Online　multiple　kernel　classification(OMKC)　algorithm　is　a　promising　algorithm　in　machine　learning.　Because　of　low　error　rate　and　relatively　fast　training　time,　it　has　been　sucessfully　applied　to　many　real-world　problems.　However,　in　the　phase　of　learning　a　single　classifier　for　a　given　kernel,　the　OMKC　adopts　the　perceptron　algorithm,　which　significantly　limits　the　performance　of　the　algorithm.　In　this　paper,　we　adopts　the　double　updating　online　learning(DUOL)　algorithm　to　learn　the　single　classifier.　Comparing　to　the　perceptron　algorithm,　the　DUOL　algorithm　not　only　assigns　a　weight　to　the　misclassified　example,　but　also　updates　the　weight　for　one　of　the　existing　support　vectors,　which　significantly　improves　the　classification　performance.　Then　we　use　the　hedge　algorithm　to　combines　these　classifiers.　The　experimental　results　show　that　the　proposed　algorithm　is　more　effective　than　the　OMKC　algorithm,　the　state-of-the-art　algorithms,　and　single　kernel　learning　algorithm.　©　2014　IEEE.