High-Performance Support Vector Machines and Its Applications

TL;DR

This paper introduces HPSVM, a distributed SVM algorithm optimized for cloud computing that reduces data shuffling and inter-machine communication, achieving comparable or superior classification performance.

Contribution

HPSVM presents a novel distributed SVM method that minimizes communication overhead and eliminates data shuffling, enhancing scalability and efficiency in cloud environments.

Findings

HPSVM achieves comparable or better accuracy than traditional SVMs.

HPSVM reduces communication costs in distributed environments.

HPSVM demonstrates scalability on real-world datasets.

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.