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rbf (radial basis kernel) function  (MathWorks Inc)


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    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/rbf+(radial+basis+kernel)+function/pm40425716-125-4-15
    Average 90 stars, based on 1 article reviews
    rbf (radial basis kernel) function - by Bioz Stars, 2026-10
    90/100 stars

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    Article Title: A Benchmark Study on Error Assessment and Quality Control of CCS Reads Derived from the PacBio RS.
    Article Snippet: For the dataset in this study, we chose a radial basis function (RBF) kernel with the default settings [15] to build the model with the training set of PacBio spike-in CCS reads in Matlab.

    Article Title: An intelligent support system for automatic detection of cerebral vascular accidents from brain CT images.
    Article Snippet: In order to compare the obtained results with a SVM [39], the MATLAB SVM tool with Gaussian RBF (Radial Basis Function) kernel was used.

    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: Wireless sensor network for AI-based flood disaster detection
    Article Snippet: In recent decades, floods have led to massive destruction of human life and material.. Time is of the essence for evacuation, which in turn is determined by early warning systems.. This study proposes a wireless sensor network decision model for the detection of flood disasters by observing changes in weather conditions compared to historical information at a given location.

    Article Title: A convex hull-based data selection method for data driven models
    Article Snippet: The accuracy of classification and regression tasks based on data driven models, such as Neural Networks or Support Vector Machines, relies to a good extent on selecting proper data for designing these models, covering the whole input range in which they will be employed.. The convex hull algorithm can be applied as a method for data selection; however the use of conventional implementations of this method in high dimensions, due to its high complexity, is not feasible.. In this paper, we propose a randomized approximation convex hull algorithm which can be used for high dimensions in an acceptable execution eywords: onvex hull ata selection problem lassification egression eural networks upport vector machines time, and with low memory requirements.

    Article Title: Characterization of prostate cancer using diffusion tensor imaging: A new perspective.
    Article Snippet: Purpose: This study is aimed at evaluating the potential role of quantitative magnetic resonance diffusion tensor imaging (DTI) and tractography parameters in the detection and characterization of peripheral zone prostate cancer with a particular attention for fiber tract density.. Materials and Methods: DTI was acquired from eleven high risk, transrectal ultrasound (TRUS)-guided biopsy proven prostate cancers with perineural invasion (histological Gleason score ≥ 7) on a 3T magnet.. Twenty parameters derived from DTI were quantified in cancer and healthy regions of the prostate.

    Article Title: Computational Detection of piRNA in Human Using Support Vector Machine
    Article Snippet: In addition, for optimizing the one-class SVM model, the radial basis function (RBF) kernel parameter nu (γ) were adjusted by the grid search strategy in MATLAB. illustrates the pipeline for piRNA identification.



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    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
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    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
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    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
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    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