Review





Similar Products

svm with a radial basis function (rbf) kernel  (MathWorks Inc)
90
MathWorks Inc svm with a radial basis function (rbf) kernel
Svm With A Radial Basis Function (Rbf) Kernel, 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 with a radial basis function (rbf) kernel/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
svm with a radial basis function (rbf) kernel - by Bioz Stars, 2026-04
90/100 stars
  Buy from Supplier
svm radial basis function kernel  (MathWorks Inc)
90
MathWorks Inc svm radial basis function kernel
Svm Radial Basis Function Kernel, 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 radial basis function kernel/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
svm radial basis function kernel - by Bioz Stars, 2026-04
90/100 stars
  Buy from Supplier
svm with a gaussian or radial basis function kernel  (MathWorks Inc)
90
MathWorks Inc svm with a gaussian or radial basis function kernel
Svm With A Gaussian Or Radial Basis Function Kernel, 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 with a gaussian or radial basis function kernel/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
svm with a gaussian or radial basis function kernel - by Bioz Stars, 2026-04
90/100 stars
  Buy from Supplier
svm kernels gaussian (radial basis function (rbf)  (MathWorks Inc)
90
MathWorks Inc svm kernels gaussian (radial basis function (rbf)
Svm Kernels Gaussian (Radial Basis Function (Rbf), 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 kernels gaussian (radial basis function (rbf)/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
svm kernels gaussian (radial basis function (rbf) - by Bioz Stars, 2026-04
90/100 stars
  Buy from Supplier
svm models with a gaussian kernel (radial basis) function  (MathWorks Inc)
90
MathWorks Inc svm models with a gaussian kernel (radial basis) function
Svm Models With A Gaussian Kernel (Radial Basis) Function, 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 models with a gaussian kernel (radial basis) function/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
svm models with a gaussian kernel (radial basis) function - by Bioz Stars, 2026-04
90/100 stars
  Buy from Supplier
svm with certain kernels (e.g. polynomial and radial basis function, rbf)  (Drucker Diagnostics)
90
Drucker Diagnostics svm with certain kernels (e.g. polynomial and radial basis function, rbf)
Svm With Certain Kernels (E.G. Polynomial And Radial Basis Function, Rbf), supplied by Drucker Diagnostics, 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 with certain kernels (e.g. polynomial and radial basis function, rbf)/product/Drucker Diagnostics
Average 90 stars, based on 1 article reviews
svm with certain kernels (e.g. polynomial and radial basis function, rbf) - by Bioz Stars, 2026-04
90/100 stars
  Buy from Supplier
svm radial kernel  (Millipore)
90
Millipore svm radial kernel
Machine learning model specifics.
Svm Radial Kernel, supplied by Millipore, 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 radial kernel/product/Millipore
Average 90 stars, based on 1 article reviews
svm radial kernel - by Bioz Stars, 2026-04
90/100 stars
  Buy from Supplier
svm classifications with a radial basis function kernel (rbf-svm)  (MathWorks Inc)
90
MathWorks Inc svm classifications with a radial basis function kernel (rbf-svm)
Results of support vector machine analysis to discriminate cancer from normal tissue in the prostate transition zone. Left: ROC plot of combinations of <t>multiparametric</t> <t>MRI</t> values. T2WI + DWI is in blue and T2WI + DWI + DCE in green. Without MRSI is represented by a dashed-line and with MRSI by a solid-line. Only multiparametric MRI parameters with statistically significant differences between cancer and normal tissues ( p < 0.05) were used for classification. Right: Bar charts of AUC values, sensitivity and specificity of the corresponding <t>RBF-SVM</t> models. McNemar test was used for pairwise comparison of sensitivity and specificities of models and Delong test was calculated for pairwise comparison of AUC of models. ** p < 0.01
Svm Classifications With A Radial Basis Function Kernel (Rbf Svm), 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 classifications with a radial basis function kernel (rbf-svm)/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
svm classifications with a radial basis function kernel (rbf-svm) - by Bioz Stars, 2026-04
90/100 stars
  Buy from Supplier
nonlinear svms with gaussian radial basis function kernel  (MathWorks Inc)
90
MathWorks Inc nonlinear svms with gaussian radial basis function kernel
Results of support vector machine analysis to discriminate cancer from normal tissue in the prostate transition zone. Left: ROC plot of combinations of <t>multiparametric</t> <t>MRI</t> values. T2WI + DWI is in blue and T2WI + DWI + DCE in green. Without MRSI is represented by a dashed-line and with MRSI by a solid-line. Only multiparametric MRI parameters with statistically significant differences between cancer and normal tissues ( p < 0.05) were used for classification. Right: Bar charts of AUC values, sensitivity and specificity of the corresponding <t>RBF-SVM</t> models. McNemar test was used for pairwise comparison of sensitivity and specificities of models and Delong test was calculated for pairwise comparison of AUC of models. ** p < 0.01
Nonlinear Svms With Gaussian Radial Basis Function Kernel, 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/nonlinear svms with gaussian radial basis function kernel/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
nonlinear svms with gaussian radial basis function kernel - by Bioz Stars, 2026-04
90/100 stars
  Buy from Supplier

Image Search Results


Machine learning model specifics.

