بدائل البحث:
learning optimization » learning motivation (توسيع البحث), lead optimization (توسيع البحث)
driven optimization » design optimization (توسيع البحث), guided optimization (توسيع البحث), dose optimization (توسيع البحث)
image driven » climate driven (توسيع البحث), wave driven (توسيع البحث), mapk driven (توسيع البحث)
less based » lens based (توسيع البحث), lemos based (توسيع البحث), degs based (توسيع البحث)
learning optimization » learning motivation (توسيع البحث), lead optimization (توسيع البحث)
driven optimization » design optimization (توسيع البحث), guided optimization (توسيع البحث), dose optimization (توسيع البحث)
image driven » climate driven (توسيع البحث), wave driven (توسيع البحث), mapk driven (توسيع البحث)
less based » lens based (توسيع البحث), lemos based (توسيع البحث), degs based (توسيع البحث)
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Performance of different ensemble algorithms for optimal partitioning policy.
منشور في 2024الموضوعات: -
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Performance of different ensemble algorithms for optimal offloading policy.
منشور في 2024الموضوعات: -
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Algorithm of the PbGA search for the optimal PbF.
منشور في 2024"…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …"
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Multiobjective Tuning and Performance Assessment of PID Using Teaching–Learning-Based Optimization
منشور في 2021"…The CPA-related non-convex problem and tuning-related multiobjective problem are solved by teaching–learning-based optimization, which guarantees a tighter lower bound for MOV due to the excellent capability of local optima avoidance and has higher computational efficiency due to the low complexity. …"
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Hyperparameter search space used in the optimization of the Basic DeepInsight-CNN.
منشور في 2023الموضوعات: -
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Table_1_Unveiling suspicious phishing attacks: enhancing detection with an optimal feature vectorization algorithm and supervised machine learning.DOCX
منشور في 2024"…Subsequently, data cleansing, curation, and dimensionality reduction were performed to remove outliers, handle missing values, and exclude less predictive features. To identify the optimal model, the study evaluated and compared 15 SML algorithms arising from different machine learning (ML) families, including Bayesian, nearest-neighbors, decision trees, neural networks, quadratic discriminant analysis, logistic regression, bagging, boosting, random forests, and ensembles. …"
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Table_2_Unveiling suspicious phishing attacks: enhancing detection with an optimal feature vectorization algorithm and supervised machine learning.DOCX
منشور في 2024"…Subsequently, data cleansing, curation, and dimensionality reduction were performed to remove outliers, handle missing values, and exclude less predictive features. To identify the optimal model, the study evaluated and compared 15 SML algorithms arising from different machine learning (ML) families, including Bayesian, nearest-neighbors, decision trees, neural networks, quadratic discriminant analysis, logistic regression, bagging, boosting, random forests, and ensembles. …"
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The flow of the SP-DRL algorithm.
منشور في 2023"…Inspired by deep learning and reinforcement learning, combined with the characteristics of rectangular piece packing, a novel algorithm based on deep reinforcement learning is proposed in this work to solve the rectangular strip packing problem. …"
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