國立臺灣大學資訊工程學系Fan, Rong-EnRong-EnFanChen, Pai-HsuenPai-HsuenChenLin, Chih-JenChih-JenLin2006-09-272018-07-052006-09-272018-07-052005http://ntur.lib.ntu.edu.tw//handle/246246/20060927122855804710Working set selection is an important step in decomposition methods for training support vector machines (SVMs). This paper develops a new technique for working set selection in SMO-type decomposition methods. It uses second order information to achieve fast con- vergence. Theoretical properties such as linear convergence are established. Experiments demonstrate that the proposed method is faster than existing selection methods using first order information.application/pdf440676 bytesapplication/pdfzh-TWsupport vector machinesdecomposition methodssequential minimal optimizationworking set selectionjournal articlehttp://ntur.lib.ntu.edu.tw/bitstream/246246/20060927122855804710/1/quadworkset.pdf