  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.
