一种基于支持向量机的并行训练策略

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.

Parallel Training Strategy Based on Support Vector Machine

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 master-salve 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 multi-classification and reduce training time.