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All experiments are done using LibSVM and the SimpleMKL toolbox in MatLab 2015b.
Since only default parameters were used, LibSVM and SPegasos, which could be considered an optimized SVM, were designated as outliers with high counts.
For QSVMs, default learning parameters are those defined in the libSVM library (this software is available at https://www.csie.ntu.edu.tw/~cjlin/libsvm/).
Secondly, seven out of eight groups are used to train the multiclass SVM classifier with LibSVM [22], and the remaining group is left for validation purposes.
In LIBSVM, gnuplot.exe is an executable tool, which is used to find the optimal parameters pairs C and Y, while the goal of the parameter optimization operation is to classify the unknown data.
The toolbox LibSVM [56] was used for standard SVM approaches, while the SeDuMi Matlab Toolbox [57] and the codes provided by Shao et al., the author of Twin-Bounded SVM [46] (publicly available in http://www.
The LIBSVM [42] toolkit was used to complete the contrast test in SVMs.
The performance of our proposed CHDNet incorporating LIBLINEAR SVM [30] was also compared with other classifiers, including LDA, KNN, CART, GBDT, RF, and LIBSVM using identical data and features.
In this study, we used the LibSVM package 3.18 (http://www .csie.ntu.edu.tw/~cjlin/libsvm/) to implement SVM.
In our implementation, multiclass SVM training and testing are performed using the LIBSVM library [56].