يعرض 1 - 20 نتائج من 5,814 نتيجة بحث عن '(((( learning data decrease ) OR ( a larger decrease ))) OR ( i values decrease ))', وقت الاستعلام: 0.59s تنقيح النتائج
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    The introduction of mutualisms into assembled communities increases their connectance and complexity while decreasing their richness. حسب Gui Araujo (22170819)

    منشور في 2025
    "…Parameter values: interaction strengths were drawn from a half-normal distribution of zero mean and a standard deviation of 0.2, and strength for consumers was made no larger than the strength for resources. …"
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    Scheme of g-λ model with larger values λ. حسب Zhanfeng Fan (20390992)

    منشور في 2024
    "…The stress-deformation model of the single uncoupled joint (g-λ model with λ ≥ 1) is employed to depict the nonlinearity of uncoupled joints, with a greater value of the parameter λ signifying a lower degree of non-linearity in the joint model curve. …"
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    Biases in larger populations. حسب Sander W. Keemink (21253563)

    منشور في 2025
    "…<p>(<b>A</b>) Maximum absolute bias vs the number of neurons in the population for the Bayesian decoder. …"
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    The MAE value of the model under raw data. حسب Xiangjuan Liu (618000)

    منشور في 2025
    "…Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. Further integration of Spearman correlation analysis and PCA dimensionality reduction created multidimensional feature sets, revealing substantial accuracy improvements: The BiLSTM model achieved an 83.6% cumulative MAE reduction from 1.65 (raw data) to 0.27 (STL-PCA), while traditional models like Prophet showed an 82.2% MAE decrease after feature engineering optimization. …"
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    Three error values under raw data. حسب Xiangjuan Liu (618000)

    منشور في 2025
    "…Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. Further integration of Spearman correlation analysis and PCA dimensionality reduction created multidimensional feature sets, revealing substantial accuracy improvements: The BiLSTM model achieved an 83.6% cumulative MAE reduction from 1.65 (raw data) to 0.27 (STL-PCA), while traditional models like Prophet showed an 82.2% MAE decrease after feature engineering optimization. …"
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