Search alternatives:
feature optimization » resource optimization (Expand Search), feature elimination (Expand Search), structure optimization (Expand Search)
used optimization » based optimization (Expand Search), led optimization (Expand Search), guided optimization (Expand Search)
space feature » scale feature (Expand Search), scale features (Expand Search), shape features (Expand Search)
binary space » binary image (Expand Search), banach space (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
Showing 101 - 120 results of 278 for search '(( binary space feature optimization algorithm ) OR ( binary data used optimization algorithm ))', query time: 0.53s Refine Results
  1. 101
    Classification baseline performance.

    Classification baseline performance. by Doaa Sami Khafaga (21463870)

    Published 2025
    Save to List
    Saved in:
  2. 102
    ANOVA test result.

    ANOVA test result. by Doaa Sami Khafaga (21463870)

    Published 2025
    Save to List
    Saved in:
  3. 103
    Summary of literature review.

    Summary of literature review. by Doaa Sami Khafaga (21463870)

    Published 2025
    Save to List
    Saved in:
  4. 104
    An illustration and comparison of the fitness convergence curves for the Large-scale dataset (WaveformEW), medium-scale dataset (Zoo dataset), and Small-scale (Wine dataset) using 50 and 100 population sizes.

    An illustration and comparison of the fitness convergence curves for the Large-scale dataset (WaveformEW), medium-scale dataset (Zoo dataset), and Small-scale (Wine dataset) using... by Olaide N. Oyelade (14047002)

    Published 2023
    Subjects:
    Save to List
    Saved in:
  5. 105
    Datasets and their properties.

    Datasets and their properties. by Olaide N. Oyelade (14047002)

    Published 2023
    Subjects:
    Save to List
    Saved in:
  6. 106
    An illustration and comparison of the cost convergence curves for the Large-scale dataset (WaveformEW), medium-scale dataset (Zoo dataset), and Small-scale (Wine dataset) using 50 and 100 population sizes.

    An illustration and comparison of the cost convergence curves for the Large-scale dataset (WaveformEW), medium-scale dataset (Zoo dataset), and Small-scale (Wine dataset) using 50... by Olaide N. Oyelade (14047002)

    Published 2023
    Subjects:
    Save to List
    Saved in:
  7. 107
    A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some high-dimensional datasets using a variation of 50 and 100 population sizes.

    A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some high-dimensional datasets usi... by Olaide N. Oyelade (14047002)

    Published 2023
    Subjects:
    Save to List
    Saved in:
  8. 108
    Fig 7 -

    Fig 7 - by Olaide N. Oyelade (14047002)

    Published 2023
    Subjects:
    Save to List
    Saved in:
  9. 109
    Fig 4 -

    Fig 4 - by Olaide N. Oyelade (14047002)

    Published 2023
    Subjects:
    Save to List
    Saved in:
  10. 110
    A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some medium-sized dimensional datasets using a variation of 50 and 100 population sizes.

    A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some medium-sized dimensional data... by Olaide N. Oyelade (14047002)

    Published 2023
    Subjects:
    Save to List
    Saved in:
  11. 111
    A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some small-sized dimensional datasets using a variation of 50 and 100 population sizes.

    A radar plot illustrating the comparison of the classification accuracy, cost values, and fitness values for the hybrids of BEOSA when applied to some small-sized dimensional datas... by Olaide N. Oyelade (14047002)

    Published 2023
    Subjects:
    Save to List
    Saved in:
  12. 112
    Parameter settings.

    Parameter settings. by Olaide N. Oyelade (14047002)

    Published 2023
    Subjects:
    Save to List
    Saved in:
  13. 113
    Fig 2 -

    Fig 2 - by Olaide N. Oyelade (14047002)

    Published 2023
    Subjects:
    Save to List
    Saved in:
  14. 114
    Flowchart scheme of the ML-based model.

    Flowchart scheme of the ML-based model. by Noshaba Qasmi (20405009)

    Published 2024
    “…<b>G)</b> Deep feature extraction using VGG16. <b>H)</b> Training data comprising 80% of the dataset. …”
    Save to List
    Saved in:
  15. 115
    Data_Sheet_1_A Global Optimizer for Nanoclusters.PDF

    Data_Sheet_1_A Global Optimizer for Nanoclusters.PDF by Maya Khatun (7437011)

    Published 2019
    Save to List
    Saved in:
  16. 116
    Transformation of symbolic features in NSL-KDD.

    Transformation of symbolic features in NSL-KDD. by Asmaa Ahmed Awad (16726315)

    Published 2023
    Save to List
    Saved in:
  17. 117
    Microsoft excel file containing exhaustive list of Abbreviated-Injury Scale codes used to generate binary variables encoding the presence or absence of injury to various body regions/organ systems.

    Microsoft excel file containing exhaustive list of Abbreviated-Injury Scale codes used to generate binary variables encoding the presence or absence of injury to various body regio... by Michael D. Cobler-Lichter (22492930)

    Published 2025
    Save to List
    Saved in:
  18. 118
    The statistical description of the original data set of the patients (<i>n</i> = 162).

    The statistical description of the original data set of the patients (<i>n</i> = 162). by Uğur Ejder (22683228)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  19. 119
    Data_Sheet_1_Posiform planting: generating QUBO instances for benchmarking.pdf

    Data_Sheet_1_Posiform planting: generating QUBO instances for benchmarking.pdf by Georg Hahn (12530469)

    Published 2023
    Save to List
    Saved in:
  20. 120
    The list of parameters of the modified data set for machine learning (<i>n</i> = 162).

    The list of parameters of the modified data set for machine learning (<i>n</i> = 162). by Uğur Ejder (22683228)

    Published 2025
    Subjects:
    Save to List
    Saved in:

Search Tools: