文献类型：会议论文

英文题名：A modified SMO algorithm for SVM regression and its application in quality prediction of HP-LDPE

作者：Zhao, HP; Yu, JS

机构：[1]E China Univ Sci & Technol, Res Inst Automat, Shanghai 200237, Peoples R China

会议论文集：1st International Conference on Natural Computation (ICNC 2005)

会议日期：AUG 27-29, 2005

会议地点：Changsha, PEOPLES R CHINA

语种：英文

摘要：A modified sequential minimal optimization (SMO) algorithm for support vector machine (SVM) regression is proposed based on Shevade's SMO-1 algorithm. The main improvement is that a modified heuristics method is used in this modified SMO algorithm to choose the first Lagrange multiplier when optimizing the Lagrange multipliers corresponding to the non-boundary examples. To illustrate the validity of the proposed modified SMO algorithm, a benchmark dataset and a practical application in predicting the melt index of high-pressure low-density polyethylene (HP-LDPE) are used; the results demonstrate that this modified SMO algorithm is faster in most cases with the same parameters setting and more likely to obtain the better generalization performance than Shevade's SMO-1 algorithm.