  The support vector machine is a flexible optimization-based technique widely
used for classification problems. In practice, its training part becomes
computationally expensive on large-scale data sets because of such reasons as
the complexity and number of iterations in parameter fitting methods,
underlying optimization solvers, and nonlinearity of kernels. We introduce a
fast multilevel framework for solving support vector machine models that is
inspired by the algebraic multigrid. Significant improvement in the running has
been achieved without any loss in the quality. The proposed technique is highly
beneficial on imbalanced sets. We demonstrate computational results on publicly
available and industrial data sets.
