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kernel-based svm  (IEEE Access)

 
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    IEEE Access kernel-based svm
    Kernel Based Svm, supplied by IEEE Access, 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/kernel-based+svm/kernel+based+svm/10__1007_slash_s12555___020___0872___0-186-18-24
    Average 90 stars, based on 1 article reviews
    kernel-based svm - by Bioz Stars, 2026-09
    90/100 stars

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    other:

    Article Title: Gradient-based Iterative Parameter Estimation for a Finite Impulse Response System with Saturation Nonlinearity
    Article Snippet: This paper studies the identification problems of a nonlinear finite impulse response system with saturation nonlinearity.. Introducing a symbolic function, an over-parameterization gradient-based iterative algorithm is presented for estimating the parameters of the nonlinear system with saturation nonlinearity.. In order to enhance the computational efficiency, a gradient-based iterative algorithm and a hierarchical gradient-based iterative algorithm are presented for the nonlinear systems.



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    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