  Training structured prediction models is time-consuming. However, most
existing approaches only use a single machine, thus, the advantage of computing
power and the capacity for larger data sets of multiple machines have not been
exploited. In this work, we propose an efficient algorithm for distributedly
training structured support vector machines based on a distributed
block-coordinate descent method. Both theoretical and experimental results
indicate that our method is efficient.
