A novel Robust Smooth Support Vector Machine

摘要：

In this paper, we propose a new type of e-insensitive loss function, called as e-insensitive Fair estimator. With this loss function we can obtain better robustness and sparseness. To enhance the learning speed ,we apply the smoothing techniques that have been used for solving the support vector machine for classification, to replace the Einsensitive Fair estimator by an accurate smooth approximation. This will allow us to solve e-SFSVR as an unconstrained minimization problem directly. Based on the simulation results, the proposed approach has fast learning speed and better generalization performance whether outliers exist or not.

关键词： support vector regression outliers robust estimators sparseness

作者: Shaochao Sun Dao Huang

作者单位: East China University of Science and Technology, Shanghai 200237, China

会议类型: 国际会议

会议名称: 2011 International Conference on Machanical Engineering,Materials and Energy(2011年机械工程、材料与能源国际会议 ICMEME 2011)

会议地点: 大连

会议语种:英文

页码: 1438-1441

在线出版日期: 2011-10-19（万方平台首次上网日期，不代表论文的发表时间）