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maximization algorithm » optimization algorithms (Expand Search), classification algorithm (Expand Search)
features maximization » feature optimization (Expand Search), feature elimination (Expand Search)
model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
binary climate » binary image (Expand Search)
climate model » climate models (Expand Search), primate model (Expand Search)
maximization algorithm » optimization algorithms (Expand Search), classification algorithm (Expand Search)
features maximization » feature optimization (Expand Search), feature elimination (Expand Search)
model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
binary climate » binary image (Expand Search)
climate model » climate models (Expand Search), primate model (Expand Search)
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1
Hyperparameters of the LSTM Model.
Published 2025“…Out of all the models, LSTM produced the best results. The AD-PSO-Guided WOA algorithm was used to adjust the hyperparameters for the LSTM model. …”
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Prediction results of individual models.
Published 2025“…Out of all the models, LSTM produced the best results. The AD-PSO-Guided WOA algorithm was used to adjust the hyperparameters for the LSTM model. …”
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The AD-PSO-Guided WOA LSTM framework.
Published 2025“…Out of all the models, LSTM produced the best results. The AD-PSO-Guided WOA algorithm was used to adjust the hyperparameters for the LSTM model. …”
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4
Contextual Dynamic Pricing with Strategic Buyers
Published 2024“…The seller does not observe the buyer’s true feature, but a manipulated feature according to buyers’ strategic behavior. …”
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Supplementary Material 8
Published 2025“…</li><li><b>Naïve bayes (NB): </b> A probabilistic classifier based on Bayes' theorem, suitable for predicting resistance phenotypes based on genomic features.</li><li><b>Linear discriminant Analysis (LDA) is a statistica</b>l approach that maximizes class separability. …”
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Adaptive Inference for Change Points in High-Dimensional Data
Published 2021“…On the estimation front, we obtain the convergence rate of the maximizer of our test statistic standardized by sample size when there is one change-point in mean and <i>q</i> = 2, and propose to combine our tests with a wild binary segmentation algorithm to estimate the change-point number and locations when there are multiple change-points. …”