Review



sr-svm classifier and all the analyses  (MathWorks Inc)


Bioz Verified Symbol MathWorks Inc is a verified supplier  
  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
    • 90
      Buy from Supplier

    Structured Review

    MathWorks Inc sr-svm classifier and all the analyses
    Sr Svm Classifier And All The Analyses, 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+classifiers/us12333397-256-1-10
    Average 90 stars, based on 1 article reviews
    sr-svm classifier and all the analyses - by Bioz Stars, 2026-10
    90/100 stars

    Images

    Related Articles

    other:

    Article Title: Systems and methods for incorporating supplemental shape information in a support vector machine
    Article Snippet: The SR-SVM classifier and all the analyses are implemented in MATLAB R2019b on a 2.90-GHz Intel Core i7-7820HQ CPU with 16-GB RAM running Windows 10.

    Article Title: Ai-assissted multimodal classification of canine and feline (Sub-)Cutaneous tumors using ultrasound, white light and fluorescence imaging
    Article Snippet: Research aims to provide an automated method for the diagnostics of (sub-)cutaneous tumors in canine/feline patients via machine learning (ML) on multimodal imaging data.. To address the limitations of single mode imaging, we have integrated optical imaging, white light (WL) and fluorescence (FL), in tandem with high frequency (~350 MHz) ultrasound (US) imaging.. Combined hybrid approaches allowed to efficiently differentiate between malignant mastocytomas (MCTs), sarcomas (STSs) and benign lipomas (LPs).

    Article Title: StrokeNeXt: an automated stroke classification model using computed tomography and magnetic resonance images.
    Article Snippet: The DFE model employed an SVM classifier, which was identified as the most accu‐ rate shallow classifier within the MATLAB classifica‐ tion learner toolkit.

    Article Title: Machine learning algorithm partially reconfigured on FPGA for an image edge detection system
    Article Snippet: The SVM classifier was also compiled with MATLAB and processed in CPU (13th GEN INTEL® CORETM i9-13900KF (24 cores)), GPU (NVIDIA GeForce RTX 4090), and both, respectively.

    Article Title: Multilayer network-based channel selection for motor imagery brain-computer interface.
    Article Snippet: Objective.. The number of electrode channels in a motor imagery-based brain–computer interface (MI-BCI) system influences not only its decoding performance, but also its convenience for use in applications.. Although many channel selection methods have been proposed in the literature, they are usually based on the univariate features of a single channel.

    Article Title: StrokeNeXt: an automated stroke classification model using computed tomography and magnetic resonance images
    Article Snippet: The DFE model employed an SVM classifier, which was identified as the most accurate shallow classifier within the MATLAB classification learner toolkit.

    Article Title: Optical properties of cotton and mulching film and feature bands selection in the 400 to 1120 nm range
    Article Snippet: Cotton is prone to being mixed with mulching film during the harvesting and packing process in China, which can significantly decrease the quality of cotton fiber and impact the quality of subsequent textile products.. Mulching film is a translucent material that makes it challenging to detect optically, which presents an urgent challenge for cotton quality testing due to the lack of research on its detection mechanism.. In this study, the absorption coefficient (μa), reduced scattering coefficient (μs’), and scattering anisotropy factor (g) of cotton lint and mulching film in the spectral region of 400–1120 nm were obtained using an integrating sphere-based spectroscopic measurement system.

    Transformation Assay:

    Article Title: The human hypothalamus coordinates switching between different survival actions
    Article Snippet: .. Note that the output of the SVM classifier was transformed to the probabilistic output (between 0 and 1) by applying the Matlab function “FitPosterior.” Age and sex were entered as covariates and the subject, and run number (only in experiment 2) were used as the random-effect variables. ..



