Chapter

Pervasive Computing and the Networked World

Volume 7719 of the series Lecture Notes in Computer Science pp 57-68

CloudSVM: Training an SVM Classifier in Cloud Computing Systems

  • F. Ozgur CatakAffiliated withLancaster UniversityNational Research Institute of Electronics and Cryptology (UEKAE), Tubitak
  • , M. Erdal BalabanAffiliated withCarnegie Mellon UniversityQuantitative Methods, Istanbul University

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Abstract

In conventional distributed machine learning methods, 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. Single computer is incapable to train SVM algorithm with large scale data sets. 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.

Keywords

Support Vector Machines Distributed Computing Cloud Computing MapReduce