{"id":182,"date":"2016-06-27T23:00:56","date_gmt":"2016-06-27T23:00:56","guid":{"rendered":"http:\/\/uscictdialdev.wpenginepowered.com\/?page_id=182"},"modified":"2016-07-14T21:47:09","modified_gmt":"2016-07-14T21:47:09","slug":"libsvm","status":"publish","type":"page","link":"https:\/\/dialport.ict.usc.edu\/index.php\/libsvm\/","title":{"rendered":"libsvm"},"content":{"rendered":"

Home Page<\/a><\/strong><\/p>\n

LIBSVM <\/b>is an integrated software for support vector classification, (C-SVC, nu-SVC<\/a>), regression (epsilon-SVR, nu-SVR<\/a>) and distribution estimation (one-class SVM<\/a>). It supports multi-class classification.<\/p>\n

Since version 2.8, it implements an SMO-type algorithm proposed in this paper:
\nR.-E. Fan, P.-H. Chen, and C.-J. Lin. Working set selection using second order information for training SVM<\/a>. Journal of Machine Learning Research 6, 1889-1918, 2005. You can also find a pseudo code there. (how to cite LIBSVM<\/a>)<\/p>\n

Our goal is to help users from other fields to easily use SVM as a tool. <\/span>LIBSVM <\/b>provides a simple interface where users can easily link it with their own programs. Main features of LIBSVM<\/b> include<\/p>\n