CloudSVM : Training an SVM Classifier in Cloud Computing Systems

TL;DR

This paper introduces CloudSVM, a distributed SVM training method in cloud environments using MapReduce, enabling efficient training on large datasets by iterative support vector merging until convergence.

Contribution

It proposes a novel CloudSVM approach that leverages cloud computing and MapReduce for scalable, distributed SVM training on large datasets, ensuring convergence to an optimal classifier.

Findings

Supports large-scale data training in cloud environments

Ensures convergence to a global optimal classifier

Demonstrates efficiency of distributed SVM training

Abstract

In conventional method, distributed support vector machines (SVM) algorithms are trained over pre-configured intranet/internet environments to find out an optimal classifier. These methods are very complicated and costly for large datasets. Hence, we propose a method that is referred as the Cloud SVM training mechanism (CloudSVM) in a cloud computing environment with MapReduce technique for distributed machine learning applications. Accordingly, (i) SVM algorithm is trained in distributed cloud storage servers that work concurrently; (ii) merge all support vectors in every trained cloud node; and (iii) iterate these two steps until the SVM converges to the optimal classifier function. Large scale data sets are not possible to train using SVM algorithm on a single computer. The results of this study are important for training of large scale data sets for machine learning applications. We provided that iterative training of splitted data set in cloud computing environment using SVM will converge to a global optimal classifier in finite iteration size.