High-Performance Support Vector Machines and Its Applications
This addresses the computational bottleneck of SVM for large-scale applications, though it appears incremental as it builds on existing SVM methods.
The paper tackles the problem of scaling support vector machines (SVM) for classification by proposing a distributed algorithm called HPSVM, which avoids data shuffling and minimizes inter-machine communications, achieving similar or better results compared to state-of-the-art SVM on public datasets.
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.