Sequential-minimal-optimization algorithm for solving Huber-suppor-vector-regression with non-positive semi-definite kernels
求解非半正定核Huber–支持向量回归机问题的序列最小最优化算法

Sequential-minimal-optimization(SMO) algorithm is effective in solving large-scale support-vectormachine(SVM) problems. However, the existing algorithms require the kernel functions to be positive definite(PD) or positive semi-definite(PSD), thus limiting their applications. Having considered their deficiencies, we propose a new algorithm for solving Huber-SVR problems with non-positive semi-definite(non-PSD) kernels. This algorithm provides desirable regression-accuracies while ensuring the convergence. Thus, it is of theoretical and practical significance.