Distributed Training of Structured SVM

TL;DR

This paper introduces a distributed algorithm for training structured SVMs that leverages multiple machines to improve efficiency, supported by theoretical analysis and experimental validation.

Contribution

It presents a novel distributed block-coordinate descent algorithm for structured SVM training, enabling scalable and efficient learning across multiple machines.

Findings

The method is theoretically efficient.

Experimental results confirm improved training speed.

Scalability with multiple machines is demonstrated.

Abstract

Training structured prediction models is time-consuming. However, most existing approaches only use a single machine, thus, the advantage of computing power and the capacity for larger data sets of multiple machines have not been exploited. In this work, we propose an efficient algorithm for distributedly training structured support vector machines based on a distributed block-coordinate descent method. Both theoretical and experimental results indicate that our method is efficient.