High-Performance Support Vector Machines and Its Applications
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.
Cite
@article{arxiv.1905.00331,
title = {High-Performance Support Vector Machines and Its Applications},
author = {Taiping He and Tao Wang and Ralph Abbey and Joshua Griffin},
journal= {arXiv preprint arXiv:1905.00331},
year = {2019}
}
Comments
ICDATA 2018