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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+fitcsvm+function/us11633145-263-18-21
    Average 90 stars, based on 1 article reviews
    linear svm classifier matlab function fitcsvm with default settings - by Bioz Stars, 2026-10
    90/100 stars

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    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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    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