A novel speed-up SVM algorithm for massive classification tasks
Thanh‐Nghi Do, Van Hoa Nguyen · 2008
The new parallel incremental support vector machine (SVM) algorithm aims at classifying 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 the learning 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, parallel algorithm. The new algorithm uses graphics processors to gain high performance at low cost. Numerical test results on UCI, Delve dataset repositories showed that our parallel incremental algorithm using GPUs is about 65 times faster than a CPU implementation and often significantly over 1000 times faster than state-of-the-art algorithms LibSVM, SVM-perf and CB-SVM.