Working Set Selection Using Functional Gain for LS-SVM

Authors:
Liefeng Bo;Licheng Jiao;Ling Wang
Affiliations:
Xi- dian Univ., Xi'an;-;-
Venue:
IEEE Transactions on Neural Networks
Year:
2007

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Abstract

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.