关键词: 支持向量机, 图形处理器, 最少次方SVM

Abstract: A new parallel and incremental support vector machine （SVM） algorithm for the classification of very large datasets on graphics processing units （GPUs） is presented. 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. A recent least squares SVM （LS-SVM） proposed by Suykens and Vandewalle for building incremental and parallel algorithm is extended. The new algorithm uses graphics processors to gain high performance at low cost. Numerical test results on UCI and Delve dataset repositories show that this para-llel incremental algorithm using GPUs is about 130 times faster than its CPU implementation and often significantly faster （over 2 500 times） than state-of-the-art algorithms like LibSVM， SVM-perf and CB-SVM.

Key words: support vector machine （SVM）, general purpose graphics processing units （GP-GPU）, least squares SVM （LS-SVM）