svm-rbf kernel Search Results


rbf kernel svm algorithm architecture  (MathWorks Inc)
90
MathWorks Inc rbf kernel svm algorithm architecture
Comparison of Different FPGA <t> SVM </t> Implementation
Rbf Kernel Svm Algorithm Architecture, 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
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tf-idf, ig  (CH Instruments)
90
CH Instruments tf-idf, ig
Comparison of Different FPGA <t> SVM </t> Implementation
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
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svm kernels gaussian (radial basis function (rbf)  (MathWorks Inc)
90
MathWorks Inc svm kernels gaussian (radial basis function (rbf)
Comparison of Different FPGA <t> SVM </t> Implementation
Svm Kernels Gaussian (Radial Basis Function (Rbf), 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
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Average 90 stars, based on 1 article reviews
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matlab bioinformatics toolbox  (MathWorks Inc)
90
MathWorks Inc matlab bioinformatics toolbox
Comparison of Different FPGA <t> SVM </t> Implementation
Matlab Bioinformatics Toolbox, 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/result/matlab bioinformatics toolbox/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
matlab bioinformatics toolbox - by Bioz Stars, 2026-04
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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
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svm-rbf kernel  (MathWorks Inc)
90
MathWorks Inc svm-rbf kernel
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 Rbf Kernel, 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/result/svm-rbf kernel/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
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svml  (MathWorks Inc)
90
MathWorks Inc svml
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
Svml, 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/result/svml/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
svml - by Bioz Stars, 2026-04
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svm classifications with a radial basis function (rbf) kernel  (MathWorks Inc)
90
MathWorks Inc svm classifications with a radial basis function (rbf) kernel
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 Classifications With A Radial Basis Function (Rbf) Kernel, 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/result/svm classifications with a radial basis function (rbf) kernel/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
svm classifications with a radial basis function (rbf) kernel - by Bioz Stars, 2026-04
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Image Search Results


Comparison of Different FPGA SVM Implementation

Journal: Ieee Transactions on Very Large Scale Integration (Vlsi) Systems

Article Title: An Ultralow-Power Real-Time Machine Learning Based fNIRS Motion Artifacts Detection

doi: 10.1109/TVLSI.2024.3356161

Figure Lengend Snippet: Comparison of Different FPGA SVM Implementation

Article Snippet: The combined fNIRS data preprocessing and RBF kernel SVM algorithm architecture were designed and simulated within the Simulink environment.

Techniques: Comparison

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