一种基于支持向量机的并行训练策略

Parallel Training Strategy Based on Support Vector Machine

针对基于支持向量机的分类器训练时间过长问题,提出一种并行训练策略.该策略在并行程序设计上采用主从模式,将训练任务划分成若干个子任务,分配到多个从节点上计算,最后由主节点将各从节点上的训练结果收集,生成分类器模型.采用这种算法,使用了多组稀疏型和连续型的数据集,经过在自强3000高性能计算机上测试,实验结果表明该算法不仅能够保证多分类的高准确率,而且缩短了训练时间.

We propose a parallel training strategy, which is an improved parallel algorithm of support vector machine （SVM）, to shorten the training time based on SVM＇s classification. The strategy uses the mastersalve mode and divides the whole training task into several sub-tasks, each sub-task computed by a node. The master node collects the training results from slave nodes to produce the classifiable model. Performance of this algorithm is analyzed and evaluated with sparse and dense dataset on a high-performance computer ZQ3000 cluster. The results indicate that the proposed method can ensure high precision in the original multiclassification and reduce training time.