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linear svm approach within a library for svms (libsvms) toolkit  (MathWorks Inc)


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    MathWorks Inc linear svm approach within a library for svms (libsvms) toolkit
    Linear Svm Approach Within A Library For Svms (Libsvms) Toolkit, 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+approach/pmc07236549-70-24-29
    Average 90 stars, based on 1 article reviews
    linear svm approach within a library for svms (libsvms) toolkit - by Bioz Stars, 2026-09
    90/100 stars

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    Related Articles

    Plasmid Preparation:

    Article Title: Increased Gray Matter Density and Functional Connectivity of the Pons in Restless Legs Syndrome
    Article Snippet: To clarify whether the identified abnormal features might have potential power for diagnosing RLS, we performed a linear SVM approach within LIBSVM in MATLAB. .. B

    Biomarker Discovery:

    Article Title: Increased Gray Matter Density and Functional Connectivity of the Pons in Restless Legs Syndrome
    Article Snippet: To clarify whether the identified abnormal features might have potential power for diagnosing RLS, we performed a linear SVM approach within LIBSVM in MATLAB. .. B

    Functional Assay:

    Article Title: Increased Gray Matter Density and Functional Connectivity of the Pons in Restless Legs Syndrome
    Article Snippet: To clarify whether the identified abnormal features might have potential power for diagnosing RLS, we performed a linear SVM approach within LIBSVM in MATLAB. .. B



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    linear svm approach within a library for svms (libsvms) toolkit  (MathWorks Inc)
    90
    MathWorks Inc linear svm approach within a library for svms (libsvms) toolkit
    Linear Svm Approach Within A Library For Svms (Libsvms) Toolkit, 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+approach/pmc07236549-70-24-29
    Average 90 stars, based on 1 article reviews
    linear svm approach within a library for svms (libsvms) toolkit - by Bioz Stars, 2026-09
    90/100 stars
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    linear svm approach  (MathWorks Inc)
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    MathWorks Inc linear svm approach
    Multivariate pattern analysis using support vector machine <t>(SVM)</t> was applied to provide provisional evidence to determine whether identified neural indices might serve to <t>distinguish</t> <t>RLS</t> patients from NC. ( A ) We used a leave-one-out cross-validation strategy to estimate the generalization ability of our classifier. Features of gray matter density in pons_2, and functional connectivity between pons_2 and SMA were used. The classification accuracy, specificity, and precision were showed. ( B ) The receiver operating characteristic (ROC) curve. AUC, area under the curve.
    Linear Svm Approach, 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+approach/pmc07102916-90-17-23
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    linear svm approach - by Bioz Stars, 2026-09
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    linear svm approach within a library for svms toolkit  (MathWorks Inc)
    90
    MathWorks Inc linear svm approach within a library for svms toolkit
    Multivariate pattern analysis using LIBSVM was applied to provide provisional evidence to determine whether identified neural indices might serve <t>as</t> <t>biomarkers</t> for diagnosing <t>MDD.</t> The regional GMV of amygdala, FC between SF amygdala and FFA and effective connectivity from FFA to SF amygdala were used as the features for classification. We used a leave-one-out cross-validation strategy to estimate the generalization ability of our classifier. The classification accuracy, sensitivity and specificity were showed.
    Linear Svm Approach Within A Library For Svms Toolkit, 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+approach/pmc05716231-125-13-27
    Average 90 stars, based on 1 article reviews
    linear svm approach within a library for svms toolkit - by Bioz Stars, 2026-09
    90/100 stars
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    Multivariate pattern analysis using support vector machine (SVM) was applied to provide provisional evidence to determine whether identified neural indices might serve to distinguish RLS patients from NC. ( A ) We used a leave-one-out cross-validation strategy to estimate the generalization ability of our classifier. Features of gray matter density in pons_2, and functional connectivity between pons_2 and SMA were used. The classification accuracy, specificity, and precision were showed. ( B ) The receiver operating characteristic (ROC) curve. AUC, area under the curve.

    Journal: Nature and Science of Sleep

    Article Title: Increased Gray Matter Density and Functional Connectivity of the Pons in Restless Legs Syndrome

    doi: 10.2147/NSS.S239852

    Figure Lengend Snippet: Multivariate pattern analysis using support vector machine (SVM) was applied to provide provisional evidence to determine whether identified neural indices might serve to distinguish RLS patients from NC. ( A ) We used a leave-one-out cross-validation strategy to estimate the generalization ability of our classifier. Features of gray matter density in pons_2, and functional connectivity between pons_2 and SMA were used. The classification accuracy, specificity, and precision were showed. ( B ) The receiver operating characteristic (ROC) curve. AUC, area under the curve.

    Article Snippet: To clarify whether the identified abnormal features might have potential power for diagnosing RLS, we performed a linear SVM approach within LIBSVM in MATLAB.

    Techniques: Plasmid Preparation, Biomarker Discovery, Functional Assay

    Multivariate pattern analysis using LIBSVM was applied to provide provisional evidence to determine whether identified neural indices might serve as biomarkers for diagnosing MDD. The regional GMV of amygdala, FC between SF amygdala and FFA and effective connectivity from FFA to SF amygdala were used as the features for classification. We used a leave-one-out cross-validation strategy to estimate the generalization ability of our classifier. The classification accuracy, sensitivity and specificity were showed.

    Journal: Social Cognitive and Affective Neuroscience

    Article Title: Electroconvulsive therapy selectively enhanced feedforward connectivity from fusiform face area to amygdala in major depressive disorder

    doi: 10.1093/scan/nsx100

    Figure Lengend Snippet: Multivariate pattern analysis using LIBSVM was applied to provide provisional evidence to determine whether identified neural indices might serve as biomarkers for diagnosing MDD. The regional GMV of amygdala, FC between SF amygdala and FFA and effective connectivity from FFA to SF amygdala were used as the features for classification. We used a leave-one-out cross-validation strategy to estimate the generalization ability of our classifier. The classification accuracy, sensitivity and specificity were showed.

    Article Snippet: To explore whether the identified neural indices might serve as biomarkers for diagnosing MDD, a linear SVM approach within a library for SVMs (LIBSVMs) toolkit running on MATLAB ( ) was performed.

    Techniques: Biomarker Discovery