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linear kernel-based svm  (MathWorks Inc)


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    MathWorks Inc linear kernel-based svm
    Linear Kernel Based Svm, 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/kernel-based+svm/pmc04659640-106-6-9
    Average 90 stars, based on 1 article reviews
    linear kernel-based svm - by Bioz Stars, 2026-10
    90/100 stars

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    other:

    Article Title: Detecting acute bilirubin encephalopathy in neonates based on multimodal MRI images and non-image clinical data.
    Article Snippet: The specified SVM was created by the function of ‘fitcvsm’ in MATLAB 2020b.

    Article Title: Explainable radiomics based on association of histopathological cell density and multiparametric MR radiomic features for high-risk stratification of prostate cancer patients.
    Article Snippet: Objective This study aimed to develop an explainable radiomics model for stratifying prostate cancer (PCa) patients with high-risk disease via investigation of the association between cell density (CD) in the PCa region on histopathological images and multiparametric MR (mpMR) radiomics features.. Materials and methods 137,970 radiomic features were calculated from mpMR images (101 PCa regions of 44 patients), and joint histograms (JHs) were derived from dynamic contrast-enhanced (DCE) images for each PCa region.. The association between CD on histopathological images and its corresponding mpMR radiomic features in PCa regions for various grade groups and the three risk groups was evaluated using Spearman’s correlation coefficient.

    Article Title: Enhanced role of the entorhinal cortex in adapting to increased working memory load
    Article Snippet: Hence, our analysis was based on a linear SVM as the classifier via the LIBSVM package in MATLAB .

    Article Title: Altered neurovascular coupling in depression with childhood maltreatment.
    Article Snippet: Background: Childhood maltreatment (CM) is a well-established risk to major depressive disorder (MDD) worldwide.. However, most previous research has predominantly examined single imaging modalities, ignoring the potential impact of aberrant couplings between cerebral perfusion and neuronal activity in MDD psychopathology.. This study aims to investigate alterations of neurovascular coupling (NVC) in CM-related MDD.

    Article Title: Spatial patterning of chloroplasts and stomata in developing cacao leaves
    Article Snippet: SVM with a Radial Basis Function (RBF) kernel was used for non-linear analysis using MATLAB version 9.9 (R2019a) , .

    Article Title: Mild cognitive impairment is associated with effect of uremic metabolites on gray matter structural changes in end-stage kidney disease.
    Article Snippet: Then the model construction will be performed using SVM in the Classification Learner Tool in MATLAB 2021a.

    Article Title: Two distinct cell types of the medial mammillary body forming segregated subcircuits.
    Article Snippet: The same set of E/M parameters were used for PCA and SVM using MATLAB.

    Biomarker Discovery:

    Article Title: Machine learning for Parkinson’s disease: a comprehensive review of datasets, algorithms, and challenges
    Article Snippet: , Detection of PD using parameter-weighted matrices , • MATLAB , • CNN , • High AUC (ROC) , • Not able to detect at early PD stages • Lack of varied data • Not verifying the model’s effectiveness externally. .. , ML-based PD diagnosis via I-123 FP-CIT scans , • MATLAB , • SVM , • High accuracy • High sensitivity • High specificity , • Applying a relatively small number of patients • Not considering additional parameters • Limited evaluation of diverse methods. .. , [18 F] DOPA PET/CT and CNN for PD classification , • Python , • CNN , • High accuracy • High specificity • High sensitivity , • Not performing a regional analysis • No neuropathologic confirmation.



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    linear kernel-based svm  (MathWorks Inc)
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    MathWorks Inc linear kernel-based svm
    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 <t>RBF</t> 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 <t>SVM.</t> “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
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    Image Search Results


    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