Speed Up SVM Algorithm for Massive Classification Tasks

We present a new parallel and incremental Support Vector Machine (SVM) algorithm for the classification of very large datasets on graphics processing units (GPUs). SVM and kernel related methods have shown to build accurate models but the learning task usually needs a quadratic program so that this task for large datasets requires large memory capacity and long time. We extend a recent Least Squares SVM (LS-SVM) proposed by Suykens and Vandewalle for building incremental and parallel algorithm. The new algorithm uses graphics processors to gain high performance at low cost. Numerical test results on UCI and Delve dataset repositories showed that our parallel incremental algorithm using GPUs is about 70 times faster than a CPU implementation and often significantly faster (over 1000 times) than state-of-the-art algorithms like LibSVM, SVM-perf and CB-SVM.