    Similar Products

    svm classifier  (Cortical Dynamics)
    86
    Cortical Dynamics svm classifier
    Comparative biomarker performance in <t>SVM</t> classification. SVM models leveraging dynamic functional connectivity (dFC) demonstrated superior classification performance (C, F), outperforming models based on regional <t>indices</t> <t>PerAF</t> (A, D) and dALFF (B, E). For each biomarker, the grid‐search optimized parameters (top) and validation ROC curves (bottom) are shown, underscoring dFC as a highly discriminative feature for identifying disease‐specific neural signatures.
    Svm Classifier, supplied by Cortical Dynamics, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/svm+classifiers/classifier+svm/pmc13068037-49-13-4
    Average 86 stars, based on 1 article reviews
    svm classifier - by Bioz Stars, 2026-10
    86/100 stars
    		  Buy from Supplier
    linear svm classifiers  (MathWorks Inc)
    96
    MathWorks Inc linear svm classifiers
    Comparative biomarker performance in <t>SVM</t> classification. SVM models leveraging dynamic functional connectivity (dFC) demonstrated superior classification performance (C, F), outperforming models based on regional <t>indices</t> <t>PerAF</t> (A, D) and dALFF (B, E). For each biomarker, the grid‐search optimized parameters (top) and validation ROC curves (bottom) are shown, underscoring dFC as a highly discriminative feature for identifying disease‐specific neural signatures.
    Linear Svm Classifiers, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/svm+classifiers/Statistics+and+Machine+Learning+Toolbox/bio_rxiv__64898__2026__03__20__712287-168-13-21
    Average 96 stars, based on 1 article reviews
    linear svm classifiers - by Bioz Stars, 2026-10
    96/100 stars
    		  Buy from Supplier
    svm classifier  (Keerthana Industries)
    90
    Keerthana Industries svm classifier
    Comparative biomarker performance in <t>SVM</t> classification. SVM models leveraging dynamic functional connectivity (dFC) demonstrated superior classification performance (C, F), outperforming models based on regional <t>indices</t> <t>PerAF</t> (A, D) and dALFF (B, E). For each biomarker, the grid‐search optimized parameters (top) and validation ROC curves (bottom) are shown, underscoring dFC as a highly discriminative feature for identifying disease‐specific neural signatures.
    Svm Classifier, supplied by Keerthana Industries, 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+classifiers/hybrid+cnn+models/pm40578097-206-1-27
    Average 90 stars, based on 1 article reviews
    svm classifier - by Bioz Stars, 2026-10
    90/100 stars
    		  Buy from Supplier
    sr-svm classifier and all the analyses  (MathWorks Inc)
    90
    MathWorks Inc sr-svm classifier and all the analyses
    Comparative biomarker performance in <t>SVM</t> classification. SVM models leveraging dynamic functional connectivity (dFC) demonstrated superior classification performance (C, F), outperforming models based on regional <t>indices</t> <t>PerAF</t> (A, D) and dALFF (B, E). For each biomarker, the grid‐search optimized parameters (top) and validation ROC curves (bottom) are shown, underscoring dFC as a highly discriminative feature for identifying disease‐specific neural signatures.
    Sr Svm Classifier And All The Analyses, 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+classifiers/us12333397-256-1-10
    Average 90 stars, based on 1 article reviews
    sr-svm classifier and all the analyses - by Bioz Stars, 2026-10
    90/100 stars
    		  Buy from Supplier
    svm classifier  (MathWorks Inc)
    90
    MathWorks Inc svm classifier
    Comparative biomarker performance in <t>SVM</t> classification. SVM models leveraging dynamic functional connectivity (dFC) demonstrated superior classification performance (C, F), outperforming models based on regional <t>indices</t> <t>PerAF</t> (A, D) and dALFF (B, E). For each biomarker, the grid‐search optimized parameters (top) and validation ROC curves (bottom) are shown, underscoring dFC as a highly discriminative feature for identifying disease‐specific neural signatures.
    Svm Classifier, 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+classifiers/pm40474125-271-5-19
    Average 90 stars, based on 1 article reviews
    svm classifier - by Bioz Stars, 2026-10
    90/100 stars
    		  Buy from Supplier
    svm classifier  (CH Instruments)
    90
    CH Instruments svm classifier
    Comparative biomarker performance in <t>SVM</t> classification. SVM models leveraging dynamic functional connectivity (dFC) demonstrated superior classification performance (C, F), outperforming models based on regional <t>indices</t> <t>PerAF</t> (A, D) and dALFF (B, E). For each biomarker, the grid‐search optimized parameters (top) and validation ROC curves (bottom) are shown, underscoring dFC as a highly discriminative feature for identifying disease‐specific neural signatures.
    Svm Classifier, supplied by CH Instruments, 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+classifiers/svm+classifier/pmc12092891-88-22-25
    Average 90 stars, based on 1 article reviews
    svm classifier - by Bioz Stars, 2026-10
    90/100 stars
    		  Buy from Supplier
    hybridized densenet201 and mobilenet with svm classifier  (Keerthana Industries)
    90
    Keerthana Industries hybridized densenet201 and mobilenet with svm classifier
    Comparative biomarker performance in <t>SVM</t> classification. SVM models leveraging dynamic functional connectivity (dFC) demonstrated superior classification performance (C, F), outperforming models based on regional <t>indices</t> <t>PerAF</t> (A, D) and dALFF (B, E). For each biomarker, the grid‐search optimized parameters (top) and validation ROC curves (bottom) are shown, underscoring dFC as a highly discriminative feature for identifying disease‐specific neural signatures.
    Hybridized Densenet201 And Mobilenet With Svm Classifier, supplied by Keerthana Industries, 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+classifiers/hybrid+cnn+models/pmc12092923-16-22-0
    Average 90 stars, based on 1 article reviews
    hybridized densenet201 and mobilenet with svm classifier - by Bioz Stars, 2026-10
    90/100 stars
    		  Buy from Supplier
    linear svm classifier  (MathWorks Inc)
    90
    MathWorks Inc linear svm classifier
    Comparative biomarker performance in <t>SVM</t> classification. SVM models leveraging dynamic functional connectivity (dFC) demonstrated superior classification performance (C, F), outperforming models based on regional <t>indices</t> <t>PerAF</t> (A, D) and dALFF (B, E). For each biomarker, the grid‐search optimized parameters (top) and validation ROC curves (bottom) are shown, underscoring dFC as a highly discriminative feature for identifying disease‐specific neural signatures.
    Linear Svm Classifier, 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+classifiers/pm40446048-326-6-12
    Average 90 stars, based on 1 article reviews
    linear svm classifier - by Bioz Stars, 2026-10
    90/100 stars
    		  Buy from Supplier
    svm classifier  (Kaggle Inc)
    90
    Kaggle Inc svm classifier
    Comparative biomarker performance in <t>SVM</t> classification. SVM models leveraging dynamic functional connectivity (dFC) demonstrated superior classification performance (C, F), outperforming models based on regional <t>indices</t> <t>PerAF</t> (A, D) and dALFF (B, E). For each biomarker, the grid‐search optimized parameters (top) and validation ROC curves (bottom) are shown, underscoring dFC as a highly discriminative feature for identifying disease‐specific neural signatures.
    Svm Classifier, supplied by Kaggle 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+classifiers/svm/pmc12127516-757-8-22
    Average 90 stars, based on 1 article reviews
    svm classifier - by Bioz Stars, 2026-10
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    Comparative biomarker performance in SVM classification. SVM models leveraging dynamic functional connectivity (dFC) demonstrated superior classification performance (C, F), outperforming models based on regional indices PerAF (A, D) and dALFF (B, E). For each biomarker, the grid‐search optimized parameters (top) and validation ROC curves (bottom) are shown, underscoring dFC as a highly discriminative feature for identifying disease‐specific neural signatures.

