Showing 781 - 800 results of 2,607 for search 'S6 (classification)', query time: 0.05s Refine Results
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    Layer ensemble averaging for fault tolerance in memristive neural networks by Osama Yousuf, Brian D. Hoskins, Karthick Ramu, Mitchell Fream, William A. Borders, Advait Madhavan, Matthew W. Daniels, Andrew Dienstfrey, Jabez J. McClelland, Martin Lueker-Boden, Gina C. Adam

    Published 2025-02-01
    “…For the image classification task with 20% stuck-at faults, accuracy improves from 40% to 89.6% (within 5% of baseline), and for the continual learning problem, accuracy improves from 55% to 71% (within 1% of baseline). …”
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    Klasifikasi Ulasan Palsu Menggunakan Borderline Over Sampling (BOS) dan Support Vector Machine (SVM) (Studi Kasus : Ulasan Tempat Makan) by Aisyah Awalina, Fitra Abdurrachman Bachtiar, Indriati Indriati

    Published 2022-02-01
    “…However, many of these spam review datasets are imbalanced and thus may affect classification results. In this study, BOS algorithm was used to overcome data imbalances, and SVM algorithm for the classification of spam reviews. …”
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    Geotechnical Suitability of Soils in Road Construction for Sustainable Development in Tropical Africa: Case of Lateritic Graveled Soils of Bandjoun (West, Cameroon) by Carlos Foko Tamba, Lucas Kengni, Paul Tematio

    Published 2023-01-01
    “…The LGSB presents Californian bearing ratio indices ranging from 26% to 83.3%, a plasticity index of 10.11%, a fines content of 12.05%, an average methylene blue value of 4.25, a water content at the Proctor optimum of 27.6% and a dry density of 1.75 g/cm3. They are silty or clayey gravels and sands of subgroup A-2-7 according to the Highway Research Board classification; sandy and gravelly soils with fines of class B according to the classification of the Road Construction Guide; and silty gravels according to the classification of the Central Laboratory of Bridges and Highways. …”
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