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Showing 1 - 20 results of 5,715 for search '(((( selected _ algorithm ) OR ( element method algorithm ))) OR ( source code algorithm ))', query time: 0.32s Refine Results
  1. 1
    The lowest reconstruction errors and optimal parameters of the five algorithms.

    The lowest reconstruction errors and optimal parameters of the five algorithms. by Jing Wang (6206297)

    Published 2025
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  2. 2
    The highest classification accuracies and optimal parameters of the five algorithms.

    The highest classification accuracies and optimal parameters of the five algorithms. by Jing Wang (6206297)

    Published 2025
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  3. 3
    Reconstruction errors of the five algorithms with different numbers of projection vectors.

    Reconstruction errors of the five algorithms with different numbers of projection vectors. by Jing Wang (6206297)

    Published 2025
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  4. 4
    The reconstruction error and classification accuracy of the five algorithms on the six face databases.

    The reconstruction error and classification accuracy of the five algorithms on the six face databases. by Jing Wang (6206297)

    Published 2025
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  5. 5
    Convergence curve of the DBO algorithm.

    Convergence curve of the DBO algorithm. by Ma Haohao (22177538)

    Published 2025
    “…The improved Dung Beetle Optimization algorithm, Back Propagation Neural Network, Finite Element Analysis, and Response Surface Methodology provide a strong guarantee for the selection of robot polishing process parameters. …”
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  6. 6
    Source code.

    Source code. by Jesse Wheeler (18462457)

    Published 2025
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  7. 7
    Source Code and Dataset

    Source Code and Dataset by Swathi Ganesan (14394924)

    Published 2025
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  8. 8
    File includes code "Transfer learning.R" and "updatebeta.cpp" for implementing transfer learning methods.

    File includes code "Transfer learning.R" and "updatebeta.cpp" for implementing transfer learning methods. by Lulu Pan (8977979)

    Published 2025
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  9. 9
    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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  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
    Performance comparison of feature selection strategies for <i>S</i>. <i>cerevisiae</i> S phosphorylation site.

    Performance comparison of feature selection strategies for <i>S</i>. <i>cerevisiae</i> S phosphorylation site. by Chao Wang (146527)

    Published 2024
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  13. 13
    Comparison results of four feature selection strategies for <i>S</i>. <i>cerevisiae</i> S phosphorylation site.

    Comparison results of four feature selection strategies for <i>S</i>. <i>cerevisiae</i> S phosphorylation site. by Chao Wang (146527)

    Published 2024
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  14. 14
    The source code of LazyAct.

    The source code of LazyAct. by Hongjie Zhang (136127)

    Published 2025
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  15. 15
    source code of the PyMatterSim package

    source code of the PyMatterSim package by Y.-C. Hu (21440990)

    Published 2025
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  16. 16
    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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  17. 17
    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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  18. 18
    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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  19. 19
    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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  20. 20
    The method for determining the chemical constituents of unknown materials in the algorithm.

    The method for determining the chemical constituents of unknown materials in the algorithm. by Dong Hyeok Choi (12562048)

    Published 2025
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