基于L1范数损失的非平行支持向量回归机  

Nonparallel support vector regression based on norm-L1 loss

作　　者：刘历铭 巩荣芬[1] 储茂祥[1] LIU Liming;GONG Rongfen;CHU Maoxiang(School of Electronic and Information Engineering,University of Science and Technology Liaoning,Anshan 114051,China)

机构地区：[1]辽宁科技大学电子信息与工程学院,辽宁鞍山114051

出　　处：《辽宁科技大学学报》2023年第2期101-110,共10页Journal of University of Science and Technology Liaoning

基　　金：辽宁省自然科学基金(2022-MS-353);辽宁省教育厅项目(2020LNZD06,LJKMZ20220640)。

摘　　要：针对NPSVR训练速度和预测精度问题,提出一种基于L1范数损失的非平行支持向量回归机L1NPSVR模型,用于预测数值输出。L1NPSVR通过求解两个较小规模的凸规划问题,建立一个ε_(1)-不敏感的下界函数和一个ε_(2)-不敏感的上界函数。在L1NPSVR模型中,每个优化问题同时最小化训练样本的L1范数损失和铰链损失,以保证模型的稳定性,减轻噪声和异常值的影响。L1NPSVR通过求解一对更小的优化问题来提高模型的运行效率。仿真结果验证了所提出方法的可行性及有效性。Aiming at the problem of training speed and prediction accuracy of NPSVR,a novel nonparallel support vector regression(L1NPSVR)based on norm-L1 loss is proposed for predicting numerical outputs.L1NPSVR constructs aε_(1)-insensitive lower bound function and aε_(2)-insensitive upper bound function by solving two smaller quadratic programming problems.In the L1NPSVR,each optimization problem simultaneously minimizes thenorm-L1 based losses and hinge losses,which can ensure the stability of the model and reduce the adverse of noise and outliers.L1NPSVR improves the efficiency of the model by solving a pair of smaller optimization problems.The simulation results demonstrate the feasibility and effectiveness of the proposed method.

关 键 词：模式识别 支持向量回归机 非平行支持向量回归机 L1范数损失 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP391[自动化与计算机技术—控制科学与工程]