Working Set Selection Using Functional Gain for LS-SVM
Liefeng Bo, Licheng Jiao, Ling Wang · IEEE Transactions on Neural Networks · 2007
The efficiency of sequential minimal optimization (SMO) depends strongly on the working set selection. This letter shows how the improvement of SMO in each iteration, named the functional gain (FG), is used to select the working set for least squares support vector machine (LS-SVM). We prove the convergence of the proposed method and give some theoretical support for its performance. Empirical comparisons demonstrate that our method is superior to the maximum violating pair (MVP) working set selection.