Review



linear svm classifiers  (MathWorks Inc)


Bioz Verified Symbol MathWorks Inc is a verified supplier  
  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
    • 96
      Buy from Supplier

    Structured Review

    MathWorks Inc linear svm classifiers
    Linear Svm Classifiers, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 2323 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 2323 article reviews
    linear svm classifiers - by Bioz Stars, 2026-10
    96/100 stars

    Images

    Related Articles

    Selection:

    Article Title: Gait signature changes with walking speed are similar among able-bodied young adults despite persistent individual-specific differences
    Article Snippet: .. For each data type, eight distinct SVM classifiers were trained on a progressive selection of one to eight random trials per individual over 140 total runs using built in MATLAB function ‘templateSVM’ with standardized features. ..

    other:

    Article Title: Detection and Classification of Novel Renal Histologic Phenotypes Using Deep Neural Networks
    Article Snippet: All SVM classifiers were trained with the MATLAB Statistics and Machine Learning Toolbox (Mathworks, Natick, MA).

    Article Title: Classifying intracranial stenosis disease severity from functional MRI data using machine learning
    Article Snippet: 22 SVM classifiers were trained in MATLAB using the fitcsvm function with a radial bias function kernel and fitcsvm default parameters, including kernel scale set to 1, box constraints set to 1, standardized data, and solving with sequential minimal optimization.

    Biomarker Discovery:

    Article Title: Implications of Crashes Involving Roadway Objects for Machine Vision-Based Driving Systems
    Article Snippet: Crashes involving roadway objects can cause severe injuries and property damage.. Utilizing data from the Second Strategic Highway Research Program (SHRP 2) naturalistic driving study (NDS), this study investigated crashes involving roadway objects and their implications for the potential of machine vision-based driving systems in preventing such crashes.. A comprehensive statistical and machine learning analysis was first conducted to identify major factors affecting the occurrence and severity of such events.

    Generated:

    Article Title: Connectomic Profiling Identifies Responders to Vagus Nerve Stimulation.
    Article Snippet: Authors: Karim Mithani, MEng 1 , Mirriam Mikhail, MEng 1 , Benjamin R. Morgan, MASc 1 , Simeon Wong, BASc 2 , Alexander G. Weil, MD 3 , Sylvain Deschenes, PhD 3 , Shelly Wang, MD, MPH 4 , Byron Bernal, MD 5 , Magno R. Guillen, PhD 5 , Ayako Ochi, MD, PhD 6 ; Hiroshi Otsubo, MD, PhD 6 , Ivanna Yau, MN, NP-Pediatrics 6 , William Lo, MD 7 , Elizabeth Pang, PhD 6 , Stephanie Holowka, M.R.T. (R) 8 ; O. Carter Snead, MD 6 ; Elizabeth Donner, MD 6 , James T. Rutka, MD, PhD 7 , Cristina Go, MD 6 , Elysa Widjaja, MD, MPH 8 , George M. Ibrahim, MD, PhD 2,7



    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