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Although Support Vector Machine (SVM) algorithm has a high generalizationproperty to classify for unseen examples after training phase and it has smallloss value, the algorithm is not suitable for real-life classification andregression problems. SVMs cannot solve hundreds of thousands examples intraining dataset. In previous studies on distributed machine learningalgorithms, SVM is trained over a costly and preconfigured computerenvironment. In this research, we present a MapReduce based distributedparallel SVM training algorithm for binary classification problems. This workshows how to distribute optimization problem over cloud computing systems withMapReduce technique. In the second step of this work, we used statisticallearning theory to find the predictive hypothesis that minimize our empiricalrisks from hypothesis spaces that created with reduce function of MapReduce.The results of this research are important for training of big datasets for SVMalgorithm based classification problems. We provided that iterative training ofsplit dataset with MapReduce technique; accuracy of the classifier functionwill converge to global optimal classifier function's accuracy in finiteiteration size. The algorithm performance was measured on samples from letterrecognition and pen-based recognition of handwritten digits dataset.

A MapReduce based distributed SVM algorithm for binary classification