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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
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Average 90 stars, based on 1 article reviews
rbf kernel based svm - by Bioz Stars, 2026-06
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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