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linear svm classifiers  (MathWorks Inc)


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    MathWorks Inc linear svm classifiers
    Linear Svm Classifiers, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 2255 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/svm+classifier/Statistics+and+Machine+Learning+Toolbox/bio_rxiv__64898__2026__03__20__712287-168-13-21
    Average 96 stars, based on 2255 article reviews
    linear svm classifiers - by Bioz Stars, 2026-09
    96/100 stars

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    other:

    Article Title: Hilbert-Huang Transform and machine learning based electromechanical analysis of induction machine under power quality disturbances
    Article Snippet: The SVM method is trained with PQ data and classification results using MATLAB classifier and Python software.

    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: Cognitive load recognition in simulated flight missions: an EEG study
    Article Snippet: The traditional method that uses an SVM classifier combining PSD features is implemented in Matlab 2019a.

    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: Brain Tumor Detection
    Article Snippet: Executed in MATLAB, the International Open-Access, Double-Blind, Peer-Reviewed, Refereed, Multidisciplinary Online Journal Volume 4, Issue 6, November 2024 Copyright to IJARSCT DOI: 10.48175/IJARSCT-22518 87 www.ijarsct.co.in Impact Factor: 7.53 SVM classifier categorizes brain tumors into numerous sorts, moving forward by and large framework execution compared to ordinary CNN models.

    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. ..



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    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.

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    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.

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    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.

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    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