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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/product/svm+linear+classifiers+fitcsvm/pm40382316-516-10-18
    Average 90 stars, based on 1 article reviews
    svm linear classifiers fitcsvm - by Bioz Stars, 2026-10
    90/100 stars

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    Activity Assay:

    Article Title: A reduced ability to discriminate social from non-social touch at the circuit level may underlie social avoidance in autism.
    Article Snippet: .. Decoding touch context from neuronal activity and behavior We used SVM linear classifiers (default parameters of fitcsvm in MATLAB 2023b, radial basis function kernel with box constraint parameter = 1 for every classifier and 10-fold cross-validation) to determine how well all neurons, or neurons within a particular cluster, could decode touch context (object vs. social presentations) under voluntaryor forced conditions.Weused activity from80%of trials (64/ 80 of both object and social touch trials) as the training dataset, and the remaining 20% (26/80) was used for testing the classifier’s accuracy. ..



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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