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support vector classification.
This method named support vector classification (SVC).
Figure 4 "Minimum LSD" vector classification scheme.
They used support vector classification to predict the damage location.
These tables list, respectively, techniques based on peak-picking and vector classification.
The results indicate the robustness of support vector classification for the experimental data with two output classes.
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This article introduces the "Support Vector Classification-Regression" machine for K-class classification purposes (K-SVCR), a new training algorithm with ternary outputs {−1,0,+1} based on Vapnik's Support Vector theory.
The second strategy consisted of a GMM presegmentation and a speech label refinement by means of i-vector classification via multilayer perceptron (MLP).
When including all oligonucleotide frequencies in the feature vectors, classification performance decreased, giving very similar results as when including only dinucleotide frequencies.
The goal of SVC is to calculate a maximal margin hyperplane separating the two classes; this hyperplane is fully specified by a subset of support vectors; classification was also performed using the Workbench WEKA (Hall et al., 2009).
Lastly, a data segmentation based support vector machine classification method is proposed, which eliminates the classification error caused by unreliable EEG data segments.
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