GBSVM (Granular-ball Support Vector Machine) is an important attempt to use
the coarse granularity of a granular-ball as the input to construct a
classifier instead of a data point. It is the first classifier whose input
contains no points, i.e., xi​, in the history of machine learning. However,
on the one hand, its dual model is not derived, and the algorithm has not been
implemented and can not be applied. On the other hand, there are some errors in
its existing model. To address these problems, this paper has fixed the errors
of the original model of GBSVM, and derived its dual model. Furthermore, an
algorithm is designed using particle swarm optimization algorithm to solve the
dual model. The experimental results on the UCI benchmark datasets demonstrate
that GBSVM has good robustness and efficiency