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linear svm classifier matlab function fitcsvm with default settings  (MathWorks Inc)


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    MathWorks Inc linear svm classifier matlab function fitcsvm with default settings
    Linear Svm Classifier Matlab Function Fitcsvm With Default Settings, 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/linear+svm+classifier+matlab+function+fitcsvm/us11633145-263-18-21
    Average 90 stars, based on 1 article reviews
    linear svm classifier matlab function fitcsvm with default settings - by Bioz Stars, 2026-11
    90/100 stars

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    Article Title: Non-invasive suppression of essential tremor via phase-locked disruption of its temporal coherence
    Article Snippet: The feature space was partitioned, i.e. classified, using a linear SVM classifier, implemented with the classify function of MATLAB’s Statistics Toolbox , which returned a threshold that optimally separated the two classes, i.e. ‘responders’ and ‘non-responders’ time-series.

    Article Title: Decoding predicted future states from the brain’s “physics engine”
    Article Snippet: For each layer, we trained a linear SVM classifier (fitclinear function in MATLAB) for contact versus noncontact decoding on the feature representations of either the perceived roll or throw scenario and computed the decoding accuracy on the held-out perceived and predicted scenarios.

    Article Title: Decoding predicted future states from the brain's "physics engine".
    Article Snippet: For each layer, we trained a linear SVM classifier (fitclinear function in MATLAB) for contact versus noncontact decoding on the feature representations of either the perceived roll or throw scenario and computed the decoding accuracy on the held- out perceived and predicted scenarios.

    Article Title: Asymmetries around the visual field: From retina to cortex to behavior
    Article Snippet: We used a linear SVM classifier as implemented in MATLAB’s fitcsvm with 10-fold cross-validation and built-in z-scoring.

    Article Title: Cardiac signals inform auditory regularity processing in the absence of consciousness.
    Article Snippet: 122 No. 20 e2505454122 https://doi.org/10.1073/pnas.2505454122 9 of 10 Multivariate decoding was based on a linear SVM classifier, as implemented in MATLAB (fitclinear.m).

    Article Title: Cortical Transformation of Stimulus Space in Order to Linearize a Linearly Inseparable Task.
    Article Snippet: To identify brain regions that were sensitive to button responses but not category information, for each ROI in each participant we trained a linear SVM classifier (MATLAB fitcsvm function) in one task to distinguish between Button 1 and Button 2 presses and tested it on another task.

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    Article Title: Specimen imaging with x-ray and optical measurement
    Article Snippet: .. Texture feature vectors associated with one benign tissue subtype and one malignant tissue subtype were classified using a linear SVM classifier (MATLAB function fitcsvm with default settings) with correlation-based feature selection. ..



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    linear svm classifier matlab function fitcsvm  (MathWorks Inc)
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    MathWorks Inc linear svm classifier matlab function fitcsvm
    Classification performance using subdiffuse reflectance ( f x = 1.37 mm − 1 , λ = 490 nm ) and evaluated by ROC curve analysis for (a) adipose, (b) connective, and (c) FCD, versus the <t>three</t> <t>malignant</t> tissue subtypes. Classification used a linear <t>SVM</t> classifier, correlation-based feature selection with grid-searching, fivefold CV, sample size matching, and averaging over n = 100 iterations. At most 11 texture features were included in each classification.
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    Classification performance using subdiffuse reflectance ( f x = 1.37 mm − 1 , λ = 490 nm ) and evaluated by ROC curve analysis for (a) adipose, (b) connective, and (c) FCD, versus the three malignant tissue subtypes. Classification used a linear SVM classifier, correlation-based feature selection with grid-searching, fivefold CV, sample size matching, and averaging over n = 100 iterations. At most 11 texture features were included in each classification.

    Journal: Journal of Biomedical Optics

    Article Title: Structured light imaging for breast-conserving surgery, part II: texture analysis and classification

    doi: 10.1117/1.JBO.24.9.096003

    Figure Lengend Snippet: Classification performance using subdiffuse reflectance ( f x = 1.37 mm − 1 , λ = 490 nm ) and evaluated by ROC curve analysis for (a) adipose, (b) connective, and (c) FCD, versus the three malignant tissue subtypes. Classification used a linear SVM classifier, correlation-based feature selection with grid-searching, fivefold CV, sample size matching, and averaging over n = 100 iterations. At most 11 texture features were included in each classification.

    Article Snippet: Texture feature vectors (11 total features, detailed in ) associated with one benign tissue subtype and one malignant tissue subtype were classified using a linear SVM classifier (MATLAB function fitcsvm with default settings ) with correlation-based feature selection.

    Techniques: Selection

    Summary of classification performance using subdiffuse SFDI-derived reflectance, a linear SVM classifier, correlation-based feature selection with grid searching for the optimal feature set, and a total of 11 possible texture features. Accuracy 95% confidence intervals are given in parentheses.

    Journal: Journal of Biomedical Optics

    Article Title: Structured light imaging for breast-conserving surgery, part II: texture analysis and classification

    doi: 10.1117/1.JBO.24.9.096003

    Figure Lengend Snippet: Summary of classification performance using subdiffuse SFDI-derived reflectance, a linear SVM classifier, correlation-based feature selection with grid searching for the optimal feature set, and a total of 11 possible texture features. Accuracy 95% confidence intervals are given in parentheses.

    Article Snippet: Texture feature vectors (11 total features, detailed in ) associated with one benign tissue subtype and one malignant tissue subtype were classified using a linear SVM classifier (MATLAB function fitcsvm with default settings ) with correlation-based feature selection.

    Techniques: Selection