一种大规模支持向量机的高效求解算法

现有大规模支持向量机求解算法需要大量的内存资源和训练时间,通常在大集群并行环境下才能实现。提出了一种大规模支持向量机(SVM)的高效求解算法,以在个人PC机求解大规模SVM。它包括3个步骤:首先对大规模样本进行子采样来降低数据规模；然后应用随机傅里叶映射显式地构造随机特征空间,使得可在该随机特征空间中应用线性SVM来一致逼近高斯核SVM；最后给出线性SVM在多核环境下的并行实现方法以进一步提高求解效率。标准数据集的对比实验验证了该求解算法的可行性与高效性。

Efficient Algorithm for Large-scale Support Vector Machine

The algorithm for solving large-scale support vector machine(SVM) needs large memory requirement and computation time.Therefore,large-scale SVMs are performed on computer clusters or supercomputers.An efficient algorithm for large-scale SVM was presented,which can be operated on a daily-life PC.First,the large-scale training examples were subsampled to reduce the data size.Then,the random Fourier mapping was explicitly applied to the subsample to generate the random feature space,making it possible to apply a linear SVM to uniformly approximate to the Gaussian kernel SVM.Finally,a parallelized linear SVM algorithm was implemented to speed up the training further.Experimental results on benchmark datasets demonstrate the feasibility and efficiency of the proposed algorithm.