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

Authors:
Hengping Zhao;Jinshou Yu
Affiliations:
Research Institution of Automation, East China University of Science and Technology, Shanghai, PRC;Research Institution of Automation, East China University of Science and Technology, Shanghai, PRC
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Year:
2005

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Abstract

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.