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Showing 81 - 100 results of 1,176 for search '(( query processing algorithm ) OR ((( relevant data algorithm ) OR ( elements based algorithm ))))', query time: 0.48s Refine Results
  1. 81
    Prevalence of stunting among adolescent girls in Ethiopia, 2016 with unbalanced data (A) and balanced date using SMOTE (B).

    Prevalence of stunting among adolescent girls in Ethiopia, 2016 with unbalanced data (A) and balanced date using SMOTE (B). by Alemu Birara Zemariam (17540938)

    Published 2025
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  2. 82
    Data Sheet 1_Predicting place of delivery choice among childbearing women in East Africa: a comparative analysis of advanced machine learning techniques.pdf

    Data Sheet 1_Predicting place of delivery choice among childbearing women in East Africa: a comparative analysis of advanced machine learning techniques.pdf by Habtamu Setegn Ngusie (12178327)

    Published 2024
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  3. 83
    Data Sheet 2_Predicting place of delivery choice among childbearing women in East Africa: a comparative analysis of advanced machine learning techniques.pdf

    Data Sheet 2_Predicting place of delivery choice among childbearing women in East Africa: a comparative analysis of advanced machine learning techniques.pdf by Habtamu Setegn Ngusie (12178327)

    Published 2024
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  4. 84
    Model-Based Clustering of Categorical Data Based on the Hamming Distance

    Model-Based Clustering of Categorical Data Based on the Hamming Distance by Raffaele Argiento (647076)

    Published 2024
    “…<p>A model-based approach is developed for clustering categorical data with no natural ordering. …”
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  5. 85
    Predicting CO<sub>2</sub> Solubility in Diverse Ionic Liquids: A Data-Driven Approach Using Machine Learning Algorithms

    Predicting CO<sub>2</sub> Solubility in Diverse Ionic Liquids: A Data-Driven Approach Using Machine Learning Algorithms by Zahra Bastami (21474755)

    Published 2025
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  6. 86
    Predicting CO<sub>2</sub> Solubility in Diverse Ionic Liquids: A Data-Driven Approach Using Machine Learning Algorithms

    Predicting CO<sub>2</sub> Solubility in Diverse Ionic Liquids: A Data-Driven Approach Using Machine Learning Algorithms by Zahra Bastami (21474755)

    Published 2025
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  7. 87
    Results of RF algorithm screening factors.

    Results of RF algorithm screening factors. by Jintao Li (448681)

    Published 2024
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  8. 88
    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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  9. 89
    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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  10. 90
    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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  11. 91
    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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  12. 92
    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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  13. 93
    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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  14. 94
    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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  15. 95
    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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  16. 96
    Graphs of the regularization terms.

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

    Published 2024
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  17. 97
    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. 98
    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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  19. 99
    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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  20. 100
    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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