  Support vector machines (SVM) can classify data sets along highly non-linear
decision boundaries because of the kernel-trick. This expressiveness comes at a
price: During test-time, the SVM classifier needs to compute the kernel
inner-product between a test sample and all support vectors. With large
training data sets, the time required for this computation can be substantial.
In this paper, we introduce a post-processing algorithm, which compresses the
learned SVM model by reducing and optimizing support vectors. We evaluate our
algorithm on several medium-scaled real-world data sets, demonstrating that it
maintains high test accuracy while reducing the test-time evaluation cost by
several orders of magnitude---in some cases from hours to seconds. It is fair
to say that most of the work in this paper was previously been invented by
Burges and Sch\"olkopf almost 20 years ago. For most of the time during which
we conducted this research, we were unaware of this prior work. However, in the
past two decades, computing power has increased drastically, and we can
therefore provide empirical insights that were not possible in their original
paper.
