Samples Selection Based on SVR for Prediction of Steel Mechanical Property

摘要：

Support Vector Regression is a new kind of machine learning algorithm based on the idea of structural risk minimization with good generalization performance, which is applied to build prediction model for steel mechanical property in this paper. Training SVR requires large memory and long CPU time when the data set is large. To alleviate the computational burden in SVR training, a new sample selection algorithm is proposed, which calculate the times for bootstrap samples locating outside the εtube and decide those samples with larger probability according to the times as selected samples for modeling. Simulation result and the performance of practical application in some steel factory show that the proposed algorithm reserve effective samples, and also improve the performance of the SVR modeling.

关键词： support vector regression ε tube sample selection mechanical property

作者: Wang Ling Fu DongMei Li Qing

作者单位: School of Automation & Electrical Engineering, University of Science and Technology Beijing, Beijing School of Automation & Electrical Engineering, University of Science and Technology Beijing, Beijing

会议类型: 国际会议

会议名称: 2012 International Conference on Intelligent System Design and Engineering Applications(2012年智能系统设计与工程应用国际会议 ISDEA 2012)

会议地点: 三亚

会议语种:英文

页码: 909-912

在线出版日期: 2012-01-06（万方平台首次上网日期，不代表论文的发表时间）