Search alternatives:
largest decrease » larger decrease (Expand Search), marked decrease (Expand Search)
values decrease » values increased (Expand Search)
learning data » learning dataset (Expand Search), learning a (Expand Search)
data decrease » rate decreased (Expand Search), a decrease (Expand Search), deaths decreased (Expand Search)
a largest » _ largest (Expand Search), a large (Expand Search), a latest (Expand Search)
largest decrease » larger decrease (Expand Search), marked decrease (Expand Search)
values decrease » values increased (Expand Search)
learning data » learning dataset (Expand Search), learning a (Expand Search)
data decrease » rate decreased (Expand Search), a decrease (Expand Search), deaths decreased (Expand Search)
a largest » _ largest (Expand Search), a large (Expand Search), a latest (Expand Search)
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List of network connections predictive of individual SoF values, together with their β weights, in decreasing order.
Published 2025“…<p>List of network connections predictive of individual SoF values, together with their β weights, in decreasing order.…”
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Median SHAP values (calculated for CSCHF at age 5000 days) for binary features in the competing risks RSF model for different disease categories. The features are sorted in decreasing order by absolute value for the cancer outcomes.
Published 2025“…The features are sorted in decreasing order by absolute value for the cancer outcomes.…”
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The MAE value of the model under raw data.
Published 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.
Published 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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Yearly crude rates normalized by their 1999 value.
Published 2025“…<p>Values below 1 indicate crude rate decreases relative to their 1999 value, those above 1 indicate increases. …”
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Comparison of environmental perception time results at different learning rates.
Published 2025Subjects: -
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RMSE versus learning rate.
Published 2025“…Field experiments demonstrate that the predicted values from the LSTM model closely align with the measured values, maintaining short-term shape error prediction accuracy within 3 mm. …”
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