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svm with certain kernels (e.g. polynomial and radial basis function, rbf)  (Drucker Diagnostics)

 
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    Drucker Diagnostics svm with certain kernels (e.g. polynomial and radial basis function, rbf)
    Svm With Certain Kernels (E.G. Polynomial And Radial Basis Function, Rbf), supplied by Drucker Diagnostics, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/svm-rbf+kernel/svm/pm35697648-48-8-23
    Average 90 stars, based on 1 article reviews
    svm with certain kernels (e.g. polynomial and radial basis function, rbf) - by Bioz Stars, 2026-10
    90/100 stars

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    Comparison of Different FPGA SVM Implementation

    Journal: Ieee Transactions on Very Large Scale Integration (Vlsi) Systems

    Article Title: An Ultralow-Power Real-Time Machine Learning Based fNIRS Motion Artifacts Detection

    doi: 10.1109/TVLSI.2024.3356161

    Figure Lengend Snippet: Comparison of Different FPGA SVM Implementation

    Article Snippet: The combined fNIRS data preprocessing and RBF kernel SVM algorithm architecture were designed and simulated within the Simulink environment.

    Techniques: Comparison

    The multistage learning approach and decision level fusion of individual classifiers. “Fusion 1” refers to the hard-level combination of the individual predictions obtained from RBF and Polynomial kernel based SVMs. “Fusion 2” refers to the hard-level combination of the individual predictions obtained from Softmax function and RBF kernel based SVM. “Fusion 3” refers to the hard-level combination of the individual predictions obtained from Softmax function and Polynomial kernel based SVM. “Fusion 4” refers to the hard-level combination of the individual predictions obtained from Softmax function, RBF and Polynomial kernel based SVMs

    Journal: Applied Intelligence

    Article Title: Decision and feature level fusion of deep features extracted from public COVID-19 data-sets

    doi: 10.1007/s10489-021-02945-8

    Figure Lengend Snippet: The multistage learning approach and decision level fusion of individual classifiers. “Fusion 1” refers to the hard-level combination of the individual predictions obtained from RBF and Polynomial kernel based SVMs. “Fusion 2” refers to the hard-level combination of the individual predictions obtained from Softmax function and RBF kernel based SVM. “Fusion 3” refers to the hard-level combination of the individual predictions obtained from Softmax function and Polynomial kernel based SVM. “Fusion 4” refers to the hard-level combination of the individual predictions obtained from Softmax function, RBF and Polynomial kernel based SVMs

    Article Snippet: “Fusion 2” refers to the hard-level combination of the individual predictions obtained from Softmax function and RBF kernel based SVM.

    Techniques:

    The detailed presentation of accuracy values obtained from applied individual and ensemble learning scenarios for three data-sets (average accuracy values of 5-folds are given)

    Journal: Applied Intelligence

    Article Title: Decision and feature level fusion of deep features extracted from public COVID-19 data-sets

    doi: 10.1007/s10489-021-02945-8

    Figure Lengend Snippet: The detailed presentation of accuracy values obtained from applied individual and ensemble learning scenarios for three data-sets (average accuracy values of 5-folds are given)

    Article Snippet: “Fusion 2” refers to the hard-level combination of the individual predictions obtained from Softmax function and RBF kernel based SVM.

    Techniques: Standard Deviation, Plasmid Preparation