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Showing 141 - 160 results of 12,434 for search '(((( algorithm i function ) OR ( algorithm pre function ))) OR ( algorithm python function ))', query time: 1.44s Refine Results
  1. 141
    Definitions of global parameters used for constructing random ecosystems.

    Definitions of global parameters used for constructing random ecosystems. by Robert Marsland (8616483)

    Published 2020
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  2. 142
    WST results for LLSKSO and other algorithms on benchmark test functions. (<i>α</i> = 0.05).

    WST results for LLSKSO and other algorithms on benchmark test functions. (<i>α</i> = 0.05). by Ruyi Dong (9038174)

    Published 2025
    “…<p>WST results for LLSKSO and other algorithms on benchmark test functions. (<i>α</i> = 0.05).…”
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  3. 143
    WST results for LLSKSO and other algorithms on benchmark test functions. (<i>α</i> = 0.05).

    WST results for LLSKSO and other algorithms on benchmark test functions. (<i>α</i> = 0.05). by Ruyi Dong (9038174)

    Published 2025
    “…<p>WST results for LLSKSO and other algorithms on benchmark test functions. (<i>α</i> = 0.05).…”
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  4. 144
    Results from trajectory optimization of walking.

    Results from trajectory optimization of walking. by Antoine Falisse (6061601)

    Published 2019
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  5. 145
    Hist-8000 sequence similarity scores are closer to the ground truth than the corresponding Sum-of-learnt-Trigrams scores. (NW, Hist-8000) and (NW, Hist-SDM12) are both higher than (NW,SoT) for 10 sets of 1000 proteins sampled from Swiss-Prot [41]. close to 1 indicates a strong positive relationship between the alignment and cos. similarity scores, while values closer to zero indicate no relationship. We report mean and standard deviation of the correlations. NW: Needleman-Wunch global sequence alignment algorithm, Hist-8000: Histogram-8000, SoT: Sum-of-learnt-Trigrams, : Spearman rank correlation, Hist-SDM12: Hist-SDM12: Histogram-Structural Derived Matrix-12.

    Hist-8000 sequence similarity scores are closer to the ground truth than the corresponding Sum-of-learnt-Trigrams scores. (NW, Hist-8000) and (NW, Hist-SDM12) are both higher than... by Frixos Papadopoulos (22001664)

    Published 2025
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  6. 146
    Reward function related parameters.

    Reward function related parameters. by Honglei Pang (22693724)

    Published 2025
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  7. 147
    Comparison of details of different algorithms.

    Comparison of details of different algorithms. by Hao Wu (65943)

    Published 2024
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  8. 148
    Structure of the LMPEC algorithm model.

    Structure of the LMPEC algorithm model. by Hao Wu (65943)

    Published 2024
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  9. 149
    Dataset of networks used in assessing the Troika algorithm for clique partitioning and community detection

    Dataset of networks used in assessing the Troika algorithm for clique partitioning and community detection by Samin Aref (4683934)

    Published 2025
    “…Each network is provided in .gml format or .pkl format which can be read into a networkX graph object using standard functions from the networkX library in Python. For accessing other networks used in the study, please refer to the article for references to the primary sources of those network data.…”
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  10. 150
    Rosenbrock function.

    Rosenbrock function. by Rutinaldo Aguiar Nascimento (14259594)

    Published 2022
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  11. 151
    Rosenbrock function.

    Rosenbrock function. by Rutinaldo Aguiar Nascimento (14259594)

    Published 2022
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  12. 152
    Rastrigin function.

    Rastrigin function. by Rutinaldo Aguiar Nascimento (14259594)

    Published 2022
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  13. 153
    Rosenbrock function.

    Rosenbrock function. by Rutinaldo Aguiar Nascimento (14259594)

    Published 2022
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  14. 154
    Rastrigin function.

    Rastrigin function. by Rutinaldo Aguiar Nascimento (14259594)

    Published 2022
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  15. 155
    Search Algorithms and Loss Functions for Bayesian Clustering

    Search Algorithms and Loss Functions for Bayesian Clustering by David B. Dahl (11761055)

    Published 2022
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  16. 156
    Global optima for objective functions <i>f</i><sub>1</sub> –<i>f</i><sub>14</sub>.

    Global optima for objective functions <i>f</i><sub>1</sub> –<i>f</i><sub>14</sub>. by Panagiotis Aivaliotis-Apostolopoulos (13851171)

    Published 2022
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  17. 157
    Non-converging numerical solution of the non-linear equation system through the iterations <i>p</i> using the Newton-Raphson algorithm and the Levenberg–Marquardt algorithm.

    Non-converging numerical solution of the non-linear equation system through the iterations <i>p</i> using the Newton-Raphson algorithm and the Levenberg–Marquardt algorithm. by Martin Dodek (20623832)

    Published 2025
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  18. 158
    Population <i>versus</i> success rate for the Rosenbrock function.

    Population <i>versus</i> success rate for the Rosenbrock function. by Rutinaldo Aguiar Nascimento (14259594)

    Published 2022
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  19. 159
    Population <i>versus</i> success rate for the Rastrigin function.

    Population <i>versus</i> success rate for the Rastrigin function. by Rutinaldo Aguiar Nascimento (14259594)

    Published 2022
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  20. 160
    Tunable parameters in stimuli generation module. <sup><i>a</i></sup>The fixation cross is displayed once before an experiment starts to help subjects concentrate. <sup><i>b</i></sup>Different stimuli are displayed equally. If desired by the user, some stimuli can be displayed more/less frequently by assigning them a higher/lower weight.

    Tunable parameters in stimuli generation module. <sup><i>a</i></sup>The fixation cross is displayed once before an experiment starts to help subjects concentrate. <sup><i>b</i></su... by Gursimran Singh (575288)

    Published 2025
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