In learning machines, the larger is the training dataset the better model can be obtained. Therefore, the training phase can be very demanding in terms of computational time in mono-processor computers. To overcome this difficulty, codes should be parallelized. This article describes two general purpose parallelization techniques of a classification system based on Support Vector Machines (SVM). Both of them have been applied to the recognition of the L-H confinement regime in JET. This has allowed reducing the training computation time from 70h to3m.