Working Set Selection Using Second Order Information for Training Support Vector Machines Export

@article{working, abstract = {Working 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 convergence. Theoretical properties such as linear convergence are established. Experiments demonstrate that the proposed method is faster than existing selection methods using first order information.}, address = {Cambridge, MA, USA}, author = {Fan, Rong E. and Chen, Pai H. and Lin, Chih J.}, citeulike-article-id = {4070932}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1194907}, issn = {1533-7928}, journal = {J. Mach. Learn. Res.}, keywords = {svm}, pages = {1889--1918}, posted-at = {2009-02-19 11:54:01}, priority = {2}, publisher = {MIT Press}, title = {Working Set Selection Using Second Order Information for Training Support Vector Machines}, url = {http://portal.acm.org/citation.cfm?id=1194907}, volume = {6}, year = {2005} }

TY - JOUR ID - working L3 - citeulike-article-id:4070932 N2 - Working 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 convergence. Theoretical properties such as linear convergence are established. Experiments demonstrate that the proposed method is faster than existing selection methods using first order information. JF - J. Mach. Learn. Res. SN - 1533-7928 AD - Cambridge, MA, USA EP - 1918 TI - Working Set Selection Using Second Order Information for Training Support Vector Machines PB - MIT Press VL - 6 SP - 1889 KW - svm AU - Fan, Rong AU - Chen, Pai AU - Lin, Chih PY - 2005/// UR - http://portal.acm.org/citation.cfm?id=1194907 ER -