Published on Wed May 01 2019

High-Performance Support Vector Machines and Its Applications

Taiping He, Tao Wang, Ralph Abbey, Joshua Griffin

The algorithm is named high-performance support vector machines (HPSVM) HPSVM provides a new way to distribute computations to machines in the cloud without shuffling the data.

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Abstract

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.