论文标题
基于Cascade SVM的平行SVM算法的研究
Research on Parallel SVM Algorithm Based on Cascade SVM
论文作者
论文摘要
Cascade SVM（CSVM）可以并行分组数据集和训练子集，从而大大减少了训练时间和记忆消耗。但是，与直接训练相比，使用此方法获得的模型精度有一些错误。为了减少错误，我们分析了分组训练中错误的原因，并在理想条件下概括分组而无需误差。提出了平衡的CASCADE SVM（BCSVM）算法，该算法平衡了分组后的样本比例，以确保子集中的样本比例与原始数据集相同。同时，它证明了BCSVM算法获得的模型的准确性高于CSVM。最后，使用两个常见数据集进行实验验证，结果表明，使用BCSVM算法获得的准确性误差从CSVM的1％降低到0.1％，这通过数量级降低。
Cascade SVM (CSVM) can group datasets and train subsets in parallel, which greatly reduces the training time and memory consumption. However, the model accuracy obtained by using this method has some errors compared with direct training. In order to reduce the error, we analyze the causes of error in grouping training, and summarize the grouping without error under ideal conditions. A Balanced Cascade SVM (BCSVM) algorithm is proposed, which balances the sample proportion in the subset after grouping to ensure that the sample proportion in the subset is the same as the original dataset. At the same time, it proves that the accuracy of the model obtained by BCSVM algorithm is higher than that of CSVM. Finally, two common datasets are used for experimental verification, and the results show that the accuracy error obtained by using BCSVM algorithm is reduced from 1% of CSVM to 0.1%, which is reduced by an order of magnitude.