    Journal: CNS Neuroscience & Therapeutics

    Article Title: Decoding Post‐Stroke Cognitive Impairment After Acute Basal Ganglia Infarction: The Synergistic Role of Functional Segregation and Integration in an SVM fMRI Framework

    doi: 10.1002/cns.70871

    Figure Lengend Snippet: Comparative biomarker performance in SVM classification. SVM models leveraging dynamic functional connectivity (dFC) demonstrated superior classification performance (C, F), outperforming models based on regional indices PerAF (A, D) and dALFF (B, E). For each biomarker, the grid‐search optimized parameters (top) and validation ROC curves (bottom) are shown, underscoring dFC as a highly discriminative feature for identifying disease‐specific neural signatures.

    Article Snippet: PSCI patients exhibit altered cerebellar‐cortical dynamics in PerAF, dALFF, and dFC, and an SVM classifier based on dFC features achieves 94.52% accuracy and 0.98 AUC, outperforming single‐metric models.

    Techniques: Biomarker Discovery, Functional Assay

    Enhanced diagnostic classification using combined biomarkers. Integration of multimodal neuroimaging metrics (PerAF, dALFF, dFC) yields a powerful classifier for PSCI. The SVM model, optimized via grid search (A), achieves superior discriminatory performance, as evidenced by the ROC curve in (B), outperforming models based on single metrics.

    Journal: CNS Neuroscience & Therapeutics

    Article Title: Decoding Post‐Stroke Cognitive Impairment After Acute Basal Ganglia Infarction: The Synergistic Role of Functional Segregation and Integration in an SVM fMRI Framework

    doi: 10.1002/cns.70871

    Figure Lengend Snippet: Enhanced diagnostic classification using combined biomarkers. Integration of multimodal neuroimaging metrics (PerAF, dALFF, dFC) yields a powerful classifier for PSCI. The SVM model, optimized via grid search (A), achieves superior discriminatory performance, as evidenced by the ROC curve in (B), outperforming models based on single metrics.

    Article Snippet: PSCI patients exhibit altered cerebellar‐cortical dynamics in PerAF, dALFF, and dFC, and an SVM classifier based on dFC features achieves 94.52% accuracy and 0.98 AUC, outperforming single‐metric models.

    Techniques: Diagnostic Assay