by two performance indicators: accuracy and training time (seconds, [*s*]{}).

      Dataset      \# of samples   \# of classes   \# of attributes
  --------------- --------------- --------------- ------------------
   Breast cancer        286              2                9
     Diabetes           768              2                8
    Solar Flare         144              3                9
      German           1000              2                20
       Heart            270              2                13
       Image           2310              7                19
     Ringnorm          7400              2                20
      Splice           3190              3                60
      Thyroid           215              3                5
      Twonorm          7400              2                20
     Waveform          5000              3                21

  : Summary of the UCI datasets used in the experiments.[]{data-label="table1"}

Evaluation on linear F-SVM using the UCI datasets
-------------------------------------------------

We evaluate the performance of linear F-SVM on the 11 datasets from the UCI machine learning repository, where the reason to choose them is that they had been widely adopted for evaluating SVM and kernel methods [@ratsch2001soft; @scholkopft1999fisher; @cawley2003efficient]. Table \[table1\] provides a brief summary of these UCI datasets, which includes 6 2-class problems and 5 multi-class problems. Tables \[table2\] and \[table3\] list the mean classification accuracy and training time of five linear classifiers, i.e., linear SVM, linear RMM [@shivaswamy2010maximum], linear R-SVM$^{+}$ [@do2013convex], linear R-SVM$_{\mu}^{+}$ [@do2013convex], and linear F-SVM. RMM [@shivaswamy2010maximum], R-SVM$^{+}$ [@do2013convex], R-SVM$_{\mu}^{+}$ [@do2013convex], and F-SVM consider both margin and radius information, while SVM only considers margin. As shown in 