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svm linear classifiers fitcsvm  (MathWorks Inc)


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    MathWorks Inc svm linear classifiers fitcsvm
    Svm Linear Classifiers Fitcsvm, 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/result/svm linear classifiers fitcsvm/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    svm linear classifiers fitcsvm - by Bioz Stars, 2026-03
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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 <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