linear svm approach Search Results


linear svm approach  (MathWorks Inc)
90
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/result/linear svm approach/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
linear svm approach - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier
libsvm toolbox  (MathWorks Inc)
90
MathWorks Inc libsvm toolbox
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.
Libsvm Toolbox, 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/libsvm toolbox/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
libsvm toolbox - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier

Image Search Results


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