Showing 10,121 - 10,140 results of 55,503 for search '(( a ((mean decrease) OR (linear decrease)) ) OR ( a ((largest decrease) OR (greater decrease)) ))', query time: 0.86s Refine Results
  1. 10121

    Image_1_Machine Learning Applied to the Search for Nonlinear Features in Breeding Populations.TIF by Iulian Gabur (11720927)

    Published 2022
    “…<p>Large plant breeding populations are traditionally a source of novel allelic diversity and are at the core of selection efforts for elite material. …”
  2. 10122

    Table_2_Machine Learning Applied to the Search for Nonlinear Features in Breeding Populations.CSV by Iulian Gabur (11720927)

    Published 2022
    “…<p>Large plant breeding populations are traditionally a source of novel allelic diversity and are at the core of selection efforts for elite material. …”
  3. 10123

    Image_2_Machine Learning Applied to the Search for Nonlinear Features in Breeding Populations.TIF by Iulian Gabur (11720927)

    Published 2022
    “…<p>Large plant breeding populations are traditionally a source of novel allelic diversity and are at the core of selection efforts for elite material. …”
  4. 10124

    Table_1_Machine Learning Applied to the Search for Nonlinear Features in Breeding Populations.XLSX by Iulian Gabur (11720927)

    Published 2022
    “…<p>Large plant breeding populations are traditionally a source of novel allelic diversity and are at the core of selection efforts for elite material. …”
  5. 10125

    Table_3_Machine Learning Applied to the Search for Nonlinear Features in Breeding Populations.xlsx by Iulian Gabur (11720927)

    Published 2022
    “…<p>Large plant breeding populations are traditionally a source of novel allelic diversity and are at the core of selection efforts for elite material. …”
  6. 10126

    Table_4_Machine Learning Applied to the Search for Nonlinear Features in Breeding Populations.xlsx by Iulian Gabur (11720927)

    Published 2022
    “…<p>Large plant breeding populations are traditionally a source of novel allelic diversity and are at the core of selection efforts for elite material. …”
  7. 10127
  8. 10128

    Assessment values of machine learning models. by Bin Pan (742525)

    Published 2025
    “…The prediction results indicate that the StackBoost model excels in predicting aqueous solubility, achieving a coefficient of determination () of 0.90, a root mean square error (RMSE) of 0.29, and a mean absolute error (MAE) of 0.22, significantly outperforming the other comparative models. …”
  9. 10129

    List of datasets in AqSolDB. by Bin Pan (742525)

    Published 2025
    “…The prediction results indicate that the StackBoost model excels in predicting aqueous solubility, achieving a coefficient of determination () of 0.90, a root mean square error (RMSE) of 0.29, and a mean absolute error (MAE) of 0.22, significantly outperforming the other comparative models. …”
  10. 10130

    Feature importance derived from SHAP analysis. by Bin Pan (742525)

    Published 2025
    “…The prediction results indicate that the StackBoost model excels in predicting aqueous solubility, achieving a coefficient of determination () of 0.90, a root mean square error (RMSE) of 0.29, and a mean absolute error (MAE) of 0.22, significantly outperforming the other comparative models. …”
  11. 10131
  12. 10132
  13. 10133
  14. 10134

    Significant repeated measurements sEMG outcomes. by María Benito-de-Pedro (22057468)

    Published 2025
    “…<div><p>Lateral ankle sprain (LAS) is a very common injury in the world of basketball. …”
  15. 10135

    Maximum voluntary contraction assessment. by María Benito-de-Pedro (22057468)

    Published 2025
    “…<div><p>Lateral ankle sprain (LAS) is a very common injury in the world of basketball. …”
  16. 10136

    Significant single measurement sEMG outcomes. by María Benito-de-Pedro (22057468)

    Published 2025
    “…<div><p>Lateral ankle sprain (LAS) is a very common injury in the world of basketball. …”
  17. 10137
  18. 10138
  19. 10139
  20. 10140