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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