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weight optimization » joint optimization (Expand Search), yet optimization (Expand Search)
design optimization » bayesian optimization (Expand Search)
binary sample » final sample (Expand Search), binary people (Expand Search), intra sample (Expand Search)
sample design » sampling design (Expand Search), game design (Expand Search)
binary more » binary image (Expand Search)
more weight » core weight (Expand Search), model weight (Expand Search), tumor weight (Expand Search)
Showing 1 - 4 results of 4 for search '(( binary more weight optimization algorithm ) OR ( binary sample design optimization algorithm ))', query time: 0.52s Refine Results
  1. 1
    tUbenet Foundation Model Weights

    tUbenet Foundation Model Weights by Natalie Holroyd (7099391)

    Published 2025
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  2. 2
    MCLP_quantum_annealer_V0.5

    MCLP_quantum_annealer_V0.5 by Anonymous Anonymous (4854526)

    Published 2025
    “…This paper first proposes the QUBO-MCLP algorithm workflow and designs the Transformation Operator for Inequality Constraints Considering the Capacity of Accessible Providers (TOICCAP), which accounts for the scale of accessible supply points. …”
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  3. 3
    Data Sheet 1_Detection of litchi fruit maturity states based on unmanned aerial vehicle remote sensing and improved YOLOv8 model.docx

    Data Sheet 1_Detection of litchi fruit maturity states based on unmanned aerial vehicle remote sensing and improved YOLOv8 model.docx by Changjiang Liang (21099887)

    Published 2025
    “…In addition, YOLOv8-FPDW was more competitive than mainstream object detection algorithms. …”
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  4. 4
    Table 1_Heavy metal biomarkers and their impact on hearing loss risk: a machine learning framework analysis.docx

    Table 1_Heavy metal biomarkers and their impact on hearing loss risk: a machine learning framework analysis.docx by Ali Nabavi (21097424)

    Published 2025
    “…., blood lead and cadmium levels) were analyzed as features, with hearing loss status—defined as a pure-tone average threshold exceeding 25 dB HL across 500, 1,000, 2000, and 4,000 Hz in the better ear—serving as the binary outcome. Multiple machine learning algorithms, including Random Forest, XGBoost, Gradient Boosting, Logistic Regression, CatBoost, and MLP, were optimized and evaluated. …”
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