CloudSVM: Training an SVM Classifier in Cloud Computing Systems

  • F. Ozgur Catak
  • M. Erdal Balaban
Conference paper

DOI: 10.1007/978-3-642-37015-1_6

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7719)
Cite this paper as:
Catak F.O., Balaban M.E. (2013) CloudSVM: Training an SVM Classifier in Cloud Computing Systems. In: Zu Q., Hu B., Elçi A. (eds) Pervasive Computing and the Networked World. ICPCA/SWS 2012. Lecture Notes in Computer Science, vol 7719. Springer, Berlin, Heidelberg


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.


Support Vector Machines Distributed Computing Cloud Computing MapReduce 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • F. Ozgur Catak
    • 1
  • M. Erdal Balaban
    • 2
  1. 1.National Research Institute of Electronics and Cryptology (UEKAE)TubitakTurkey
  2. 2.Quantitative MethodsIstanbul UniversityTurkey

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