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Showing 1 - 20 results of 2,646 for search '(( elements method algorithm ) OR ((( data code algorithm ) OR ( based solutions algorithm ))))', query time: 0.37s Refine Results
  1. 1
    Data and code resources.

    Data and code resources. by Sam Hall-McMaster (10343795)

    Published 2025
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  2. 2
    Numbers of iterations (#Ite) and eigenvalue calculations (#Eig) in the DC algorithm on the random and chain graph datasets.

    Numbers of iterations (#Ite) and eigenvalue calculations (#Eig) in the DC algorithm on the random and chain graph datasets. by Tomokaze Shiratori (9635271)

    Published 2024
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  3. 3
    Code might be found under: https://kaggle.com/code/agatasko/anomalies-graph-networks.

    Code might be found under: https://kaggle.com/code/agatasko/anomalies-graph-networks. by Agata Skorupka (20469080)

    Published 2024
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  4. 4
    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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  5. 5
    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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  6. 6
    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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  7. 7
    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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  8. 8
    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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  9. 9
    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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  10. 10
    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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  11. 11
    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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  12. 12
    Graphs of the regularization terms.

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

    Published 2024
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  13. 13
    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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  14. 14
    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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  15. 15
    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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  16. 16
    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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  17. 17
    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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  18. 18
    G R code algorithm.

    G R code algorithm. by R. Sakthivel (2589547)

    Published 2024
    “…The algorithm was developed and coded in Verilog and simulated using Modelsim. …”
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  19. 19
    QQESPM-GS - Codes and Data

    QQESPM-GS - Codes and Data by Carlos Pontes (20835533)

    Published 2025
    “…<p dir="ltr">Codes and Data for the algorithms, methods and experiments presented in the paper titled "QQESPM-GS: an Efficient Solution for Geo-textual Search Parameterized by Graph-based Spatial Patterns".…”
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  20. 20
    Code and data.

    Code and data. by Shangyang Jin (21772466)

    Published 2025
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