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.
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