Review



rbf (radial basis kernel) function  (MathWorks Inc)


Bioz Verified Symbol MathWorks Inc is a verified supplier  
  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
    • 90
      Buy from Supplier

    Structured Review

    MathWorks Inc rbf (radial basis kernel) function
    Rbf (Radial Basis Kernel) 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/product/radial+basis+kernel+function+(rbf)/pm40425716-125-4-15
    Average 90 stars, based on 1 article reviews
    rbf (radial basis kernel) function - by Bioz Stars, 2026-11
    90/100 stars

    Images

    Related Articles

    other:

    Article Title: Ultra-thin multifocal integral LED-projector based on aspherical microlens arrays
    Article Snippet: Although the LUT generation by RBF method is time consuming (30 seconds in MATLAB for generating a sub-image with resolution of 800×800 pixels).

    Article Title: Radial Basis Functional Model of Multi-Point Dieless Forming Process for Springback Reduction and Compensation
    Article Snippet: After developing the prediction model using RBF as a function of curvature radius, punch holding time, and blank thickness, the study employed genetic algorithm on MATLAB to determine the global optimal value of the given objective.

    Article Title: Selecting Useful Groups of Features in a Connectionist Framework
    Article Snippet: We use the Mathworks neural network toolbox implementations of RBF and MLP.

    Article Title: Differentiation of canine and feline neoplasms using multi-modal imaging and machine learning.
    Article Snippet: Classifier was implemented with RBF (radial basis kernel) function, other hyper-parameters were set default (in Matlab); fitPosterior function was used in the post-processing to obtain classification probabilities.

    Article Title: A comparative study of the scalability of alternative metamodelling techniques
    Article Snippet: Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform.. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content.. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis.

    Article Title: Computational fluid dynamics analysis and optimisation of polymerase chain reaction thermal flow systems
    Article Snippet: The RBF method uses the Multi-Objective Genetic Algorithm (MOGA) solver in MATLAB, whereas the NN methods use the SciPy.optimization module in Python based on a simple weighting between the competing objectives.

    Article Title: Developing a model to predict the start of combustion in HCCI engine using ANN-GA approach
    Article Snippet: The combustion process in Homogeneous Charge Compression Ignition Engines (HCCI) is one of the new methods of futuristic combustion technologies.. Since there is no direct operator for the start of the combustion (SOC) of these engines, air-fuel mixture properties at the moment of entering the combustion chamber, specifies the ignition timing.. In HCCI engines, the ignition timing is the most crucial factor in determining other engine operating characteristics such as power output, pollution, and fuel consumption.

    Article Title: A Support Vector Machine-Assisted Metabolomics Approach for Non-Targeted Screening of Multi-Class Pesticides and Veterinary Drugs in Maize.
    Article Snippet: Therefore, we chose SVMRFE under RBF to run the program in MATLAB software (Version R2019b).



    Similar Products

    rbf (radial basis kernel) function  (MathWorks Inc)
    90
    MathWorks Inc rbf (radial basis kernel) function
    Rbf (Radial Basis Kernel) 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/product/radial+basis+kernel+function+(rbf)/pm40425716-125-4-15
    Average 90 stars, based on 1 article reviews
    rbf (radial basis kernel) function - by Bioz Stars, 2026-11
    90/100 stars
    		  Buy from Supplier
    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/product/radial+basis+kernel+function+(rbf)/pm40181062-231-6-21
    Average 90 stars, based on 1 article reviews
    svm with a radial basis function (rbf) kernel - by Bioz Stars, 2026-11
    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/product/radial+basis+kernel+function+(rbf)/pmc10977878-376-5-0
    Average 90 stars, based on 1 article reviews
    svm kernels gaussian (radial basis function (rbf) - by Bioz Stars, 2026-11
    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/product/radial+basis+kernel+function+(rbf)/svm/pm35697648-48-8-23
    Average 90 stars, based on 1 article reviews
    svm with certain kernels (e.g. polynomial and radial basis function, rbf) - by Bioz Stars, 2026-11
    90/100 stars
    		  Buy from Supplier
    radial basis kernel function (rbf)  (RStudio)
    90
    RStudio radial basis kernel function (rbf)
    Radial Basis Kernel Function (Rbf), supplied by RStudio, 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/radial+basis+kernel+function+(rbf)/radial+basis+kernel+function++rbf+/10__3390_slash_hydrology10030066-193-2-1
    Average 90 stars, based on 1 article reviews
    radial basis kernel function (rbf) - by Bioz Stars, 2026-11
    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/product/radial+basis+kernel+function+(rbf)/pmc08290561-82-9-32
    Average 90 stars, based on 1 article reviews
    svm classifications with a radial basis function kernel (rbf-svm) - by Bioz Stars, 2026-11
    90/100 stars
    		  Buy from Supplier
    simulated annealing radial basis function (sa-rbf) with kernel extreme learning  (MathWorks Inc)
    90
    MathWorks Inc simulated annealing radial basis function (sa-rbf) with kernel extreme learning
    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
    Simulated Annealing Radial Basis Function (Sa Rbf) With Kernel Extreme Learning, 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/radial+basis+kernel+function+(rbf)/10__3390_slash_su13041629-262-17-22
    Average 90 stars, based on 1 article reviews
    simulated annealing radial basis function (sa-rbf) with kernel extreme learning - by Bioz Stars, 2026-11
    90/100 stars
    		  Buy from Supplier
    radial basis function (rbf) kernel  (MathWorks Inc)
    90
    MathWorks Inc radial basis function (rbf) 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
    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/product/radial+basis+kernel+function+(rbf)/10__1007_slash_s10479___020___03754___x-209-5-20
    Average 90 stars, based on 1 article reviews
    radial basis function (rbf) kernel - by Bioz Stars, 2026-11
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    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