Search alternatives:
algorithm machine » algorithm achieves (Expand Search), algorithm within (Expand Search)
machine function » achieve functions (Expand Search), sine function (Expand Search)
algorithm state » algorithm steps (Expand Search), algorithms sorted (Expand Search), algorithms estimated (Expand Search)
state function » state functional (Expand Search), rate function (Expand Search), wave function (Expand Search)
Showing 1 - 20 results of 2,122 for search '(( algorithm machine function ) OR ( algorithm state function ))', query time: 0.42s Refine Results
  1. 1
    Automatic translation error detection based on fuzzy decision tree algorithm.

    Automatic translation error detection based on fuzzy decision tree algorithm. by Danqiangyu Zhou (22146196)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  2. 2
    Baseline characteristics of recipients’ comparison before and after data match by the unsupervised machine learning algorithm.

    Baseline characteristics of recipients’ comparison before and after data match by the unsupervised machine learning algorithm. by Mahmoudreza Moein (22127153)

    Published 2025
    Save to List
    Saved in:
  3. 3
    Acurracy comparison of CCA and FBCCA algorithms.

    Acurracy comparison of CCA and FBCCA algorithms. by Hossein Hamidi Shishavan (19688384)

    Published 2024
    Save to List
    Saved in:
  4. 4
    Accuracy of the Random forest algorithm for different values of test samples and different choice of random states.

    Accuracy of the Random forest algorithm for different values of test samples and different choice of random states. by Abeer Aljohani (18497914)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  5. 5
    IEEE CEC 2017 benchmark function specifications.

    IEEE CEC 2017 benchmark function specifications. by Kankan Tang (21474968)

    Published 2025
    Save to List
    Saved in:
  6. 6
    IEEE CEC 2022 benchmark function specifications.

    IEEE CEC 2022 benchmark function specifications. by Kankan Tang (21474968)

    Published 2025
    Save to List
    Saved in:
  7. 7
    Trainable parameters for different algorithms.

    Trainable parameters for different algorithms. by Anwar Hossain Efat (19942283)

    Published 2024
    Save to List
    Saved in:
  8. 8
    Parametric study of accuracy of the Random forest algorithm for different proportions of test samples and different choice of random states.

    Parametric study of accuracy of the Random forest algorithm for different proportions of test samples and different choice of random states. by Abeer Aljohani (18497914)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  9. 9
    (a) Comparison between SDNN and Traditional Methods. The horizontal axis represents the prediction horizon, while the vertical axis denotes computation time; red denotes traditional methods, and blue represents SDNN. (b) Comparison of different algorithms under different inputs. The horizontal axis represents algorithm type and total surrounding computation speed, with red lines indicating single input and blue lines representing multiple inputs.

    (a) Comparison between SDNN and Traditional Methods. The horizontal axis represents the prediction horizon, while the vertical axis denotes computation time; red denotes traditiona... by Kai Sun (140584)

    Published 2025
    Save to List
    Saved in:
  10. 10
    Comparison of PyNoetic’s functionality for BCI design with other existing state-of-the-art frameworks.

    Comparison of PyNoetic’s functionality for BCI design with other existing state-of-the-art frameworks. by Gursimran Singh (575288)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  11. 11
    Multimodal reference functions.

    Multimodal reference functions. by Ruiyu Zhan (21602031)

    Published 2025
    “…We performed comparative analyses against other methodologies across various functions and public datasets to assess their effectiveness. …”
    Save to List
    Saved in:
  12. 12
    Description of test functions.

    Description of test functions. by Lijie Sun (224007)

    Published 2025
    Save to List
    Saved in:
  13. 13
    The convergence curves of the test functions.

    The convergence curves of the test functions. by Ruiyu Zhan (21602031)

    Published 2025
    “…We performed comparative analyses against other methodologies across various functions and public datasets to assess their effectiveness. …”
    Save to List
    Saved in:
  14. 14
    Single-peaked reference functions.

    Single-peaked reference functions. by Ruiyu Zhan (21602031)

    Published 2025
    “…We performed comparative analyses against other methodologies across various functions and public datasets to assess their effectiveness. …”
    Save to List
    Saved in:
  15. 15
    Test results of multimodal benchmark functions.

    Test results of multimodal benchmark functions. by Ruiyu Zhan (21602031)

    Published 2025
    “…We performed comparative analyses against other methodologies across various functions and public datasets to assess their effectiveness. …”
    Save to List
    Saved in:
  16. 16
    Fixed-dimensional multimodal reference functions.

    Fixed-dimensional multimodal reference functions. by Ruiyu Zhan (21602031)

    Published 2025
    “…We performed comparative analyses against other methodologies across various functions and public datasets to assess their effectiveness. …”
    Save to List
    Saved in:
  17. 17
    Test results of multimodal benchmark functions.

    Test results of multimodal benchmark functions. by Ruiyu Zhan (21602031)

    Published 2025
    “…We performed comparative analyses against other methodologies across various functions and public datasets to assess their effectiveness. …”
    Save to List
    Saved in:
  18. 18
    Classification performances of Ensemble (Subspace KNN), SVM, and KNN algorithms when trained with time domain HbO features.

    Classification performances of Ensemble (Subspace KNN), SVM, and KNN algorithms when trained with time domain HbO features. by Ayşenur Eser (21713911)

    Published 2025
    Save to List
    Saved in:
  19. 19
    WGCNA and machine learning algorithms.

    WGCNA and machine learning algorithms. by Shushu Wang (6870191)

    Published 2024
    Save to List
    Saved in:
  20. 20
    Confusion matrix values for different size of test samples and different choice of random states.

    Confusion matrix values for different size of test samples and different choice of random states. by Abeer Aljohani (18497914)

    Published 2025
    Subjects:
    Save to List
    Saved in:

Search Tools: