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svm with certain kernels (e.g. polynomial and radial basis function, rbf)  (Drucker Diagnostics)

 
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    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/svm+radial+basis+function+kernel/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, 2026-10
    90/100 stars

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    Related Articles

    other:

    Article Title: Estimation of tunnel axial orientation in the interlayered rock mass using a comprehensive algorithm
    Article Snippet: In addition, SVM has been proven to have robust potential in the fields of classification, estimation, and pattern recognition (Drucker et al., 1997; Thang et al., 2018).

    Article Title: Alternating conditional expectation (ACE) algorithm and supervised classification models for rapid determination and classification of the adulterated cinnamon samples using diffuse reflectance FT-IR spectroscopy
    Article Snippet: Cinnamon authentication is an important subject in the field of spice adulteration studies due to its widespread applications in the food and pharmacy industries.. Rapid, non-destructive, and smart approaches lead us to the best results.. Cinnamomum verum (C. verum), as the high-priced species of cinnamon, is at risk of mixing with Cinnamomum cassia (C. cassia), black pepper, and clove.

    Article Title: Forecasting personal heat strain under extremely hot environments: Utilizing feature importance in machine learning
    Article Snippet: By introducing ε-insensitive loss function to the SVM’s regression model, the SVM model, which has been mainly utilized for classification, can be applicable to the domain of regression (Drucker et al., 1996).

    Article Title: Machine learning methods for predicting CO2 solubility in hydrocarbons
    Article Snippet: In the SVM algorithm the margin between the data points and the hyper plane is tried to be maximized (Drucker et al., 1996).

    Article Title: Advanced Machine Learning Techniques for Precise hourly Air Quality Index (AQI) Prediction in Azamgarh, India
    Article Snippet: This paper forecasts the hourly AQI in Azamgarh, Uttar Pradesh, India, using machine learning (ML) models.. This work involved the real-time measurement of hourly particulate matter (PM2.5, PM10), gaseous concentrations (NO2, SO2), and meteorological parameters (temperature, relative humidity, wind direction, wind speed, and uv radiation) in the ambient air.. We have calculated the pollutant subindex and hourly AQI for Azamgarh from July 2022 to June 2023, encompassing a total of 8760 data points.

    Article Title: A novel muscle network approach for objective assessment and profiling of bulbar involvement in ALS
    Article Snippet: SVM is a flexible ML algorithm that allows raw data to be mapped into linear or nonlinear space using different kernels (Drucker et al., ).

    Extraction:

    Article Title: Comparative Analysis of Machine Learning Algorithms for Email Spam Detection using TF-IDF
    Article Snippet: Patinavalasa Durga Prasad*, Pathri Deepthi Sri, Doddi Praveen Kumar, Sayyed Akbar Alisha, Vasireddi Saran Manikanta, Suneel Kimar Duvvuri Student, M.Sc (Computer Science), Government College (Autonomous), Rajahmundry, Adhra Pradesh, India.. Student, B.Sc (Artificial Intelligence), Government College (Autonomous), Rajahmundry, Adhra Pradesh, India.. Student, B.Sc (Artificial Intelligence), Government College (Autonomous), Rajahmundry, Adhra Pradesh, India.



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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