The Support Vector Machine (SVM) is an efficient tool in machine learning with high
accuracy performance. However, in order to achieve the highest accuracy performance, n-
fold cross validation is commonly used to identify the best hyperparameters for SVM. This
becomes a weak point of SVM due to the extremely long training time for various
hyperparameters of different kernel functions. In this paper, a novel parallel SVM training
implementation is proposed to accelerate the cross validation procedure by running multiple …