  The time complexity of support vector machines (SVMs) prohibits training on
huge data sets with millions of data points. Recently, multilevel approaches to
train SVMs have been developed to allow for time-efficient training on huge
data sets. While regular SVMs perform the entire training in one -- time
consuming -- optimization step, multilevel SVMs first build a hierarchy of
problems decreasing in size that resemble the original problem and then train
an SVM model for each hierarchy level, benefiting from the solved models of
previous levels. We present a faster multilevel support vector machine that
uses a label propagation algorithm to construct the problem hierarchy.
Extensive experiments indicate that our approach is up to orders of magnitude
faster than the previous fastest algorithm while having comparable
classification quality. For example, already one of our sequential solvers is
on average a factor 15 faster than the parallel ThunderSVM algorithm, while
having similar classification quality.
