Working Set Selection Using Functional Gain for LS-SVM

Liefeng Bo, Licheng Jiao, Ling Wang
2007 IEEE Transactions on Neural Networks  
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
more » ... MVP) working set selection. Index Terms-Fast algorithm, least squares support vector machine (LS-SVM), sequential minimal optimization (SMO).
doi:10.1109/tnn.2007.899715 pmid:18220204 fatcat:mox7yhn2dra7vlf632xaoa7qba