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bayesian optimization » based optimization (Expand Search)
phase optimization » whale optimization (Expand Search), based optimization (Expand Search), path optimization (Expand Search)
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its phase » gas phase (Expand Search), ii phase (Expand Search)
bayesian optimization » based optimization (Expand Search)
phase optimization » whale optimization (Expand Search), based optimization (Expand Search), path optimization (Expand Search)
library based » laboratory based (Expand Search)
binary its » binary pairs (Expand Search)
its phase » gas phase (Expand Search), ii phase (Expand Search)
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Classification performance after optimization.
Published 2025“…We applied this hybrid strategy to a Radial Basis Function Network (RBFN), and validated its performance improvements through extensive experiments, including ANOVA and Wilcoxon tests for both feature selection and optimization phases. …”
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3
ANOVA test for optimization results.
Published 2025“…We applied this hybrid strategy to a Radial Basis Function Network (RBFN), and validated its performance improvements through extensive experiments, including ANOVA and Wilcoxon tests for both feature selection and optimization phases. …”
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4
Wilcoxon test results for optimization.
Published 2025“…We applied this hybrid strategy to a Radial Basis Function Network (RBFN), and validated its performance improvements through extensive experiments, including ANOVA and Wilcoxon tests for both feature selection and optimization phases. …”
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5
Wilcoxon test results for feature selection.
Published 2025“…We applied this hybrid strategy to a Radial Basis Function Network (RBFN), and validated its performance improvements through extensive experiments, including ANOVA and Wilcoxon tests for both feature selection and optimization phases. …”
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6
Feature selection metrics and their definitions.
Published 2025“…We applied this hybrid strategy to a Radial Basis Function Network (RBFN), and validated its performance improvements through extensive experiments, including ANOVA and Wilcoxon tests for both feature selection and optimization phases. …”
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7
Statistical summary of all models.
Published 2025“…We applied this hybrid strategy to a Radial Basis Function Network (RBFN), and validated its performance improvements through extensive experiments, including ANOVA and Wilcoxon tests for both feature selection and optimization phases. …”
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8
Feature selection results.
Published 2025“…We applied this hybrid strategy to a Radial Basis Function Network (RBFN), and validated its performance improvements through extensive experiments, including ANOVA and Wilcoxon tests for both feature selection and optimization phases. …”
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9
ANOVA test for feature selection.
Published 2025“…We applied this hybrid strategy to a Radial Basis Function Network (RBFN), and validated its performance improvements through extensive experiments, including ANOVA and Wilcoxon tests for both feature selection and optimization phases. …”
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10
Classification performance of ML and DL models.
Published 2025“…We applied this hybrid strategy to a Radial Basis Function Network (RBFN), and validated its performance improvements through extensive experiments, including ANOVA and Wilcoxon tests for both feature selection and optimization phases. …”
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11
Results of network meta-analysis.
Published 2023“…Therefore, based on Bayesian algorithm, a network meta-analysis was conducted by us to make a comparison between different combination therapies of α-RBs and traditional Chinese medicine external therapy.…”
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Results of network meta-analysis.
Published 2023“…Therefore, based on Bayesian algorithm, a network meta-analysis was conducted by us to make a comparison between different combination therapies of α-RBs and traditional Chinese medicine external therapy.…”
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13
Results of network meta-analysis.
Published 2023“…Therefore, based on Bayesian algorithm, a network meta-analysis was conducted by us to make a comparison between different combination therapies of α-RBs and traditional Chinese medicine external therapy.…”
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14
Results of network meta-analysis.
Published 2023“…Therefore, based on Bayesian algorithm, a network meta-analysis was conducted by us to make a comparison between different combination therapies of α-RBs and traditional Chinese medicine external therapy.…”
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15
Study flowchart.
Published 2023“…Therefore, based on Bayesian algorithm, a network meta-analysis was conducted by us to make a comparison between different combination therapies of α-RBs and traditional Chinese medicine external therapy.…”
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16
Risk of bias graph.
Published 2023“…Therefore, based on Bayesian algorithm, a network meta-analysis was conducted by us to make a comparison between different combination therapies of α-RBs and traditional Chinese medicine external therapy.…”
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17
Results of network meta-analysis.
Published 2023“…Therefore, based on Bayesian algorithm, a network meta-analysis was conducted by us to make a comparison between different combination therapies of α-RBs and traditional Chinese medicine external therapy.…”
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18
Characteristics of included studies.
Published 2023“…Therefore, based on Bayesian algorithm, a network meta-analysis was conducted by us to make a comparison between different combination therapies of α-RBs and traditional Chinese medicine external therapy.…”
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19
Diversity and specificity of lipid patterns in basal soil food web resources
Published 2019“…In marine environments, multivariate optimization models (Quantitative Fatty Acid Signature Analysis) and Bayesian approaches (source-tracking algorithm) were established to predict the proportion of predator diets using lipids as tracers. …”
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Minisymposterium: Muq-Hippylib: A Bayesian Inference Software Framework Integrating Data with Complex Predictive Models under Uncertainty
Published 2021“…In this poster, we present an extensible software framework MUQ-hIPPYlib that overcomes this hurdle by providing unprecedented access to state-of-the-art algorithms for Bayesian inverse problems. MUQ provides a spectrum of powerful Bayesian inversion models and algorithms, but expects forward models to come equipped with gradients/Hessians to permit large-scale solution. hIPPYlib implements powerful large-scale gradient/Hessian-based solvers in an environment that can automatically generate needed derivatives, but it lacks full Bayesian capabilities. …”