  We present a novel coreset construction algorithm for solving classification
tasks using Support Vector Machines (SVMs) in a computationally efficient
manner. A coreset is a weighted subset of the original data points that
provably approximates the original set. We show that coresets of size
polylogarithmic in $n$ and polynomial in $d$ exist for a set of $n$ input
points with $d$ features and present an $(\epsilon,\delta)$-FPRAS for
constructing coresets for scalable SVM training. Our method leverages the
insight that data points are often redundant and uses an importance sampling
scheme based on the sensitivity of each data point to construct coresets
efficiently. We evaluate the performance of our algorithm in accelerating SVM
training against real-world data sets and compare our algorithm to
state-of-the-art coreset approaches. Our empirical results show that our
approach outperforms a state-of-the-art coreset approach and uniform sampling
in enabling computational speedups while achieving low approximation error.
