kernel-based svm Search Results


brain tumor segmentation and classification in mri using clustering and kernel-based svm  (Raipur Municipal)
90
Raipur Municipal brain tumor segmentation and classification in mri using clustering and kernel-based svm
Brain Tumor Segmentation And Classification In Mri Using Clustering And Kernel Based Svm, supplied by Raipur Municipal, 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/brain tumor segmentation and classification in mri using clustering and kernel-based svm/product/Raipur Municipal
Average 90 stars, based on 1 article reviews
brain tumor segmentation and classification in mri using clustering and kernel-based svm - by Bioz Stars, 2026-04
90/100 stars
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polynomial kernel based svm  (SoftMax Inc)
90
SoftMax Inc polynomial kernel based svm
Polynomial Kernel Based Svm, supplied by SoftMax 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/polynomial kernel based svm/product/SoftMax Inc
Average 90 stars, based on 1 article reviews
polynomial kernel based svm - by Bioz Stars, 2026-04
90/100 stars
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rbf kernel based svm  (SoftMax Inc)
90
SoftMax Inc rbf kernel based svm
The multistage learning approach and decision level fusion of individual classifiers. “Fusion 1” refers to the hard-level combination of the individual predictions obtained from <t>RBF</t> and Polynomial kernel based SVMs. “Fusion 2” refers to the hard-level combination of the individual predictions obtained from Softmax function and RBF kernel based <t>SVM.</t> “Fusion 3” refers to the hard-level combination of the individual predictions obtained from Softmax function and Polynomial kernel based SVM. “Fusion 4” refers to the hard-level combination of the individual predictions obtained from Softmax function, RBF and Polynomial kernel based SVMs
Rbf Kernel Based Svm, supplied by SoftMax 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/rbf kernel based svm/product/SoftMax Inc
Average 90 stars, based on 1 article reviews
rbf kernel based svm - by Bioz Stars, 2026-04
90/100 stars
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kernel-based svm  (IEEE Access)
90
IEEE Access kernel-based svm
The multistage learning approach and decision level fusion of individual classifiers. “Fusion 1” refers to the hard-level combination of the individual predictions obtained from <t>RBF</t> and Polynomial kernel based SVMs. “Fusion 2” refers to the hard-level combination of the individual predictions obtained from Softmax function and RBF kernel based <t>SVM.</t> “Fusion 3” refers to the hard-level combination of the individual predictions obtained from Softmax function and Polynomial kernel based SVM. “Fusion 4” refers to the hard-level combination of the individual predictions obtained from Softmax function, RBF and Polynomial kernel based SVMs
Kernel Based Svm, supplied by IEEE Access, 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/kernel-based svm/product/IEEE Access
Average 90 stars, based on 1 article reviews
kernel-based svm - by Bioz Stars, 2026-04
90/100 stars
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Image Search Results


The multistage learning approach and decision level fusion of individual classifiers. “Fusion 1” refers to the hard-level combination of the individual predictions obtained from RBF and Polynomial kernel based SVMs. “Fusion 2” refers to the hard-level combination of the individual predictions obtained from Softmax function and RBF kernel based SVM. “Fusion 3” refers to the hard-level combination of the individual predictions obtained from Softmax function and Polynomial kernel based SVM. “Fusion 4” refers to the hard-level combination of the individual predictions obtained from Softmax function, RBF and Polynomial kernel based SVMs

Journal: Applied Intelligence

Article Title: Decision and feature level fusion of deep features extracted from public COVID-19 data-sets

doi: 10.1007/s10489-021-02945-8

Figure Lengend Snippet: The multistage learning approach and decision level fusion of individual classifiers. “Fusion 1” refers to the hard-level combination of the individual predictions obtained from RBF and Polynomial kernel based SVMs. “Fusion 2” refers to the hard-level combination of the individual predictions obtained from Softmax function and RBF kernel based SVM. “Fusion 3” refers to the hard-level combination of the individual predictions obtained from Softmax function and Polynomial kernel based SVM. “Fusion 4” refers to the hard-level combination of the individual predictions obtained from Softmax function, RBF and Polynomial kernel based SVMs

Article Snippet: “Fusion 2” refers to the hard-level combination of the individual predictions obtained from Softmax function and RBF kernel based SVM.

Techniques:

The detailed presentation of accuracy values obtained from applied individual and ensemble learning scenarios for three data-sets (average accuracy values of 5-folds are given)

Journal: Applied Intelligence

Article Title: Decision and feature level fusion of deep features extracted from public COVID-19 data-sets

doi: 10.1007/s10489-021-02945-8

Figure Lengend Snippet: The detailed presentation of accuracy values obtained from applied individual and ensemble learning scenarios for three data-sets (average accuracy values of 5-folds are given)

Article Snippet: “Fusion 2” refers to the hard-level combination of the individual predictions obtained from Softmax function and RBF kernel based SVM.

Techniques: Standard Deviation, Plasmid Preparation