CloudSVM : Training an SVM Classifier in Cloud Computing Systems
This addresses the problem of costly and complex SVM training for large-scale machine learning applications, though it appears incremental as it adapts existing distributed methods to cloud environments.
The paper tackles the challenge of training SVM classifiers on large datasets by proposing CloudSVM, a method that uses MapReduce in cloud computing to distribute training across servers and iteratively merge support vectors, achieving convergence to an optimal classifier in finite iterations.
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.