svms with the linear kernel Search Results


kernel svms  (Komet GmbH)
90
Komet GmbH kernel svms
Kernel Svms, supplied by Komet GmbH, 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/svms+with+the+linear+kernel/kernel+svms/pmc11423346-90-3-0
Average 90 stars, based on 1 article reviews
kernel svms - by Bioz Stars, 2026-09
90/100 stars
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svm kernel trick  (RStudio)
90
RStudio svm kernel trick
Svm Kernel Trick, 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/svms+with+the+linear+kernel/svm/pmc08529344-189-14-18
Average 90 stars, based on 1 article reviews
svm kernel trick - by Bioz Stars, 2026-09
90/100 stars
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svm  (Kaggle Inc)
86
Kaggle Inc svm
Svm, supplied by Kaggle Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/svms+with+the+linear+kernel/svm/pmc12501078-38-9-6
Average 86 stars, based on 1 article reviews
svm - by Bioz Stars, 2026-09
86/100 stars
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support vector machine  (MathWorks Inc)
96
MathWorks Inc support vector machine
Support Vector Machine, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/svms+with+the+linear+kernel/Statistics+and+Machine+Learning+Toolbox/pmc10853508-230-10-29
Average 96 stars, based on 1 article reviews
support vector machine - by Bioz Stars, 2026-09
96/100 stars
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tf-idf, ig  (CH Instruments)
90
CH Instruments tf-idf, ig
Tf Idf, Ig, supplied by CH Instruments, 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/svms+with+the+linear+kernel/tf+idf++ig/10__18280_slash_mmep__110209-217-20-43
Average 90 stars, based on 1 article reviews
tf-idf, ig - by Bioz Stars, 2026-09
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/product/svms+with+the+linear+kernel/rbf+kernel+based+svm/pmc08556802-234-16-13
Average 90 stars, based on 1 article reviews
rbf kernel based svm - by Bioz Stars, 2026-09
90/100 stars
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support vector machine (svm) dot kernel algorithm  (RapidMiner Inc)
90
RapidMiner Inc support vector machine (svm) dot kernel algorithm
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
Support Vector Machine (Svm) Dot Kernel Algorithm, supplied by RapidMiner 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/svms+with+the+linear+kernel/support+vector+machine++svm/pmc05749291-60-10-18
Average 90 stars, based on 1 article reviews
support vector machine (svm) dot kernel algorithm - by Bioz Stars, 2026-09
90/100 stars
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svm using cubic polynomial kernel with c1⁄4 23  (Verlag GmbH)
90
Verlag GmbH svm using cubic polynomial kernel with c1⁄4 23
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
Svm Using Cubic Polynomial Kernel With C1⁄4 23, supplied by Verlag GmbH, 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/svms+with+the+linear+kernel/svm+using+cubic+polynomial+kernel+with+c1%E2%81%844+23/10__1002_slash_maco__201407788-216-15-3
Average 90 stars, based on 1 article reviews
svm using cubic polynomial kernel with c1⁄4 23 - by Bioz Stars, 2026-09
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