  The support vector machines (SVM) algorithm is a popular classification
technique in data mining and machine learning. In this paper, we propose a
distributed SVM algorithm and demonstrate its use in a number of applications.
The algorithm is named high-performance support vector machines (HPSVM). The
major contribution of HPSVM is two-fold. First, HPSVM provides a new way to
distribute computations to the machines in the cloud without shuffling the
data. Second, HPSVM minimizes the inter-machine communications in order to
maximize the performance. We apply HPSVM to some real-world classification
problems and compare it with the state-of-the-art SVM technique implemented in
R on several public data sets. HPSVM achieves similar or better results.