Journal: Cancers

Article Title: Factors Associated with Thyroid-Related Adverse Events in Patients Receiving PD-1 or PD-L1 Inhibitors Using Machine Learning Models

doi: 10.3390/cancers13215465

Figure Lengend Snippet: Machine learning model specifics.

Article Snippet: SVM with radial kernel , Sigma: 2 −15 , 2 −13 , 2 −11 , 2 −9 , 2 −7 , 2 −5 , 2 −3 , 2 −1 , 2, 2 3 , Sigma: 0.125.

Techniques:

Results of support vector machine analysis to discriminate cancer from normal tissue in the prostate transition zone. Left: ROC plot of combinations of multiparametric MRI values. T2WI + DWI is in blue and T2WI + DWI + DCE in green. Without MRSI is represented by a dashed-line and with MRSI by a solid-line. Only multiparametric MRI parameters with statistically significant differences between cancer and normal tissues ( p < 0.05) were used for classification. Right: Bar charts of AUC values, sensitivity and specificity of the corresponding RBF-SVM models. McNemar test was used for pairwise comparison of sensitivity and specificities of models and Delong test was calculated for pairwise comparison of AUC of models. ** p < 0.01

Journal: Journal of Biomedical Science

Article Title: Diagnosis of transition zone prostate cancer by multiparametric MRI: added value of MR spectroscopic imaging with sLASER volume selection

doi: 10.1186/s12929-021-00750-6

Figure Lengend Snippet: Results of support vector machine analysis to discriminate cancer from normal tissue in the prostate transition zone. Left: ROC plot of combinations of multiparametric MRI values. T2WI + DWI is in blue and T2WI + DWI + DCE in green. Without MRSI is represented by a dashed-line and with MRSI by a solid-line. Only multiparametric MRI parameters with statistically significant differences between cancer and normal tissues ( p < 0.05) were used for classification. Right: Bar charts of AUC values, sensitivity and specificity of the corresponding RBF-SVM models. McNemar test was used for pairwise comparison of sensitivity and specificities of models and Delong test was calculated for pairwise comparison of AUC of models. ** p < 0.01

Article Snippet: We developed a machine learning platform for mp-MRI including support vector machine (SVM) classifications with a radial basis function kernel (RBF-SVM) and area under receiver operator characteristic (ROC) analyses using an in-house Matlab routine to evaluate the diagnostic performance of models with different parametric combinations: T2WI + DWI, T2WI + DWI + DCE, T2WI + DWI + MRSI, and T2WI + DWI + DCE + MRSI.

Techniques: Plasmid Preparation, Comparison

Results of support vector machine analysis to separate tumor aggressiveness classes. Left side: ROC curves of the six RBF-SVM models for low-risk vs high-risk cancer, low-risk vs intermediate-risk cancer and intermediate-risk vs high-risk cancer. A leave-one-out cross-validation technique was used for the combined ADC and K trans (dashed line) and all the combined ADC, K trans and metabolite ratios (solid line) with a significant difference between the two groups ( p < 0.05). Right side: bar charts of AUC values, sensitivity and specificity of the corresponding RBF-SVM models. McNemar test was used for pairwise comparison of sensitivity and specificities of models and Delong test was calculated for pairwise comparison of AUC of models. ** p < 0.01 and * p < 0.05

Journal: Journal of Biomedical Science

Article Title: Diagnosis of transition zone prostate cancer by multiparametric MRI: added value of MR spectroscopic imaging with sLASER volume selection

doi: 10.1186/s12929-021-00750-6

Figure Lengend Snippet: Results of support vector machine analysis to separate tumor aggressiveness classes. Left side: ROC curves of the six RBF-SVM models for low-risk vs high-risk cancer, low-risk vs intermediate-risk cancer and intermediate-risk vs high-risk cancer. A leave-one-out cross-validation technique was used for the combined ADC and K trans (dashed line) and all the combined ADC, K trans and metabolite ratios (solid line) with a significant difference between the two groups ( p < 0.05). Right side: bar charts of AUC values, sensitivity and specificity of the corresponding RBF-SVM models. McNemar test was used for pairwise comparison of sensitivity and specificities of models and Delong test was calculated for pairwise comparison of AUC of models. ** p < 0.01 and * p < 0.05

Article Snippet: We developed a machine learning platform for mp-MRI including support vector machine (SVM) classifications with a radial basis function kernel (RBF-SVM) and area under receiver operator characteristic (ROC) analyses using an in-house Matlab routine to evaluate the diagnostic performance of models with different parametric combinations: T2WI + DWI, T2WI + DWI + DCE, T2WI + DWI + MRSI, and T2WI + DWI + DCE + MRSI.

Techniques: Plasmid Preparation, Biomarker Discovery, Comparison