Table \[table2\], the radius-margin based SVM methods generally outperform SVM in terms of classification accuracy, which indicates that the incorporation of radius can improve the classification performance. As listed in Table \[table3\], the training time of SVM is much less than the other four methods, indicating that the introduction of radius makes the model more complex to train.

      Dataset       SVM       RMM      R-SVM$^{+}$   R-SVM$_{\mu}^{+}$     F-SVM
  --------------- ------- ----------- ------------- ------------------- -----------
   Breast cancer   71.40     70.19        71.54            71.14         **71.68**
     Diabetes      76.57     76.29        76.67            76.42         **77.00**
    Solar Flare    67.66     67.38        67.66            67.66         **67.69**
      German       75.58     75.99        76.01            75.87         **76.04**
       Heart       83.61     83.64        83.83            83.96         **84.02**
       Image       83.77     84.12      **84.39**          83.97           84.32
     Ringnorm      75.41     75.78        75.63            75.43         **77.05**
      Splice       84.54   **85.05**      84.67            84.74           84.81
      Thyroid      89.76     91.19      **91.23**          90.09           86.81
      Twonorm      96.92   **97.79**      97.41            97.39           97.08
     Waveform      86.95   **88.54**      88.51            86.88           86.76

  : Comparison of the average classification accuracy (%) of linear SVM, linear RMM [@shivaswamy2010maximum], linear R-SVM$^{+}$ [@do2013convex], linear R-SVM$_{\mu}^{+}$ [@do2013convex], and linear F-SVM.[]{data-label="table2"}

      Dataset                      SVM                                   RMM                               R-SVM$^{+}$                        R-SVM$_{\mu}^{+}$                           F-SVM
  --------------- ------------------------------------- ------------------------------------- ------------------------------------- ------------------------------------- -------------------------------------
   Breast cancer   $\!\!6.20\!\!\times\!\!10^{\!-\!3}$   $\!\!3.70\!\!\times\!\!10^{\!+\!1}$   $\!\!2.29\!\!\times\!\!10^{\!+\!1}$   $\!\!0.20\!\!\times\!\!10^{\!+\!0}$   $\!\!6.60\!\!\times\!\!10^{\!-\!3}$
     Diabetes      $\!\!1.12\!\!\times\!\!10^{\!-\!2}$   $\!\!2.63\!\!\times\!\!10^{\!+\!2}$   $\!\!2.80\!\!\times\!\!10^{\!+\!2}$   $\!\!1.20\!\!\times\!\!10^{\!+\!3}$   $\!\!1.38\!\!\times\!\!10^{\!-\!2}$
    Solar Flare    $\!\!7.79\!\!\times\!\!10^{\!-\!4}$   $\!\!1.38\!\!\times\!\!10^{\!+\!1}$   $\!\!3.41\!\!\times\!\!10^{\!+\!1}$   $\!\!4.61\!\!\times\!\!10^{\!+\!8}$   $\!\!6.60\!\!\times\!\!10^{\!-\!4}$
      German       $\!\!4.83\!\!\times\!\!10^{\!-\!2}$   $\!\!3.77\!\!\times\!\!10^{\!+\!2}$   $\!\!7.38\!\!\times\!\!10^{\!+\!2}$   $\!\!1.55\!\!\times\!\!10^{\!+\!2}$   $\!\!5.95\!\!\times\!\!10^{\!-\!2}$
       Heart       