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svm with a radial basis function (rbf) kernel  (MathWorks Inc)


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    MathWorks Inc svm with a radial basis function (rbf) kernel
    Svm With A Radial Basis Function (Rbf) Kernel, supplied by MathWorks Inc, 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/pm40181062-231-6-21
    Average 90 stars, based on 1 article reviews
    svm with a radial basis function (rbf) kernel - by Bioz Stars, 2026-11
    90/100 stars

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    Article Snippet: 1Hotchkiss Brain Institute and Department of Physiology and Pharmacology, University of Calgary, Calgary, Alberta, Canada.. 2CSM Optogenetics Core Facility, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.. ✉e-mail: jsbains@ucalgary.ca The ability of any organism to respond to an immediate threat is critical for survival.



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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