and enhances the predictive potential of the MIDAS model.…”
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